The Analytics Times

Top Analytics Services to Boost Your Business Insights 💡

SIFT Analytics Group offers a comprehensive suite of analytics services designed to help organizations harness their data effectively. Our key services include:

Data Discovery and Visualization: Assisting businesses in exploring and visualizing data to gain a comprehensive understanding of their operations.
Modern Data Governance and Stewardship: Developing strategies to ensure data is managed securely and effectively throughout its lifecycle.
Data Process Automation: Implementing intelligent automation to streamline workflows and reduce manual, repetitive tasks.
Data Democratization: Facilitating seamless data access across the organization, enabling all employees to work comfortably with data.

Predictive and Prescriptive Analytics
Automated Machine Learning (AutoML)
Natural Language and Image Processing
AI-Driven Dashboards and Reporting
Intelligent Anomaly Detection

SIFT Analytics – Data, Analytics, and AI Solutions That Drive Results

SIFT Analytics services help organizations process large volumes of data to extract valuable insights that can enhance decision-making and operational efficiency. By utilizing data analytics, companies gain a deeper understanding of customer preferences and behaviors, leading to more effective marketing strategies and improved customer engagement. Real-time data analysis enables swift adjustments to strategies based on immediate performance indicators.

Moreover, data analytics plays a crucial role in identifying inefficiencies within business processes, leading to improved productivity and cost savings. Anticipating risks through data analysis allows businesses to develop strategies to mitigate potential threats, ensuring a more secure and resilient operation.

Encouraging employees to utilize data in their daily tasks can further enhance customer satisfaction and operational efficiency.

SIFT Analytics Data Integration Services - Essential Data Integration Tools for Analytics

Data integration tools are indispensable for streamlining the process of gathering data from diverse sources. These tools connect software and ensure effective data flow, facilitating:

data ingestion
processing
transformation
storage

This seamless integration is crucial for comprehensive analytical processes and deriving reliable insights.

Three essential categories of data integration tools are ETL tools, data connectors, and data cleansing tools. Each plays a pivotal role in ensuring the accuracy, quality, and accessibility of data. Let’s delve deeper into these tools to understand their specific functions and benefits.

ETL Tools

ETL stands for Extract, Transform, Load, which is a common method of data integration. ETL processes encompass various activities such as data cleansing, sorting, and enrichment, which are essential for preparing data for analysis. ETL tools enable businesses to speed up data ingestion and analysis, especially with cloud-based warehouses. This leads to increased efficiency and reduced errors.

Documenting how applications are connected ensures transparency in data integration. This documentation helps in tracking data flow and maintaining the integrity of the data integration process.

Data Connectors

Data connectors play a crucial role in ensuring seamless communication between databases. They facilitate data movement and transformation, enabling businesses to integrate data from multiple sources into a unified system. This capability is vital for maintaining consistent data and supporting comprehensive analytics.

Middleware acts as a mediator to normalize data for the master pool. Middleware standardizes data formats, allowing effective combination and analysis of data from various sources.

Data Cleansing Tools

Data cleansing tools are essential for maintaining the accuracy and quality of datasets. They detect and rectify data issues, ensuring that the data used for analysis is reliable and consistent. This is crucial for deriving meaningful insights and making informed decisions.

An organized data management process is necessary to manage inconsistent data. Assigning one team or person to be responsible for data quality and management processes can help in maintaining data integrity and ensuring that data cleansing tasks are effectively carried out.

SIFT Analytics Advanced Analytics Services

Machine learning enables systems to improve their predictive capabilities by learning from vast amounts of data over time. Deep learning, a subset of machine learning, uses complex algorithms to uncover patterns in data. These advanced analytics capabilities allow businesses to process large datasets quickly, enabling dynamic and real-time insights for business planning.

Data lakes enable businesses to combine multiple data sources, leading to actionable insights that generate business value. Incorporating external factors like market events and weather into predictive models enables more accurate and informed decisions.

Building a Data Warehouse for Analytics

Data warehouses are essential for centralizing information and enhancing analytics capabilities within businesses. Engaging stakeholders early in the data warehousing development process significantly improves its alignment with business objectives. This ensures that the data warehouse meets the specific needs of the organization and supports its strategic goals.

Schema design should align with both the warehouse technology used and the specific business requirements for optimal performance. Adopting an iterative development approach can enhance a data warehouse’s adaptability and performance, allowing for continuous improvements and adjustments as business needs evolve.

Leveraging Data Lakes for Data

Cloud-based data lakes allow organizations to scale their infrastructure according to their specific data needs, paying only for the storage and compute they use. This scalability is crucial for handling large volumes of unstructured data and overcoming data silos, which is common in big data applications.

A significant advantage of cloud data lakes is their ability to quickly adapt to varying workloads, reducing the time required for data teams to manage the platform. The cloud’s architecture also enhances disaster recovery capabilities, allowing for swift provisioning of new nodes or clusters in case of failures.

Real-Time Data Integration

Real-time data integration facilitates immediate data processing and access to data from various sources. Timeliness of data is essential, as change data capture can help ensure that data does not lose significant value shortly after production, combining data and making prompt data handling a key consideration.

Ensuring Data Governance and Security

Implementing strong data governance is critical to ensuring the quality and reliability of data in a warehouse setup. Data governance includes implementing security protocols that protect sensitive data and ensure compliance with regulations like GDPR and CCPA.

Defining clear user roles is essential for managing data access and updates effectively within a data warehouse. Establishing clear roles and responsibilities within data governance helps enhance accountability and prevents data misuse.

Creating a Data-Driven Culture

Data literacy is vital for professionals across all levels to effectively utilize analytics for informed decision-making. To truly embrace data-driven practices, organizations must focus on integrating data insights into everyday operations and decisions. This requires investing in data technologies and hiring skilled analytical professionals.

Promoting transparency and accessibility of data within the organization helps in cultivating a data-oriented mindset among employees. Starting with a small-scale trial can help businesses evaluate their team’s skills and identify challenges in implementing data analytics.

Steps to Implementing Data Analytics Services

Creating a data-driven environment involves addressing obstacles that hinder data-driven decision-making. Engaging external specialists can enhance internal skills and support the implementation of data analytics services.

Choosing an analytics tool that fits the organization’s culture and existing systems is crucial for successful implementation. Ongoing monitoring and iterative adjustments to data analytics solutions are necessary to optimize their effectiveness after launch.

Summary

Data analytics services are indispensable in today’s data-driven business landscape. From boosting decision-making and operational efficiency to enhancing customer experiences and mitigating risks, the benefits are immense. By leveraging essential data integration tools, building robust data warehouses and lakes, and ensuring real-time data integration, businesses can unlock the full potential of their data. Embracing a data-driven culture and selecting the right service provider will pave the way for sustained growth and innovation. Take the leap and harness the power of data analytics to transform your business today.

Next Steps

For more information or enquiries about Advanced Analytics services, feel free to contact us below.

The Data Advantage:
Using Advanced Analytics to Drive Smarter Business

How can businesses make smarter, data-driven decisions? Advanced analytics provides the answer. By using complex techniques, it transforms raw data into actionable insights. In this article, we will explore what advanced analytics is, why it’s crucial, and how it can elevate your business strategy.

Key Takeaways

Investing in Advanced Analytics

In today’s digital age, the sheer volume of data generated by businesses is staggering. But data alone is not enough; it’s the insights derived from this data that drive smart decision-making. Advanced analytics tools and techniques enable companies to analyze data effectively, uncover hidden patterns, and predict future outcomes accurately. This capability helps identify and capitalize on market opportunities, enhancing decision-making and operational efficiency through traditional business intelligence, data analysis, data mining, and data science.

Organizations prioritizing advanced analytics techniques can significantly improve their competitive positioning. Leveraging these techniques, businesses can perform predictive modeling and prescriptive analytics to optimize operations and strategic planning. These techniques identify trends, understand customer behaviors, and generate insights for targeted marketing campaigns and other essential functions.

However, selecting which functions and use cases to prioritize for investment can be daunting. Organizations must evaluate their analytics maturity to devise an effective strategy and identify the optimal starting point. This evaluation clarifies current capabilities and gaps, ensuring analytics investments align with business goals and long-term vision.

SIFT Analytics Group's 2025 Vision: Empowering Businesses with Advanced Analytics

SIFT Analytics Group commits to leading advanced analytics, helping businesses unify data and leverage AI for actionable insights. Our 2025 vision focuses on several key areas, starting with data integration and cohesion and ensuring organizations can make confident, data-driven decisions with up-to-date, reliable information.

AI-driven insights and automation are another cornerstone of our vision. Advancements in machine learning and natural language processing allow SIFT Analytics Group to leverage AI for automating insights extraction from unstructured data. Our evolving AI-powered solutions enable businesses to process large volumes of documents, uncover hidden insights, and respond to market dynamics quickly.

Predictive analytics plays a crucial role in our strategy. Combining historical data with AI-driven algorithms, we help organizations forecast trends, optimize operations, and stay competitive. Our comprehensive solutions portfolio meets each organization’s unique needs, ensuring scalability and customization for their predictive models analytics journey.

Finally, our commitment to customer engagement strategies and innovative analytics tools ensures that businesses can make informed strategic decisions. SIFT Analytics Group believes in the transformative power of advanced analytics to turn data into a strategic asset, empowering businesses to thrive in the digital age.

How SIFT Analytics Group Helps Organizations Navigate the Analytics Journey

Navigating the analytics journey is complex, but SIFT Analytics Group guides organizations every step of the way. Our approach begins with a maturity assessment, evaluating your organization’s current analytics capabilities, data management, and decision-making processes. This assessment identifies areas for improvement, ensuring investments in analytics align with business goals and long-term vision.

After understanding your needs, we design tailored analytics solutions to meet your unique requirements. Whether data integration, AI-powered document processing, or predictive analytics, we customize our offerings, including enterprise software solutions, to maximize impact and value for your business. Our solutions are scalable and flexible, adapting to your organization’s evolving needs.

SIFT Analytics Group offers end-to-end support throughout the analytics journey. From strategy development and platform selection to deployment and ongoing optimization, we ensure the success of your analytics initiatives. Our comprehensive support ensures effective implementation and benefits from advanced analytics tools and techniques.

Conclusion: The Future of Advanced Analytics

As the world continues to generate more data, the need for advanced analytics solutions has never been greater. By 2025, SIFT Analytics Group envisions a future where businesses can seamlessly integrate their data, harness the power of AI to extract insights from documents, and leverage predictive analytics to stay ahead of the competition. Through our innovative services and customized solutions, we are committed to helping organizations navigate this complex landscape and unlock the full potential of their data.

The future of advanced analytics is bright, with new opportunities emerging daily. Businesses embracing these technologies will be well-positioned to thrive in the digital age.

SIFT Analytics Group supports this journey, providing tools and expertise to transform your data into a strategic asset. Ready to embrace the future of analytics and transform your business? SIFT Analytics Group is here to help. Together, we can build a smarter, more efficient, data-driven organization ready to thrive in the digital age.

Frequently Asked Questions

Why is investing in advanced analytics important for businesses?
Investing in advanced analytics is crucial for businesses because it boosts decision-making, enhances operational efficiency, and provides a competitive edge through data-driven insights. Embracing this technology can significantly transform how you operate and succeed.

What is SIFT Analytics Group’s vision for 2025?
SIFT Analytics Group envisions a future in 2025 where data integration and AI-driven insights empower businesses to make confident, data-driven decisions. It’s all about staying ahead of the trends, and we’re committed to making that happen!

How does SIFT Analytics Group support organizations in their analytics journey?
SIFT Analytics Group is here to back you up with everything from assessing your current analytics maturity to crafting custom solutions and optimizing your strategies. We aim to make sure your analytics journey leads to real success.

Next Steps

For more information or enquiries about Advanced Analytics services, feel free to contact us below.

SIFT Analytics Group Empowering Businesses with Data Integration and AI Document Processing

Whether it’s extracting insights from multiple data sources or documents, SIFT’s solutions provide a competitive edge in today’s AI data-driven world.

Key Takeaways

Organizations often encounter a difficult decision-making process when determining which functions and use cases to prioritize for advanced analytics investments. Evaluating the organization’s maturity in developing and deploying analytics can help identify the most effective strategy and the place to begin.
Data integration has emerged as a critical trend, gaining traction across industries. As organizations increasingly tap into multiple data sources, the challenge is how to unify them into a cohesive and actionable format. The goal is to create a "single source of truth
We are constantly surrounded by documents in various forms — reports, articles, legal papers, research studies, and more. The need for tools that can help us efficiently process, understand, and extract meaningful answers from documents is more critical than ever. This is where artificial intelligence (AI) comes in.

How SIFT Analytics Group Helps Organizations Navigate the Analytics Journey

After understanding your needs, we design tailored analytics solutions to meet your unique requirements. Whether data integration, data governance, or other predictive analytics, we customize our offerings, including enterprise software solutions, to maximize impact and value for your business. Our solutions are scalable and flexible, adapting to your organization’s evolving needs.

The Importance of Data Integration: Creating a Single Source of Truth

One of the foundational elements of a robust analytics strategy is data integration. A single source of truth is vital for businesses to make consistent, reliable, data-driven decisions. When data is scattered across multiple systems, it leads to silos that hinder collaboration and insights. Integrating data from various sources into a centralized repository ensures everyone is on the same page, enhancing accountability and improving team communication.

Data integration involves creating a cohesive and consistent view of information that stakeholders can trust. This process reduces errors and confusion from fragmented data, leading to more accurate and actionable insights. A unified view of data enables more effective analysis and reporting, crucial for informed decision-making.

Achieving a single source of truth requires advanced tools, robust platforms, and innovative strategies. The benefits, however, are well worth the effort. Businesses can break down silos, foster collaboration, and ensure that data-driven insights are consistent across the organization. This unified approach not only enhances decision-making but also builds trust in the data being used.

As we move forward, the role of AI in processing and extracting insights from documents will be explored. Leveraging AI further enhances data integration efforts, ensuring insights are accurate and actionable.

Leveraging AI to Process and Extract Insights from Documents

Artificial intelligence is revolutionizing the way organizations approach document processing. AI-powered solutions automate the extraction of key information from documents, such as customer insights, market trends, and legal requirements. This automation speeds up decision-making processes and ensures that the insights gleaned are accurate and actionable.

AI technologies, particularly natural language processing, enable more accurate comprehension and analysis of document contents. Businesses can analyze large volumes of documents efficiently, freeing up time and resources to focus on strategic initiatives.

How Document AI Helps Different Industries

Document AI is a game-changer when it comes to handling large volumes of documents and quickly pulling out valuable insights. It’s especially useful in industries where processing lots of paperwork is part of the job. Let’s take a look at a few industries where Document AI is making a real difference:

Legal Documents

Lawyers have to sift through a ton of documents—laws, regulations, contracts, case files—you name it. Document AI helps legal teams cut through the clutter by automatically sorting and digitizing all that information, so they can find what they need faster. This makes case prep, contract review, and legal research a whole lot easier and more efficient.

Insurance Agencies
When insurance companies bring on new clients, there’s a mountain of paperwork to go through. Document AI steps in to automate all those routine tasks, helping insurers quickly analyze all the data they need to assess risk and understand a client’s needs. This means better, quicker decision-making and more personalized service for clients.
Banking and Finance

In commercial banking, reviewing tons of paperwork is crucial to understand the financial and legal risks involved in things like loan approvals. Document AI makes this process a lot smoother by processing and analyzing financial documents in no time. This helps banks onboard clients faster and make more informed decisions about loans, all while cutting down on manual work.

As you look towards the future, consider how these solutions can transform your business. With SIFT Analytics Group as your partner, you can navigate the complexities of the analytics journey and build a AI data-driven organization ready to thrive in the digital age

Next Steps

For more information or enquiries about retail analytics services, feel free to contact us below.

Top Data Analytics Trends for 2025

Looking to understand the top data analytics trends for 2025 and how SIFT Analytics Services can help you? This article covers the latest trends and how SIFT Analytics transforms them into actionable insights.

Key Takeaways

Emerging trends in data analytics for 2025 include agentic AI, predictive/prescriptive analytics, edge analytics, explainable AI, and data fabric integration, all aimed at improving business efficiency.
Cloud-based solutions and data democratization are making analytics more accessible to non-technical users, allowing for better decision-making and operational scalability.
The future of data analytics is being shaped by technologies like quantum computing and 5G, which promise faster data processing and more advanced analytics capabilities.

An infographic depicting emerging data analytics trends for 2025.

Emerging Data Analytics Trends in 2025

The world of data analytics is on the brink of a revolution, with several emerging trends set to redefine how businesses operate and compete. One of the most significant developments is the rise of agentic AI, which performs independent tasks and is expected to be a game-changer in 2025. Additionally, the shift towards cloud-based platforms and a heightened focus on data ethics and governance are reshaping traditional analytics practices. With the estimated amount of worldwide data projected to reach 175 zettabytes by 2025, businesses must adopt advanced analytics tools to handle the volume, variety, and speed of data. Additionally, data exploration tools like Apache Superset and Looker Studio are becoming essential for businesses to effectively analyze and interpret their data, enhancing organizational insights and performance.

Four key trends set to dominate the data analytics landscape in 2025 include predictive and prescriptive analytics, edge analytics for real-time insights, explainable AI, and data fabric integration. These trends offer businesses unique opportunities to gain actionable insights, enhance operational efficiency, and maintain a competitive edge.

Exploring these trends in detail reveals their implications for the future of data analytics.

Predictive and Prescriptive Analytics

Predictive analytics has transformed how businesses anticipate future trends and behaviors, significantly enhancing decision-making processes. By analyzing historical sales data and customer patterns, companies can forecast future trends and make data-driven decisions. This technique is especially valuable in dynamic environments, offering a competitive edge by enabling businesses to anticipate changes early and adjust their strategies. Retailers, for example, can optimize pricing, improve customer engagement, and enhance performance by using predictive analytics to identify trends and project future sales volumes.

Complementing predictive analytics is prescriptive analytics, which not only forecasts future outcomes but also recommends actionable steps to optimize decision-making. Prescriptive analytics enhances operational efficiency and drives growth by analyzing data and generating actionable recommendations.

For example, retailers can use prescriptive analytics to optimize inventory management, ensuring that stock levels align with demand and minimizing the risk of overstock or stockouts. Together, predictive and prescriptive analytics provide a powerful combination for businesses looking to gain actionable insights and stay ahead of the competition.

Edge Analytics for Real-Time Insights

Edge analytics is emerging as a critical trend in 2025, enabling businesses to gain instantaneous insights by analyzing data directly at its source. This approach is particularly valuable in IoT applications and decentralized environments, where immediate insights and actions can significantly enhance operational efficiency. Processing data at the edge reduces latency, enabling real-time decisions crucial for applications like autonomous vehicles and emergency response systems.

One of the key benefits of edge analytics is its ability to minimize the need for data to be sent to central servers, thereby conserving bandwidth. In manufacturing, edge analytics enables real-time monitoring of equipment performance, anomaly detection, and instant corrective measures.

By 2025, 75% of enterprise data is projected to be processed at the edge, underscoring the growing importance of this trend.

Explainable AI (XAI)

Explainable AI (XAI) is gaining prominence as organizations prioritize transparency in AI systems to enhance trustworthiness and foster user confidence. XAI aims to provide clarity and understanding in AI decision-making processes, making it easier for users to trust and rely on AI-generated insights.

As businesses increasingly adopt AI-driven analytics tools, ensuring that these systems’ decisions are transparent and explainable is becoming particularly important.

Data Fabric Integration

Data fabric integration is set to revolutionize the way businesses handle and analyze data. By facilitating the integration of disparate data sources, data fabric enhances operational efficiency and accelerates innovation. This architecture allows businesses to create a comprehensive view of their operations, enabling more effective data analytics and decision-making. With data fabric, organizations can seamlessly integrate various data types, including structured, semi-structured, and unstructured data, into a cohesive system.

Data fabric integration offers more than operational efficiency. It provides a unified view of data, enabling businesses to gain deeper insights, identify trends, and make decisions that drive growth and innovation. This trend is particularly relevant in today’s data-rich environment, where organizations must manage and analyze vast amounts of data from multiple sources to stay competitive.

A visual representation of AI and machine learning applications in data analytics.

The Role of AI and Machine Learning in Data Analytics

Artificial intelligence (AI) and machine learning (ML) are at the forefront of the data analytics revolution, playing a crucial role in processing large datasets and driving data-driven decision-making. The integration of AI and ML into data analytics offers numerous benefits, including automating complex processes, enhancing the speed and accuracy of analysis, and providing quick insights from large datasets. As businesses continue to generate massive amounts of data, the need for AI and ML capabilities becomes increasingly critical.

AI and machine learning are transforming data analytics by enhancing traditional methods and paving the way for more sophisticated solutions. This section focuses on advanced AI models and machine learning capabilities. AI and ML enable businesses to gain actionable insights, automate data processing, and make more informed decisions.

Advanced AI Models

Advanced AI models are revolutionizing the field of predictive analytics by leveraging massive datasets to make accurate predictions and identify patterns. Techniques like time series analysis play a crucial role in predicting future trends by examining past data patterns and understanding recurring events. These models enable businesses to forecast future trends and make data-driven decisions that enhance their competitiveness.

Predictive modeling, a key component of advanced AI models, is utilized to analyze customer behavior and create detailed segments that enhance targeted strategies. Integrating AI algorithms into visualization tools helps businesses automatically uncover patterns within large datasets, making data analytics more comprehensible and effective.

These advancements are revolutionizing data analysis and decision-making, offering a significant edge in today’s data-driven world.

Machine Learning Capabilities

Machine learning capabilities are enhancing customer satisfaction by enabling personalized marketing strategies and deeper insights into customer data, preferences, and behaviors. AI-driven CRM analytics deliver valuable insights into customer interactions, allowing businesses to tailor their marketing efforts and improve customer engagement. By leveraging predictive modeling, businesses can better understand customer behavior and develop strategies that enhance customer satisfaction and loyalty.

Future trends in machine learning involve AI-driven personalized visualizations tailored to user preferences and past data interactions. These advancements will enable businesses to gain deeper insights into market trends and customer behavior, driving more effective data analytics and decision-making.

Investing in machine learning capabilities allows organizations to stay ahead in the competitive data analytics landscape and achieve their business goals.

A diagram illustrating cloud-based solutions and data democratization.

Cloud-Based Solutions and Data Democratization

Cloud-based solutions and the democratization of data analytics are transforming how businesses access and utilize data. Data democratization allows all users, regardless of technical expertise, to access and analyze data, promoting a culture of informed decision-making across organizations.

With the rise of augmented analytics, AI and machine learning are simplifying data preparation and insight generation for users without deep technical skills. This trend is empowering non-technical users to extract actionable insights, bridging the gap between technical and business teams.

This section explores the benefits of cloud-based solutions and data democratization, emphasizing scalability, flexibility, and empowering non-technical users. These advancements are not only making data analytics more accessible but also enabling businesses to scale their operations and make data-driven decisions more effectively.

Exploring these trends in detail reveals their implications for the future of data analytics.

Scalability and Flexibility

Cloud computing offers unparalleled scalability and flexibility for storing and analyzing large datasets. Cloud-based CRM platforms provide secure access from any location, automatic updates, and the ability to scale resources dynamically to meet changing demands. This scalability is a significant advantage, allowing businesses to handle larger datasets and integrate with existing infrastructure smoothly. Solutions like Apache Superset and Qlik Sense offer cloud-native architectures that support effective scaling and flexible deployment options, whether as SaaS or on-premises.

Cloud-based solutions allow organizations to start small and scale resources as needed, overcoming challenges in big data analytics. This dynamic scaling capability is essential for managing the ever-increasing volume of data and ensuring efficient data processing and analysis.

As businesses generate and analyze more data, the flexibility and scalability of cloud computing will be crucial for maintaining operational efficiency and staying competitive.

Empowering Non-Technical Users

The democratization of analytics is empowering non-technical users to analyze data and make informed decisions without needing specialized skills. Self-service analytics platforms, such as those provided by Sift Analytics, allow users to extract actionable insights and bridge the gap between technical and business teams. These platforms make data analytics more accessible, promoting a culture of data-driven decision-making across organizations.

Data literacy initiatives are also playing a crucial role in empowering non-technical users. By helping all employees understand and utilize data effectively, organizations can improve data quality, decision-making, and drive better business outcomes.

With the adoption of cloud-based solutions and self-service analytics platforms, empowering non-technical users becomes increasingly important for operational efficiency and enhancing customer engagement.

An example of an interactive dashboard for data visualization..

Enhanced Data Visualization and Interpretation Tools

Data visualization tools and data analytics tool are essential for deriving actionable insights and interpreting complex datasets. These tools help users understand data better, enabling informed decisions and quicker responses to business needs. Data exploration tools, such as Apache Superset and Looker Studio, are also gaining traction, providing businesses with powerful capabilities to explore and interpret their data more effectively.

Recent advancements in visualization tools include:

Automated insights
Integration with AI for predictive analytics
Customizable dashboards
Interactive visualization

These features simplify data analysis and enhance decision-making, making it easier for businesses to stay competitive in today’s data-driven world. This section explores two key advancements in data visualization: interactive dashboards and AI-driven visualization platforms. These tools are transforming how businesses visualize and interpret data, providing deeper insights and enhancing user experience.

Interactive Dashboards

Interactive dashboards are revolutionizing the way businesses explore and visualize data. These dashboards allow users to adjust parameters, drill down into metrics, and explore scenarios in real-time, providing a dynamic and engaging way to analyze data. Advanced dashboards offer features like sliders and filters, enabling users to manipulate interactive elements and gain deeper insights into their data.

Tools like Tableau and D3.js are at the forefront of this trend, offering customizable graphs, data-driven transformations, and tailored visualizations for data representation. Qlik Sense empowers users with self-service capabilities, including associative analytics and smart search features, making it easier for non-technical users to interact with and understand their data.

Interactive dashboards enable businesses to make informed decisions and respond quickly to changing market conditions.

AI-Driven Visualization Platforms

AI-driven visualization platforms are enhancing the data analytics landscape by providing deeper insights and improving user experience. Coupled with AI capabilities powered by Salesforce Einstein, Tableau enhances analytics processes and enables better decision-making. Power BI features deep integration with Microsoft products, offering real-time analytics and personalized marketing strategies through AI.

These AI-driven platforms are transforming data visualization by uncovering patterns within large datasets and providing automated insights. By integrating AI with visualization tools, businesses can gain more comprehensive and actionable insights, making it easier to understand complex data and make data-driven decisions.

As AI evolves, these platforms will play a more critical role in the data analytics landscape.

Data Analytics Tools

Data analytics tools are software applications that enable organizations to analyze and interpret data. These tools provide a range of features and functionalities, including data visualization, data mining, predictive analytics, and data science. By leveraging these tools, businesses can transform raw data into actionable insights, helping them make informed decisions and optimize their operations. The right data analytics tools can significantly enhance an organization’s ability to process data, identify trends, and gain valuable insights.

Overview of Data Analytics Tools

There are many different types of data analytics tools available, each with its own strengths. Some popular data analytics tools include:

Qlik and Talend: provide a seamless end-to-end solution that empowers organizations to manage, transform, and visualize data for actionable insights. Talend excels at efficiently integrating and cleaning data from diverse sources, while Qlik’s powerful analytics platform enables users to explore and visualize that data intuitively. Together, they simplify the entire data pipeline—ensuring high-quality, real-time data is available for analysis, driving better decision-making across businesses of all sizes.
Alteryx: lies in its ability to enable both technical and non-technical users to quickly prepare, blend, and analyze data with ease, without needing advanced coding skills. Its powerful automation capabilities streamline repetitive tasks like data cleansing and transformation, allowing teams to focus on higher-value work.
Tableau: A data visualization tool that enables users to create interactive dashboards and reports. Tableau’s intuitive interface allows users to easily explore and analyze data, making it a popular choice for businesses looking to enhance their data visualization capabilities.
Power BI: A business analytics service by Microsoft that allows users to create interactive visualizations and business intelligence reports. Power BI integrates seamlessly with other Microsoft products, providing a comprehensive solution for data analysis and reporting.

These tools offer a variety of features that cater to different data analytics needs, helping organizations analyze data, visualize insights, and make data-driven decisions.

Summary

The landscape of data analytics is evolving rapidly, with emerging trends and technologies set to transform how businesses operate and compete. Predictive and prescriptive analytics, edge analytics, explainable AI, and data fabric integration are among the key trends shaping the future of data analytics. These advancements offer unique opportunities for businesses to gain actionable insights, improve operational efficiency, and stay ahead of the competition. The integration of AI and machine learning, coupled with cloud-based solutions and enhanced data visualization tools, is further driving the evolution of data analytics.

As we look to the future, technologies like quantum computing and 5G are poised to revolutionize data processing and analysis, providing faster and more accurate insights. By leveraging these emerging trends and technologies, businesses can transform their data into actionable insights, driving growth and innovation. The journey of data analytics is just beginning, and the possibilities are limitless. Embrace these trends, invest in advanced analytics tools, and stay ahead in the competitive landscape of the digital age.

Read the next article on SIFT Analytics services to meet your business needs in 2025.

Frequently Asked Questions

What is the projected size of the global data analytics market by 2025?

The global data analytics market is expected to surpass $140 billion by 2025. That’s a huge opportunity for businesses looking to leverage data!

What is one major trend expected in data analytics by 2025?

By 2025, you can expect a significant shift towards predictive and prescriptive analytics driven by advanced AI models, making data insights more proactive and actionable. This trend will likely enhance decision-making across various industries.

How does data fabric benefit organizations?

Data fabric boosts operational efficiency and fosters innovation by seamlessly connecting various data sources, making it easier for organizations to access and utilize their data effectively.

Why is Explainable AI (XAI) gaining prominence?

Explainable AI (XAI) is becoming more important because companies are focusing on making AI systems transparent, which helps build trust and confidence among users. This focus on clarity is crucial for responsible AI adoption.

What impact does the democratization of analytics have on organizations?

Democratizing analytics enables everyone in an organization, not just tech experts, to gain valuable insights, fostering better collaboration between technical and business teams. This inclusivity significantly enhances decision-making and boosts overall efficiency.

Next Steps

For more information or enquiries about retail analytics services, feel free to contact us below.

Mastering Analytics for Retail: Your Comprehensive Guide

How can analytics transform your retail business? Analytics for retail delivers insights into customer behavior, inventory management, and sales optimization. This guide explores its importance, key types, and practical applications to help you drive growth and stay competitive.

Key Takeaways

Retail analytics serves as a vital tool for modern retailers, enhancing decision-making and improving customer satisfaction through systematic data analysis.
The four key types of retail analytics—descriptive, diagnostic, predictive, and prescriptive—each play a crucial role in optimizing business performance and guiding strategic decisions
Integrating multiple data sources and employing advanced analytics solutions are essential best practices that help retailers gain comprehensive insights into operations and improve overall performance.

A visual representation of retail analytics showcasing its importance in understanding customer behavior.

The Importance of Retail Analytics

Retail analytics is the cornerstone of modern retail businesses, providing actionable insights that can significantly enhance customer satisfaction and streamline decision-making processes. Systematic data analysis in retail analytics boosts revenue, cuts overhead costs, and optimizes profit margins. Imagine being able to refine item orders, pricing strategies, and marketing efforts based on solid data rather than guesswork; this is the competitive edge that retail analytics offers.

Savvy retail executives find that retail analytics drives operational efficiency. It streamlines inventory management, prevents overstock and stockouts, and enhances customer loyalty through personalized strategies. Successful retailers leverage customer analytics to synthesize data from various sources, creating a holistic view of their operations. This data-driven approach not only improves profit margins but also fosters a competitive advantage in a crowded market.

Retail analytics involves a comprehensive analysis of sales data, customer transactions, and market trends, enabling retailers to make informed decisions that drive growth and efficiency. Understanding customer shopping patterns and correlating in-store with web analytics enables retailers to enhance customer engagement and optimize business strategies. Ultimately, retail analytics helps retailers synthesize complex data, leading to more effective decision-making and improved overall performance.

Key Types of Retail Analytics

Retail analytics includes four key categories:

Descriptive analytics, which helps retailers understand past performance and trends.
Diagnostic analytics, which uncovers the reasons behind business outcomes.
Predictive analytics, which uses historical data to forecast future trends and demand.
Prescriptive analytics, which recommends specific actions to optimize pricing, improve engagement, and enhance business performance.

Each type of analytics plays a crucial role in enhancing business insights and enabling informed decision-making.

Understanding these key types of retail analytics is essential for retail organizations looking to stay competitive and drive growth. Advanced analytics solutions and business intelligence tools provide retailers with valuable insights into operations, customer behaviors, and market trends.

This comprehensive approach to data analytics empowers retailers to make informed decisions that enhance overall business performance and customer satisfaction.

Descriptive Analytics

Descriptive analytics focuses on understanding past performance and current trends, providing essential insights for retailers. The primary purpose of descriptive analytics is to organize data in a way that tells a compelling story about past and present performance. This type of analytics involves analyzing various types of data, including sales data, social media interactions, weather patterns, and shopping behavior, to gain insights into retail operations.

Business Intelligence tools serve as a key representation of descriptive analytics and analytic tools, facilitating data analysis and reporting. Before the advent of these tools, retailers traditionally relied on manual data gathering and reporting in Excel, which was time-consuming and prone to errors.

Today, descriptive analytics tools enable retailers to visualize data more effectively, helping them make informed decisions based on historical sales data and other critical metrics.

Diagnostic Analytics

Diagnostic analytics aims to identify and analyze performance issues in retail, helping businesses understand the underlying factors behind outcomes. Combining customer feedback, financial performance, and operational metrics allows diagnostic analytics to offer a comprehensive business performance analysis. This type of analytics helps retailers identify issues hindering performance, enabling targeted improvements and strategic adjustments.

Machine learning plays a critical role in diagnostic analytics by managing the complexity and volume of data, enhancing the identification of actionable insights. Advanced data analytics techniques help retailers uncover root causes of performance issues, leading to effective problem-solving and decision-making. Ultimately, diagnostic analytics helps retailers optimize their operations and improve overall business performance.

Predictive Analytics

Predictive analytics identifies new trends early and forecasts future results, aiding retailers in decision-making. Analyzing historical sales data and customer purchase histories allows predictive analytics to help retailers understand market dynamics and predict future trends. This type of analytics is particularly valuable for demand forecasting, which uses a wider range of data to accurately calculate product demand and manage inventories effectively.

Retailers rely on predictive analytics for strategic planning and anticipating future market trends. Predictive analytics enables retailers to accurately forecast sales, manage inventories using past data and external factors, and stay competitive in changing market conditions. However, several factors complicate retail analytics forecasting, including demand variability, price sensitivity, and evolving consumer behavior.

Accurate predictive analytics requires understanding the causes behind past events to make reliable forecasts. By integrating predictive analytics into retail operations, businesses can enhance their decision-making processes and stay ahead of market trends. This comprehensive approach to data analytics helps retailers optimize their operations, improve customer satisfaction, and drive growth.

Prescriptive Analytics

Prescriptive analytics recommends actionable steps based on predicted outcomes, using AI to enhance decision-making processes. Prescriptive analytics transforms predictive findings into actionable recommendations, offering specific steps to optimize pricing, improve customer engagement, and enhance business performance. This type of analytics helps retailers set optimal prices by analyzing various factors, including competitiveness, thereby enhancing dynamic pricing strategies.

The integration of AI in prescriptive analytics allows retailers to make more informed decisions and optimize their operations effectively. Advanced data analytics solutions enhance retailers’ decision-making processes, improve customer satisfaction, and drive growth.

Ultimately, prescriptive analytics empowers retailers to take proactive measures that lead to better business outcomes.

An overview of key types of retail analytics categorized visually.

Applications of Retail Data Analytics

Retail data analytics has a wide range of applications that can significantly improve customer experience and optimize retail operations.

Customer data helps retailers understand preferences and capture demand more effectively.

Leading retailers utilize a blend of:

loyalty program data
e-commerce data
POS data
broker data

to gain a comprehensive understanding of their customers.

This holistic approach enables retailers to make data-driven decisions that enhance customer satisfaction and drive growth.

Retail analytics involves different data types. These include:

Customer purchase histories
Call center logs
E-commerce navigation patterns
Point-of-sale systems
In-store video footage
Customer demographics

Analyzing this diverse data range provides retailers with valuable insights into operations and customer behaviors. This comprehensive approach helps retailers optimize their inventory management, improve marketing strategies, and analyze data to enhance overall business performance.

Inventory Management

Retail analytics plays a crucial role in inventory management by discerning demand trends, preventing overstock, and mitigating stockouts. Real-time data enables retailers to modify prices based on demand and market conditions, ensuring sufficient stock to support merchandising layout. AI-driven inventory management systems help retailers maintain optimal stock levels, reducing costs associated with overstock and stockouts.

Dynamic pricing strategies powered by AI allow retailers to adjust prices in real-time based on market conditions. Real-time inventory management systems developed by tech providers enable retailers to monitor stock levels and forecast demand accurately. This comprehensive approach to inventory management helps retailers optimize their supply chain, improve customer satisfaction, and drive growth.

Sales Forecasting

Sales forecasting in retail utilizes predictive analytics to estimate future sales based on historical data. By analyzing past sales data and market trends, retailers can plan for busy periods, improve marketing campaigns, and manage stock effectively. Retailers commonly use a combination of Excel sheets, ERP features, and specialized software for sales forecasting, which helps them make informed decisions and optimize their operations.

The sales forecasting process involves analyzing historical sales data to identify trends and project future sales volumes. Advanced data analytics solutions enhance retailers’ sales forecasting capabilities, improve inventory management, and drive growth. This comprehensive approach to sales forecasting helps retailers stay competitive and meet customer demands effectively.

Customer Behavior Analysis

The integration of AI allows for improved personalization in customer experiences, tailoring marketing strategies to individual preferences. By identifying distinct consumer segments, retailers can create targeted marketing strategies based on KPI insights. Customer segmentation tools categorize shoppers by their purchasing behavior and preferences. This process improves personalized marketing strategies. This comprehensive approach to customer behavior analysis helps retailers understand their customers better and drive engagement.

Advanced analytics techniques like predictive modeling analyze customer behavior to create detailed segments based on buying habits and preferences. POS systems not only process transactions but also gather valuable customer data for analysis, influencing marketing strategies. These insights enable retailers to craft personalized marketing strategies that resonate with customers and drive sales.

Analyzing customer data is crucial for understanding shopping patterns and preferences, which helps in crafting personalized marketing strategies. Correlating in-store analytics with web analytics provides retailers a comprehensive view of customer interactions and optimizes marketing efforts. This comprehensive approach to customer behavior analysis helps retailers enhance customer satisfaction, improve engagement, and drive growth.

A depiction of various tools used for effective retail analytics, including software and systems.

Tools for Effective Retail Analytics

Effective retail analytics requires the use of various tools that capture and process extensive data within the retail ecosystem. Data is captured at physical store locations and on websites, providing a comprehensive understanding of customer behavior. Retail analytics tools must integrate seamlessly with existing systems to maximize their effectiveness. AI technologies enable retailers to analyze large datasets and gain actionable insights, driving growth and efficiency.

Emerging technologies like natural language processing and computer vision are expected to enhance retail data analysis capabilities. These advanced analytics solutions enable retailers to make informed decisions, optimize their operations, and improve customer satisfaction. By integrating these tools into their retail strategies, retailers can stay competitive and drive growth in a rapidly evolving market.

Point of Sale (POS) Systems

Point of Sale (POS) systems play a critical role in retail analytics by monitoring customer transactions and providing valuable insights into purchases and trends. These systems enable retailers to better understand consumer behavior, allowing them to make informed decisions about inventory management, pricing strategies, and marketing efforts. POS data helps retailers optimize operations, improve customer satisfaction, and drive growth.

In addition to POS systems, customer analytics leverages data from websites, phone logs, and customer service chats to gain a comprehensive understanding of customer interactions. Integrating these data sources allows retailers to create a holistic view of customers, tailor marketing strategies, and improve overall business performance.

This comprehensive approach to retail analytics helps retailers stay competitive and meet customer demands effectively.

Customer Relationship Management (CRM) Software

One of the primary benefits of Customer Relationship Management (CRM) software is that it tracks customer interactions and identifies sales and marketing opportunities. CRM software tracks customer interactions, helping retailers understand preferences and behaviors to create personalized marketing strategies. This comprehensive approach to customer relationship management helps retailers improve customer satisfaction and drive growth.

CRM software plays a crucial role in retail by helping manage customer data and interactions effectively. The overall impact of CRM software results in improved customer service and enhanced satisfaction, which ultimately leads to increased customer loyalty and higher sales. CRM software helps retailers optimize operations, enhance customer engagement, and drive growth.

Business Intelligence Tools

Business Intelligence (BI) tools in retail analytics are capable of tracking KPIs, creating reports, and providing insights from diverse datasets. Good unified analytics software leverages accurate demand forecasts and provides customizable optimization options. Visualization tools are preferred over traditional data formats because they are more effective at presenting data than rows and columns. Benefits of visualization tools include helping users understand data better, enabling informed decisions, and making data accessible to business users.

Business users gain substantial benefits from BI visualization tools in terms of data comprehension and decision-making. Descriptive analytics employs business intelligence tools for generating regular sales and inventory reports. These reports provide insights into historical performance.

Automation of manual tasks in business intelligence practices leads to more efficient data handling. Advanced BI tools enable retailers to structure and visualize data effectively, allowing better analysis and insights.

Best Practices in Retail Analytics

Unified advanced retail analytics combines business intelligence, diagnostics, and demand forecasting with automation. The benefits of unified advanced analytics include automating tasks, optimizing at a granular level, and generating detailed recommendations. Analyzing past sales and shopping patterns allows retail analytics to predict demand and optimize stock levels. This comprehensive approach to retail analytics helps retailers improve operational efficiency, reduce costs, and drive growth.

Scalability is important in retail analytics software as it allows adaptation to evolving business needs without overspending. When evaluating retail analytics tools, retailers should consider total cost of ownership, ongoing expenses, and essential vs. non-essential features.

To overcome challenges related to big data analytics, retailers should start small, use cloud-based solutions, and invest in training or external support. These best practices help retailers successfully implement advanced analytics solutions and drive growth.

Integrating Multiple Data Sources

Integrating data from various sources is crucial for gaining a nuanced view of retail businesses. Using different applications for retail analytics can lead to incorrect analyses because of varying definitions for data types. This type of analytics combines various data sources, including financial metrics and customer feedback, to uncover the reasons for performance issues. Integrating multiple data sources provides retailers with a comprehensive understanding of operations and informs decisions that drive growth.

To achieve this integration, retailers should leverage advanced analytics solutions and business intelligence tools that can seamlessly combine internal and external data sources. By doing so, they can create a holistic view of their operations, optimize their strategies, and improve overall business performance. This comprehensive approach to data analytics helps retailers stay competitive and meet customer demands effectively.

Prioritizing Key Performance Indicators (KPIs)

Tracking KPIs is important for retailers as it measures performance and identifies improvement areas. Key performance indicators (KPIs) such as sales velocity and customer lifetime value are critical for assessing business performance, alongside metrics like sales growth, customer retention, inventory turnover, and cost savings. A common practice used by successful retailers for KPI tracking is known as balanced scorecarding, which involves weekly KPI summaries. By regularly monitoring their KPIs, retailers can effectively track performance and drive improvements.

Successful retailers follow up the initial review of KPIs with a deeper analysis to understand the reasons behind the performance outcomes. Prioritizing key performance indicators helps retailers focus on critical aspects of their business and make data-driven decisions to enhance overall performance.

This comprehensive approach to KPI tracking helps retailers improve operational efficiency, reduce costs, and drive growth.

Utilizing Advanced Analytics Solutions

Knowledge of future likelihoods and actions leading to best outcomes is essential for predictive analytics to provide effective recommendations. Inaccuracy and failure to manage retail complexities are prevalent issues in current sales forecasting methods. Predictive modeling and real-time personalization enabled by AI and machine learning significantly enhance retail analytics capabilities. Advanced analytics solutions automate data processing, improve efficiency, and help retailers make more informed decisions.

Advanced analytics solutions like Retalon provide automation of manual tasks within Business Intelligence practices. User-friendly dashboards enable retailers to make fast, data-driven decisions by visualizing complex data quickly. By utilizing advanced analytics solutions, retailers can enhance their decision-making processes, improve customer satisfaction, and drive growth.

A futuristic representation of trends in retail analytics and technology advancements.

Future Trends in Retail Analytics

AI-based data analyses are expected to become normalized in the future of retail analytics. Predictive analytics powered by quantum computing can provide near-certainty in forecasting. AI-powered computer vision will transform physical stores into data goldmines by tracking customer foot traffic and inventory levels. Real-time analytics in BI tools allow retailers to quickly respond to market changes and customer behavior. The emergence of 5G networks will greatly increase the volume of big data in retail. This growth will facilitate real-time personalization and dynamic pricing.

Big retail players need to connect data quickly to enhance decision-making. Edge computing moves processing power to store shelves, allowing immediate analysis of customer behavior. The focus of business users is shifting from producing reports to using analytics integrated into their daily workflows. Retail analytics is expected to become more integrated and less noticeable in use.

Digital twins are used in retail to simulate and optimize store layouts and delivery routes. Staying ahead of these trends allows retailers to enhance operations, improve customer satisfaction, and drive growth.

Summary

In summary, retail analytics is a powerful tool that provides actionable insights, improves decision-making processes, and enhances customer satisfaction. By leveraging advanced data analytics techniques, retailers can increase revenue, reduce costs, and optimize profit margins. Understanding the key types of retail analytics—descriptive, diagnostic, predictive, and prescriptive—is essential for making informed decisions that drive growth and efficiency.

Retail analytics has a wide range of applications, including inventory management, sales forecasting, and customer behavior analysis. By utilizing essential retail analytics tools such as POS systems, CRM software, and Business Intelligence tools, retailers can gather and process extensive data to gain valuable insights. Following best practices in retail analytics, such as integrating multiple data sources, prioritizing key performance indicators, and utilizing advanced analytics solutions, helps retailers stay competitive and meet customer demands effectively.

The future of retail analytics is bright, with AI-based data analyses, quantum computing, and 5G networks set to revolutionize the industry. By staying ahead of these trends and implementing advanced analytics solutions, retailers can enhance their operations, improve customer satisfaction, and drive growth. Embrace the power of retail analytics and take your retail business to new heights.

Frequently Asked Questions

What is retail analytics?

Retail analytics is the systematic examination of sales data and customer transactions to derive actionable insights that enhance decision-making and improve customer satisfaction.

How can retail analytics improve inventory management?

Retail analytics significantly enhances inventory management by identifying demand trends, which prevents overstock and stockouts, while also allowing for real-time price adjustments to align with market conditions. This data-driven approach ultimately leads to more efficient inventory control and improved sales performance.

What are the key types of retail analytics?

The key types of retail analytics are descriptive, diagnostic, predictive, and prescriptive. Each type enhances business insights and supports informed decision-making.

How does predictive analytics aid in sales forecasting?

Predictive analytics significantly enhances sales forecasting by leveraging historical sales data and customer purchase patterns to anticipate future trends and demand. This enables businesses to optimize planning, marketing strategies, and inventory management.

What are the future trends in retail analytics?

Future trends in retail analytics will be driven by AI-based data analyses, quantum computing, and real-time analytics, alongside advancements in 5G networks and edge computing. These innovations, including the use of digital twins, will enhance the optimization of store layouts and delivery routes.

Next Steps

For more information or enquiries about retail analytics services, feel free to contact us below.

The Ultimate Guide to Embedded Analytics

Keys to Product Selection and Implementation

Embedded Analytics for Everyone

The world is full of paradoxes. Here’s one for data analytics professionals: analytics becomes pervasive when it disappears.

For decades, business intelligence (BI) and analytics tools have failed to penetrate more than 25% of an organization. And within that 25%, most workers use the tools only once or twice a week. Embedded analytics changes the equation. By inserting charts, dashboards, and entire authoring and administrative environments inside other applications, embedded analytics dramatically increases BI adoption. The catch is that most business users don’t know they’re “using BI”—it’s just part of the application they already use. The best BI tools are invisible.

By placing analytics at the point of need—inside operational or custom applications—embedded analytics closes the last mile of BI. Workers can see the impact of past actions and know how to respond to current issues without switching applications or context. Analytics becomes an indispensable part of the way they manage core processes and solve problems. As a result, embedded analytics has a much higher rate of adoption than traditional BI or analytics.

Embedded analytics has much higher rate of adoption than traditional BI or analytics.

Target Organizations

Embedded analytics makes existing applications more valuable for every organization. Independent software vendors (ISVs) say that embedded analytics increases the value of their applications and enables them to charge more for them. Enterprise organizations embed analytics into operational applications, such as Salesforce.com, and intranet portals, such as SharePoint. In both cases, embedded
analytics puts data and insights at users’ fingertips when they need it most—both to gain insights and take action.

ISV requirements. In the embedded world, ISVs have more stringent requirements than traditional organizations. (See “Twelve Evaluation Criteria” below.) ISVs must ensure an embedded product looks and feels like their host application, and thus require greater levels of customization and extensibility.

Also, cloud ISVs require embedded products that work in multi-tenant environments, with seamless user administration and custom deployments. Many ISVs offer tiered pricing, which requires embedded products with flexible user provisioning. Finally, because ISVs can’t always estimate how many customers will purchase the analytics, they need flexible and affordable pricing models.

Enterprise requirements. Traditional enterprises have fewer requirements than ISVs, but that is changing. More companies are pursuing digital strategies that require customer-facing Web applications. And although most don’t charge for analytics, as ISVs do, many view data analytics as a key part of the online customer experience. For example, mutual funds now provide customers with interactive dashboards where they can slice and dice their portfolios and take actions such as buying and selling funds. Thus, their requirements for customization, extensibility, multi-tenancy, and security have grown significantly in recent years.

Build or Buy?

Once organizations decide to embed analytics, they need to make a few key decisions. The first is whether to build their own analytics or buy a commercial off-the-shelf tool.

Build. Organizations with internal developers are always tempted to build their own analytics. But
unless the analytics are simple and users won’t request changes, it’s always smart to outsource analytics to a commercial vendor. Commercial analytics products deliver best-of-breed functionality that would take in-house developers years to develop, distracting them from building the host application.

Buy. Many analytics vendors have made their tools more configurable, customizable, and integrateable with host applications. Most see embedded analytics as a big market and aim to make their tools blend seamlessly with third-party applications. They also make it easy to customize the tool without coding, including changing the graphical user interface (GUI) or the ways users navigate through the tool or interact with its components. When extensive customization or integration is required, customers can use application programming interfaces (APIs) to fine-tune the tool’s look and feel, create new data connectors, charts, event actions, and export types.

Types of Embedding

The second decision is to figure out the architecture for embedding analytics. From our research, we’ve
discovered three primary approaches. (See figure 1.)

1. Detached analytics. This is a lightweight form of embedding where the two applications—host and analytics—run separately but are tightly linked via URLs. This approach works well when multiple applications use the same analytics environment, portal, or service. A common example is Google Analytics, a commercial service that multiple groups inside an organization might use to track Web traffic on various internal websites. There is no commonality between the host application and analytics tool, except for a shared URL and shared data. The two applications might also share a common authentication mechanism to facilitate single signon (SSO). Ths approach is rather uncommon these days.

2. Inline analytics. With inline analytics, output from an analytics tool is embedded into a host application—it looks, feels, and acts like the host but runs as a separate element, tab, or module within it. For example, a newspaper might embed a chart within the text of an article on a webpage. Or an ERP application might present users with a dashboard upon logging in that displays summary activity from each module in the application. Or there might be a separate tab where customers can view analytics about their activity within the application. In most
cases, the embedded components sit within an iFrame, which is an HTML container that runs inside a webpage. iFrames were once the predominant method for embedding analytics content but are disappearing due to security and other concerns. (See next section.)

3. Fused analytics. Fused analytics delivers the tightest level of integration with a host application. Here, the analytics (e.g., a chart, table, or graphical component) sit side by side with the host application components and communicate bi-directionally with them. This created a “fused” or integrated environment where the end users aren’t aware that a third-party tool is part of the experience.

For example, an inventory manager can view inventory levels in various warehouses and place replenishment orders without leaving the screen. Or a retail store manager can view daily demand forecasts and then click a button to create the shift schedule for the following week. Fused analytics is facilitated by JavaScript libraries that control front-end displays and REST API calls that activate server functions. Analytics tools with extensive API libraries and programming frameworks make all their functionality available within a host application, including collaboration capabilities, alerts, reporting and augmented analytics features.

Technology Implications

Most analytics vendors can support inline analytics without much difficulty. They simply provide “embed code”—a snippet of HTML and JavaScript—that administrators can insert into the HTML code of a webpage. The embed code calls the analytics application to display specified content within an iFrame on the webpage. People who use YouTube and other social media services are familiar with embed code.
iFrames are a quick and easy way to embed third-party content, and most analytics vendors support them.

But iFrames have disadvantages. Because they are frames or windows inside a webpage that are controlled by an external application or service, they pose considerable security risks. Also, they operate independently of the host webpage or application—the host can’t manipulate what’s inside the iFrame, and vice versa. For example, hitting the back button doesn’t change what’s inside the iFrame.
Furthermore, iFrames behave differently depending on the browser, making them difficult to manage. Consequently, a growing number of organizations refuse to allow iFrames, and the software industry is moving away from them Fused analytics requires tight integration between an analytics and host application.

Fused analytics also requires a much greater degree of customization, extensibility, flexibility, and integration than many analytics vendors support out of the box. To simplify fused analytics, many BI vendors have wrapped their APIs in programming frameworks and command line interfaces (CLIs) that make it easy for programmers to activate all functions in the analytics tool. These Javascript frameworks and CLIs have been a boon to analytics embedding. Nonetheless, companies that want to fuse analytics into an application need to look under the covers of an analytics tool to discover its true embeddability. (See “Select a Product” below.)

Product Selection and Implementation

Another major decision is selecting a commercial analytics product to embed. Selecting a product that doesn’t include a key feature you need, such as alerts or exports to PDF or printing, can undermine adoption and imperil your project. Or maybe the product doesn’t work seamlessly in a multi-tenant environment, making administration cumbersome and time-consuming and contributing to errors that undermine customer satisfaction. Or your deployment drags out by weeks or months because most customizations require custom coding.

This report is designed to help you avoid these and other pitfalls of embedded analytics. Whether you are an independent software vendor (ISV) that needs to know how to embed analytics in a commercial, multi-tenant cloud application or the vice president of application development at major corporation who wants to enhance a homegrown application, this report will provide guidance to help you ensure a successful project.

The report outlines a four-part methodology:

Plan the project. Define goals, timeline, team, and user requirements.
Select a product. Establish evaluation criteria, create a short list of vendors, conduct a proof of concept, talk to references, and select a product.
Deploy the product. Define packaging and pricing (if applicable) and develop a go-to-market strategy that includes sales, marketing, training, and support.
Sustain the product. Monitor usage, measure performance, and develop an upgrade cadence that delivers new features and bug fixes.

The report’s appendix drills into the evaluation criteria in depth, providing questions that you should ask

prospective vendors to ensure their products will meet your needs.

1. Plan the Project

Many teams start their embedded analytics project by selecting an analytics product to deploy. Although choosing the vendor to power your analytics is a critical step, it shouldn’t be the first milestone you tackle. Instead, start by considering these questions: What are you trying to build, for whom, and why? These essential questions will help you better understand your product goals and will aid in selecting the

best tool to achieve your goals.

Start by asking: What are you trying to build, for whom, and why?

We recommend a six-step model to ensure that your analytics are not only technically successful, but achieve your business goals

Establish the Focus

Setting the goals for your analytics project is an essential first step to ensure that all key stakeholders—from the executive team to the end users—are fully satisfied upon project completion.

There are three basic steps to planning for a successful analytics project: define table stakes, define delighters, and define what’s out of scope.

Define table stakes. Table stakes are the essential elements the project must include in order to be considered successful. For example, an end-user organization might decide that the analytics must include dashboards for the CEO; an ISV or OEM might decide that the analytics must support a search bar for ad hoc queries; these items are considered “table stakes,” and you should plan to include them as part of your development plan.
Define delighters. Delighters are product elements that are “nice to have”—not essential, but highly beneficial to customer satisfaction. These may be elements that would greatly streamline workflows, or would simply make the product more enjoyable to use. They aren’t critical to using the application, but they would “delight” the customer if present.
Out-of-scope items. It’s also important to decide what won’t be in the product. Try to avoid putting the analytics team in the difficult situation of deciding whether to address a late-arriving customer request. Although it’s impossible to identify all out-of-bounds features or services in advance, you should try to create a set of guidelines. For example, you might choose to deliver dashboards that users can tailor to their needs, but don’t support raw data feeds. Or, you may decide that you’ll provide a standardized set of analytics covering a variety of use cases, but you won’t build customer specific, “one-off” data models.

Define the Project Team

The composition of the project team can be a key element in the success or failure of an analytics project. For example, more than a few projects have been derailed at the last minute when the legal team—not included in the process—surfaced major issues with the plan. When structuring your analytics product team, consider including the following roles in your “core team” for the project:

> Product owner/head of product
> CTO
> Head of development/engineering
> Head of sales
> Head of marketing
> Head of operations and support

Next, identify roles that, although not involved in daily decisions, will need to be consulted along the
way, including finance, billing, legal, and sales enablement/training.

Create the Plan

A best practice is to get the key project stakeholders in a single room to discuss core elements in a facilitated session. Although it might be necessary to have some participants attend remotely, in-person attendance makes for a faster and more effective session.

Too often project teams—whether analytics-focused or otherwise—fail to create a plan to guide the key steps required to bring embedded analytics to market. Without a plan, teams are liable to spend too much time gathering requirements and too little time analyzing persona needs. Without planning, the time required to perform key tasks, such as resolving issues from beta testing, might be overlooked. The
steps to creating a basic, but useful, plan are simple:

Set project goals. Setting project goals before any technical work starts is a good way to ensure that everyone involved agrees on the success criteria. Set aside time to create project goals as the first step in your analytics plan.

Set a timeline. A timeline may seem obvious, but it’s important that, in addition to the overall start and end dates, you plan for intermediate milestones such as:

Start/end of product design. When will you begin and end the process of defining what functionality will be in your analytics product? Without scheduled start and end dates, this segment of the process can easily extend far longer than anticipated.
Start/end of technical implementation. When will the technical aspects of the project commence and complete? This should include items such as connecting to data sources, implementing the analytics platform, integrating with the core product, and applying user management.
Start marketing efforts. If you build it, you want them to come. But you don’t want to raise expectations for a speedy arrival before development has completed. Plan on setting dates for key marketing activities, including the development of logos, advertising, creation of demos and sales collateral, and training documentation.
Start/end the beta period. Before you launch your analytics, you’ll want to test your analytical application with a set of carefully chosen beta users. Pick reasonable dates for this process and be sure to include time for selecting beta users, educating them on the product, reviewing testing results, and resolving identified issues.
Start/end user onboarding. Don’t forget that, once the product is complete, you still need to onboard any existing users. Plan to onboard users in tranches—define manageable groups so that your team doesn’t become overwhelmed with support issues. And don’t forget to leave time between each tranche so that you can resolve any issues you might find.

Create a Communication Plan

It’s easy to forget that although you, as a member of the product team, might be fully aware of everything that’s taking place within your analytics project, others might not know about your progress. In the absence of information, you might find that inaccurate data is communicated to customers or other interested groups. You can prevent this by establishing a communication plan, both for internal personnel and for potential customers. Although the plans will be different for those inside your walls versus external parties, all communication plans should include:

> Regular updates on progress
> Revisions of dates for key milestones
> Upcoming events such as sneak peeks or training sessions

Set Metrics and Tripwires

Once you’ve started your product development effort, particularly once you’ve started beta testing or rollout, it can be hard to identify when critical problems surface. That’s why setting metrics and tripwires is a good idea during the planning phase.

It can be hard to identify when critical problems surface.

That’s why setting metrics and tripwires is a good idea.

Metrics are used to measure product performance and adoption. An embedded product should have
a dashboard that enables ISVs and OEMs to monitor metrics and activity across all tenants using the
product, alerting administrators when performance goes awry. Consider tracking:

> Product uptime
> Responsiveness of charts and dashboards (i.e., load time)
> Data refresh performance and failures
> Number of reloads
> Total users
> Users by persona type
> Number of sessions
> Time spent using the analytics
> Churn (users who don’t return to the analytics application)
> Functionality used, e.g., number of reports published, alerts created, or content shared

Tripwires alert you to critical situations before they cause business-disrupting problems. They are metrics values that, if exceeded, trigger a response from the development team. As an example, you might have a tripwire that states if the product uptime is less than 99.9%, the general rollout of the analytics product will cease until the issue is resolved. Each metric should have an established tripwire, and each tripwire should contain the following elements:

> A metric value that, if exceeded, triggers a response.
> A predetermined response such as “stop rollout” or “roll back to the previous version.”
> A responsible party who reviews the metric/tripwire and determines if action is required.

Although metrics and tripwires don’t ensure project success, they can greatly reduce the time —and stress for the team—to address problems.

Choose Target Users

It’s a common mistake to think either that you fully understand the users’ needs or that all users are the same. Many teams launch embedded analytics products without considering the detailed needs of target users or even the different user types they might encounter. Avoid this situation by creating detailed user personas and doing mission/workflow/gap analysis.

Many teams launch embedded analytics products without considering the detailed needs of their users or even the different user types they might encounter.

Here’s how it works:

Step One: Choose personas. The best way to create an engaging data product is to deliver analytics that solve users’ problems. This is difficult to do for a generic “user,” but it can be accomplished for a specific user “persona” or user type. Start by picking two or three key user types (personas) for whom you will tailor the analytics. These might be strategic users looking for patterns and trends (like executives) or tactical users focused on executing work steps (like salespeople or order fulfillment teams). Although you may ultimately add functionality for many personas to your analytics, start with personas that can impact adoption—key decision makers—first. Get these user types engaged with your analytics application and they can help drive adoption among other users.

Step 2: Identify mission. For each chosen persona, the next task is to understand the user’s mission. What is the person trying to accomplish in their role? Are they trying to improve overall sales? Are they striving to increase revenue per customer? Understanding what the persona must accomplish will help you understand where analytics are needed and appropriate cadence.

Step 3: Map workflows and gaps. Now that you understand each persona’s mission, the third step is to outline the workflow they follow and any gaps that exist. These steps—and gaps—inform the project team where they can add analytics to assist the persona in accomplishing their mission. Keep it simple. If your persona is “head of sales” and the mission is “increase sales,” a simple workflow might be: (a) review sales for the month (b) identify actions taken within those segments (c) recommend more effective tactics to managers.

Within this workflow, you might find opportunities where analytics can improve the effectiveness of the head of sales. Perhaps reviewing sales performance or identifying underperforming segments requires running reports rather than simply reviewing a dashboard. Maybe seeing what actions have been taken requires investigating deep within the customer relationship management (CRM) system and correlating actions back to segments.

By finding information gaps within workflows and understanding personas’ missions,

project teams can ensure they put high-value analytics in front of users.

By finding information gaps within workflows and understanding personas’ missions, project teams can ensure that they put high-value analytics in front of users. It becomes less of a guessing game—replicating existing Microsoft Excel-based reports and hoping the new format attracts users—and more of a targeted exercise. Only analytics that truly add value for the persona are placed on the dashboard, in a thoughtful layout that aids in executing the mission. Engagement increases as target users solve problems using analytics.

2. Select an Embedded Analytics Product

Create Evaluation Criteria

Once you’ve defined user requirements, you need to turn them into specifications for selecting a product. The following evaluation criteria will help you create a short list of three or four vendors from the dozens in the market. The criteria will then guide your analysis of each finalist and shape your proof of concept.

We’ve talked with dozens of vendors, each with strengths and weaknesses. Analyst firms such as Gartner and Forrester conduct annual evaluations of Analytics and BI tools, some of which are publicly available on vendor websites. G2 provides crowdsourced research on BI tools, while the German research firm BARC publishes a hybrid report that combines analyst opinions and crowdsourced evaluations.

However, these reports generally don’t evaluate features germane to embedded analytics. That’s because the differentiators are subtle and often hard to evaluate, since it requires diving into the code.

The differentiators among embedded analytics are subtle and often hard to evaluate since it requires diving into the code.

Key Differentiators

For companies that want to tightly integrate analytics with a host application, there are three key things to look for:

> How quickly can you deploy a highly customized solution?
> How scalable is the solution?
> Does the vendor have a developer’s mindset?

Deployment speed. It’s easy to deploy an embedded solution that requires minimal customization. Simply replace the vendor logo with yours, change font styles and colors, copy the embed code into your webpage, and you’re done. But if you want a solution that has a truly custom look and feel (i.e., white labeling), with custom actions (e.g., WebHooks and updates), unique data sources and export formats,
and that works seamlessly in a multi-tenant environment, then you need an analytics tool designed from the ground up for embedding.

The best tools not only provide rich customization and extensibility,

but they also do so with minimal coding.

The best tools not only provide rich customization and extensibility, but they also do so with minimal coding. They’ve written their own application so every element can be custom-tailored using a pointand-click properties editor. They also provide templates, themes, and wizards to simplify development and customization. And when customers want to go beyond what can be configured out of the box, the
tools can be easily extended via easy-to-use programming frameworks that leverage rich sets of product APIs that activate every function available in the analytics tool.

Moreover, the best embeddable products give host applications unlimited ability to tailor analytic functionality to individual customers. This requires analytics tools to use a multi-tenant approach that creates an isolated and unique analytics instance for each customer. This enables a host company to offer tiered versions of analytic functionality to customers, and even allow customers to customize their analytics instance. This mass customization should work whether the host application uses multitenancy and/or containerization or creates separate server or database environments for each customer.

Scalability. It’s important to understand the scalability of an analytics solution, especially in a commercial software setting where usage could skyrocket. The tool needs strong systems administration capabilities, such as the ability to run on clusters and support load balancing. It also needs a scalable database—whether its own or a third party’s—that delivers consistent query performance as the number of concurrent users climbs and data volumes grow. Many vendors now offer in-memory databases or caches to keep frequently queried data in memory to accelerate performance. The software also must be designed efficiently with a modern architecture that supports microservices and a granular API. Ideally, it works in a cloud environment where processing can scale seamlessly on demand.

Developer mindset. When developers need to get involved, it’s imperative that an analytics tool is geared to their needs. How well is the API documented? Can developers use their own integrated development environment, or must they learn a new development tool? Can the tool run on the host application server or does it require a proprietary application server and database? How modern is the tool’s software architecture? Does it offer JavaScript frameworks, which help simplify potentially complex or repetitive tasks by abstracting API calls and removing the need for deep knowledge of the analytics tool’s APIs by your developers?

Companies are adopting modern software architectures and don’t want to
pollute them with monolithic, proprietary software from third parties

Increasingly, companies are adopting modern software architectures and don’t want to pollute them with monolithic, proprietary software from third parties. The embedded analytics solutions of the future will insert seamlessly into host code running on host application and Web servers, not proprietary servers and software.

Twelve Evaluation Criteria

It’s important to know what questions to ask vendors to identify their key differentiators and weak spots. Below is a list of 12 criteria to evaluate when selecting an embedded analytics product. (See the appendix for a more detailed description of each item.)

These criteria apply to both ISVs and enterprises, although some are more pertinent to one or the other. For instance, customization, extensibility, multi-tenancy, and pricing/packaging are very important for ISVs; less so for enterprises.

Embedding. What parts of the analytics tool can you embed, and which can you not? The best embedded analytics tools let you embed everything—including mobile usage, authoring, and administration. Are objects embedded via iFrames (i.e., inline) or modern techniques, such as Javascript programming frameworks?
Customization. What parts of the tool can you customize without coding? The best tools let you create a custom graphical interface that blends seamlessly with the host application without developer assistance. The less coding, the quicker the project deploys.
Extensibility. Does the tool provide APIs or plug-in frameworks that make it easy to add new functionality, such as new charts, data connectors, or export functionality?
Data architecture. How flexible is the data architecture? Can it query the host database and other data sources directly? Can it load data into its own in-memory or persistent database to improve scalability and performance? Can it model, clean, integrate, and transform data, if needed, using a point-and-click interface?
Process integration. Does the embedded analytics tool support bidirectional communication with the host application? Can the host navigation framework (i.e., a panel or tree) drive the analytics tool, and can the analytics tool update the host application? How much custom coding is required to support such integration?
Security. Does the tool support host application authentication and a single-sign-on framework? Does it support its own authentication framework, if needed? What level of permissions and access control does it support? Does it provide row- and column-level security?
Multi-tenancy. Can you customize a single dashboard so each tenant receives a different view? Can each tenant run its own database? Can each tenant configure permissions for its own analytics environment? Can customizations be upgraded? Most importantly, can tenants be centrally administered from a single console rather than individually?
Administration. Can the embedded product be managed from the application’s management console? Does the embedded product offer an administrative tool to handle DevOps, user management, systems monitoring, systems management (e.g., load balancing, cluster management, backup/restore), cloud provisioning, and localization?
Systems architecture. What is the systems footprint of the BI tool? Does it conform to your data center or cloud platform requirements? How lightweight is the product? Does it require an application server? Database server? Semantic layer?
Vendor. How much experience does the vendor have with embedding, and to what level (e.g., detached, inline, fused)? What kind of programs does it offer to jumpstart projects? Do they offer flexible or value-based pricing to match your requirements?
Analytics. What type of analytics does the product support? Is it predominantly a reporting tool, dashboard tool, OLAP tool, self-service tool, or data science tool? Although all vendors today provide a complete stack of functions, most excel in one or two areas. Does it offer value-added features, such as alerts, collaboration, natural language queries, and augmented analytics?
Software architecture. Does the analytics tool use a REST API and JSON to communicate between front-end and back-end components? Is the front end written with a JavaScript or Python framework? Is the software designed around microservices?

3. Build Your Analytics Application

With a plan and tool selected, the next step is to begin development. But perhaps not the development that might initially come to mind. We recommend that, alongside the technical implementation of your analytics, you develop the business aspects of your project. This phase requires you to fully consider how the analytics will be introduced to the market—how they will be packaged, priced, and supported post-launch.

Define Packaging

Start by designing the packaging for your analytics. Packaging is particularly important for software vendors who sell a commercial product. But enterprises that embed analytics into internal applications can also benefit from understanding these key principles.

Teams often consider analytics to be an “all-or-nothing” undertaking. You either have a complete set of analytics with a large set of features, or you don’t have any analytics at all.

But this approach fails to consider different user needs. Expert users may desire more sophisticated analytics functionality, while novice users may need less. The “all or nothing” approach also leaves you with little opportunity to create an upsell path as you add new features. It’s better to segment your analytics, giving more powerful analytics to expert users while allowing other users to purchase additional capabilities as they need them.

The Tiered Model

You never want to give users every conceivable analytical capability from the outset. Instead, use a tiered model. If you’ve ever signed up for an online service and been asked to choose from the “basic,” “plus,” or “premium” version of the product, you’ve seen a tiered model. Keep it simple, don’t add too many levels from which buyers are expected to choose. For example, you might use the following tiers:

> Basic. This is the “entry level” tier and should be the default for all users. You put these introductory, but still useful analytics in the hands of every single user so that they understand the value of analytical insights. Most organizations bundle these analytics into the core application without charging extra, but they usually raise the price of the core application by a nominal amount to cover costs.

> Plus. These are more advanced analytics, such as benchmarking against internal teams (e.g. the western region vs. the eastern region), additional layers of data (e.g. weather data, economic indicators, or financial data), or the ability to drill deeper into charts. This tier should be priced separately, as an additional fee on top of the core application.

> Premium. The top tier will be purchased for use by power users, analysts, or other more advanced users. Here, you might add in features such as external benchmarking (e.g. compare performance to the industry as a whole), and the ability for users to create entirely new metrics, charts, and dashboards. This will be the most expensive tier.

Architecting your offering in this format has several key benefits for data product owners:

> It doesn’t overwhelm the novice user. Although offering too little functionality isn’t ideal, offering too much can be worse. Upon seeing a myriad of complex and perhaps overwhelming features, users may decide the application is too complicated to use. These users leave and rarely return.

> It provides an upgrade path. Over time, you can expect customers to become more sophisticated in their analysis needs. Bar charts that satisfied users on launch day might not be sufficient a year down the road. The tiered model allows customers to purchase additional capabilities as their needs expand—you have a natural path for user growth.

> It makes it easier to engage users. How can you entice customers to buy and use your data product unless they can see the value that it delivers? Including a “basic” analytics tier with minimal, but still valuable, functionality is the answer. The basic tier can be offered free to all customers as a taste of what they can experience should they upgrade to your advanced analytics tiers.

Add-on Functionality

Unfortunately, not all customers will be satisfied by your analytics product, even if it’s structured into a tiered model. Some will require custom metrics, dashboard structures, and more data. Here are some “add-on” offerings that you can charge extra for:

> Additional data feeds. Although your core analytics product might include common data sources, some customers will require different or more data feeds for their use cases. These might include alternative CRM systems, alternative financial systems, weather, economic data, or even connections to proprietary data sources.

> Customized models. A “custom data model” allows buyers to alter the data model on which the product is based. If a buyer calculates “customer churn” using a formula that is unique to them, support this model change as an add-on.

> Visualizations. Customers often request novel ways of presenting information, such as new charts, unique mobile displays, or custom integrations.

> More data. The product team can augment an analytics application by providing more data: Seven years instead of five, detailed transactional records instead of aggregated data.

Services

Data applications can be complex, and they are often deeply integrated into many data sources. For this reason, you might consider offering services to augment your core analytics product:

> Installation/setup. Offer assistance to set up the analytics product, including mapping and connecting to the customer’s data sources, training in-house support personnel, and assisting with loading users.
> Customization. Offer to create custom charts, metrics, or data models.
> Managed analytics. Occasionally, a data product customer requests assistance in interpreting the analytics results. This can take the form of periodic reviews of the analytics (e.g., quarterly check-ups to assess performance) or an “expert-on-demand” service where the customer calls when they have analysis questions.

The situations above are very different from normal product technical support. Managed analysis services can be a highly lucrative revenue source, but they can also consume more resources than anticipated and skew your business model from a product orientation to a services model.

Establish Pricing

Pricing your analytics sounds like a simple proposition—far less complex than the technical product implementation—but that’s rarely the case. In fact, we’ve seen more than a few instances where the pricing portion of the project takes longer than the actual analytics implementation. But determining the fees to charge for your data product doesn’t have to be daunting. Here are our guidelines to help you
avoid pricing pitfalls.

Charge for your analytics. Analytics can help users make decisions faster, more accurately, and improve overall process cycle times. Such business improvements have value, and you should charge for providing it. However, if your analytics doesn’t add value, some product teams decide to offer analytics free of charge. When there is an apparent mismatch between the value and the price of an analytics product, the answer is to revisit the persona/mission/workflow/gap step of the development process.

Start early. Setting pricing late in the process is a mistake because once the product is fully defined and the costs are set, the flexibility you have for creating pricing options is severely limited. Start early and craft price points appropriate for each product tier (basic, plus, premium) rather than trying to rework key project elements just before launch day.

Keep it simple. Complicated pricing models turn off buyers. They cause confusion and slow the buying cycle. Limit the add-ons available and include key functions in the price of the core application.

Make the basic tier inexpensive. Keep the basic tier as inexpensive as possible. You want people to try analytics and get hooked so they purchase a higher tier. Roll the extra price into the cost of the core application and ensure that every user has access to the basic tier.

Match your business model. If your core application is priced based on a fee per user per month, add a small percentage to that fee to account for the additional value of analytics. Do not add a new line item called “Analytics Functionality” that jumps out at the buyer. Make analytics a part of what your product does.

Plan for Supporting Processes

Many teams spend significant energy designing analytics, creating dashboards, and thinking through pricing, but most forget to consider support processes. Embedded analytics as part of a customer-facing data product are inherently different from enterprise or “inside your walls” analytics. Data products require marketing plans, sales training, and other processes that will allow a properly designed analytics
application to flourish post-launch. Here’s how to get started planning your application’s supporting processes.

List the Impacted Processes

The first step to getting your supporting processes ready is enumerate exactly what will be impacted. There are two ways to go about this step:

1. Brainstorm the processes. The product team spends about 45 minutes brainstorming a list of potentially impacted processes. This is no different from any other brainstorming session—just be sure that you are listing processes (e.g. “process to intake/resolve/close support tickets”) and not organizational names that are less actionable (e.g., “the operations group”).

2. Work from other product lists. If you are part of an organization that has fielded products in the past, you might already have checklists for “organizational readiness” lying around. If so, the list of support processes developed by another product team might be a great place to start. You’ll find that you need to customize the list a bit, but the overlap should be significant, saving you time.

Here is a list of processes commonly impacted by a new data product:
> Provisioning or installation process
> New user onboarding process
> Trouble ticket or issue management
> User experience processes
> Product road mapping process, including request intake
> Utilization tracking or monitoring
> Sales training process
> Billing process
> Decommissioning or deactivation process

Define Changes to Processes

The next step is to determine the degree to which each of the listed processes might need to change to
support your new data product.

> Create a simple flow chart for each potentially impacted process.
> Apply scenarios. Pretend an issue occurs with your analytics. Can your existing process address it? Add or modify process steps to accommodate the analytics product.

> Publish and train. Present the new processes to any teams that might be impacted and store the process documentation wherever other process documents are kept.

Create Metrics

With new processes in place, you need to monitor process performance to ensure that everything is working as planned. For each new or modified process, create metrics to track cycle times, throughput, failure rate, cost, and customer satisfaction. Benchmark these processes against existing processes to ensure they are performing in parity.

4. Sustain Your Analytics

The process isn’t over when you’ve deployed your analytics and brought users on board. In fact, the most successful analytics teams view embedded analytics as a cycle, not a sprint to a finish line. Post-launch, you need to consider what is working and what isn’t; and you need to add or fine-tune functionality to better meet persona needs. You need to continuously improve to ensure that analytics adoption and usage doesn’t decline over time.

Create a Plan to Gather Feedback from Users

People always find unique ways of using analytics functionality. Learn what your users are doing; their experiments can guide your project development. Here are three ways to gather feedback on your analytical application:

> Use analytics on analytics. Some platforms allow you to monitor which dashboards, charts, and features are being used most frequently. Track usage at the individual and aggregate level. What functionality is being used? How often is the functionality reloaded? Number of sessions? New, recurring, one time use counts? Three-month growth by user, tenant, type of user, etc?

> Monitor edge cases. The users who complain the most, submit requests, and call your help desk are a gold mine of information. They will often talk—at length—about what functionality can be implemented to make the analytics better for everyone. Don’t ignore them.

> Shoulder surfing. Shoulder surfing is an easy way to gather insights. Get permission from users to observe them interacting with the analytics product on their own tasks at their own computers in their own environments. Shoulder surfing can uncover incredible insights that users might fail to mention in a formal focus group.

Build a Road Map to Expand Personas and Functionality

Although you started with a limited number of personas and workflows during the initial implementation, the key to sustaining your analytics is to expand both the personas served and the use cases addressed. If you started with an executive persona, consider adding tactical personas that need information about specific tasks or projects. Also, add workflows for existing personas. For example, add a budgeting dashboard for the CFO to complement the cash flow might analytics previously deployed.

Communicate the Plan

Unfortunately, in the absence of new information, users will assume that no progress is being made. Even if you can’t add all the personas, workflows, and functionality required immediately, make sure to create a communication plan so users understand what’s coming next and for whom.

Conclusion: Success Factors

Embedding another product in your application is not easy. There’s a lot that can go wrong, and the technology is the easy part. The hard part is corralling the people and establishing the processes required to deliver value to customers.

Here are key success factors to keep at the forefront of your mind during an embedded analytics project:

1. Know your strategy. If you don’t know where you’re going, you’ll end up somewhere you don’t want to be. Define the goal for the project and keep that front and center during design, product selection, and implementation.
2. Know your users. Pushing dashboards to customers for the sake of delivering information will not add value. Dashboards, whether embedded or not, need to serve an immediate need of a specific target group. Identify and address information pain points and you’ll succeed.
3. Identify product requirements. It’s hard to tell the difference between embedded analytics tools. Use the criteria defined in this report to find the best product for your needs. It may not be one you already know!
4. Define a go-to-market strategy. Here’s where the wheels can fall off the bus. Before you get too far, assemble a team that will define and execute a go-to-market strategy. Especially if you are an ISV, get your sales, marketing, pricing, support, training, and legal teams together at the outset. Keep them informed every step of the way. Make sure they provide input on your plan.

Following these key principles will help ensure the success of your embedded analytics project

Next Steps

For more information or enquiries about Qlik products and services, feel free to contact us below.

The Ultimate Guide to Embedded Analytics

Establishing a trusted data foundation for AI

Introduction

Artificial intelligence (AI) is expected to greatly improve industries like healthcare, manufacturing, and customer service, leading to higher-quality experiences for customers and employees alike. Indeed, AI technologies like machine learning (ML) have already helped data practitioners produce mathematical predictions, generate insights, and improve decision-making.

Furthermore, emerging AI technologies like generative AI (GenAI) can create strikingly realistic content that has the potential to enhance productivity in virtually every aspect of business. However, AI can’t succeed without good data, and this paper describes six principles for
ensuring your data is AI-ready

The six principles for AI-ready data

It would be foolish to believe that you could just throw data at various AI initiatives and expect magic to happen, but that’s what many practitioners do. While this approach might seem to work for the first few AI projects, data scientists increasingly spend more time correcting
and preparing the data as projects mature.

Additionally, data used for AI has to be high-quality and precisely prepared for these intelligent applications. This means spending many hours manually cleaning and enhancing the data to ensure accuracy and completeness, and organizing it in a way that machines can easily understand. Also, this data often requires extra information — like definitions and labels — to enrich semantic meaning for automated learning and to help AI perform tasks more effectively.

Therefore, the sooner data can be prepared for downstream AI processes, the greater the benefit. Using prepped, AI-ready data is like giving a chef pre-washed and chopped vegetables instead of a whole sack of groceries — it saves effort and time and helps ensure that the final dish is promptly delivered. The diagram below defines six critical principles for ensuring the “readiness” of data and its suitability for AI use.

The remaining sections of this paper discuss each principle in detail.

Data has to be diverse.

Bias in AI systems, also known as machine learning or algorithm bias, occurs when AI applications produce results reflecting human biases, such as social inequality. This can happen when the algorithm development process includes prejudicial assumptions or, more commonly, when the training data has bias. For example, a credit score algorithm may deny a loan if it consistently uses a narrow band of financial attributes.

Consequently, our first principle focuses on providing a wide variety of data to AI models, which increases data diversity and reduces bias, helping to ensure that AI applications are less likely to make unfair decisions.

Diverse data means you don’t build your AI models on narrow and siloed datasets. Instead, you draw from a wide range of data sources spanning different patterns, perspectives, variations, and scenarios relevant to the problem domain. This data could be well-structured
and live in the cloud or on-premises. It could also exist on a mainframe, database, SAP system, or software as a service (SaaS) application. Conversely, the source data could be unstructured and live as files or documents on a corporate drive.

It’s essential to acquire diverse data in various forms for integration into your ML and GenAI applications.

Data has to be timely.

While it’s true that ML and GenAI applications thrive on diverse data, the freshness of that data is also crucial. Just as a weather forecast based on yesterday’s conditions isn’t conducive for a trip you plan to take today, AI models trained on outdated information can produce inaccurate or irrelevant results. Moreover, fresh data allows AI models to stay current with trends, adapt to changing circumstances, and deliver the best possible outcomes. Therefore, the second principle of AI-ready data is timeliness.

It’s critical that you build and deploy low-latency, real-time data pipelines for your AI initiatives to ensure timely data. Change data capture (CDC) is often used to deliver timely data from relational database systems, and stream capture is used for data originating from IoT devices that require low-latency processing. Once the data is captured, target repositories are updated and changes continuously applied in near-real time for the freshest possible data.

Remember, timely data enables more accurate and informed predictions.

Data has to be accurate.

The success of any ML or GenAI initiative hinges on one key ingredient: correct data. This is because AI models act like sophisticated sponges that soak up information to learn and perform tasks. If the information is inaccurate, it’s like the sponge is soaking up dirty water, leading to
biased outputs, nonsensical creations, and, ultimately, a malfunctioning AI system. Therefore, data accuracy is the third principle and a fundamental tenet for building reliable and trustworthy AI applications.

Data accuracy has three aspects. The first is profiling source data to understand its characteristics, completeness, distribution, redundancy, and shape. Profiling is also commonly known as exploratory data analysis, or EDA.

The second aspect is operationalizing remediation strategies by building, deploying, and continually monitoring the efficacy of data quality rules. Your data stewards may need to be involved here to aid with data deduplication and merging. Alternatively, AI can help automate and accelerate the process through machine-recommended data quality suggestions.

The final aspect is enabling data lineage and impact analysis — with tools for data engineers and scientists that highlight the impact of potential data changes and trace the origin of data to prevent accidental modification of the data used by AI models.

High-quality, accurate data ensures that models can identify relevant patterns and

relationships, leading to more precise decisions, generation, and predictions.

Data has to be secure.

AI systems often use sensitive data — including personally identifiable information (PII), financial records, or proprietary business information — and use of this data requires responsibility. Leaving data unsecured in AI applications is like leaving a vault door wide open. Malicious actors could steal sensitive information, manipulate training data to bias outcomes, or even disrupt entire GenAI systems. Securing data is paramount to protecting privacy, maintaining model integrity, and ensuring the responsible development of powerful AI applications. Therefore, data security is the fourth AI-ready principle.

Again, three tactics can help you automate data security at scale, since it’s nearly impossible to do it manually. Data classification detects, categorizes, and labels data that feeds the next stage. Data protection defines policies like masking, tokenization, and encryption to obfuscate the data. Finally, data security defines policies that describe access control, i.e., who can access the data. The three concepts work together as follows: first, privacy tiers should be defined and data tagged with a security designation of sensitive, confidential, or restricted. Next, a protection policy should be applied to mask restricted data. Finally, an access control policy should be used to limit access rights.

These three tactics protect your data and are crucial for improving

the overall trust in your AI system and safeguarding its reputational value.

Data has to be discoverable.

The principles we’ve discussed so far have primarily focused on promptly delivering the right data, in the correct format, to the right people, systems, or AI applications. But stockpiling data isn’t enough. AI-ready data has to be discoverable and readily accessible within the system. Imagine a library with all the books locked away — the knowledge is there but unusable. Discoverable data unlocks the true potential of ML and GenAI, allowing these workloads to find the information they need to learn, adapt, and produce ground-breaking results. Therefore,

discoverability is the fifth principle of AI-ready data.

Unsurprisingly, good metadata practices lie at the center of discoverability. Aside from the technical metadata associated with AI datasets, business metadata and semantic typing must also be defined. Semantic typing provides extra meaning for automated systems, while additional business terms deliver extra context to aid human understanding. A best practice is to create a business glossary that maps business terms to technical items in the datasets, ensuring a common understanding of concepts. AI-assisted augmentation can also be used to automatically generate documentation and add business semantics from the glossary. Finally, all the metadata is indexed and made searchable via a data catalog.

This approach ensures that the data is directly discoverable, applicable,

practical, and significant to the AI task at hand.

Data has to be easily consumable by MLs or LLMs.

We’ve already mentioned that ML and GenAI applications are mighty tools, but their potential rests on the ability to readily consume data. Unlike humans, who can decipher handwritten notes or navigate messy spreadsheets, these technologies require information to be represented in specific formats. Imagine feeding a picky eater — if they won’t eat what you’re serving, they’ll go hungry. Similarly, AI initiatives won’t be successful if the data is not in the right format for ML experiments or LLM applications. Making data easily consumable helps unlock the potential of these AI systems, allowing them to ingest information smoothly and translate it into intelligent actions for creative outputs. Consequently, making data readily consumable is the final principle of AI-ready data.

Making Data Consumable for Machine Learning

Data transformation is the unsung hero of consumable data for ML. While algorithms like linear regression grab the spotlight, the quality and shape of the data they’re trained on are just as critical. Moreover, the effort invested in cleaning, organizing, and making data consumable by ML models reaps significant rewards. Prepared data empowers models to learn effectively, leading to accurate predictions, reliable outputs, and, ultimately, the success of the entire ML project.

However, the training data formats depend highly on the underlying ML infrastructure. Traditional ML systems are disk-based, and much of the data scientist workflow focuses on establishing best practices and manual coding procedures for handling large volumes of files. More recently, lakehouse-based ML systems have used a database-like feature store, and the data scientist workflow has transitioned to SQL as a first-class language. As a result, well-formed, high-quality, tabular data structures are the most consumable and convenient data format
for ML systems.

Making Data Consumable for Generative AI

Large language models (LLMs) like OpenAI’s GPT-4, Anthropic’s Claude, and Google AI’s LaMDA and Gemini have been pre-trained on masses of text data and lie at the heart of GenAI. OpenAI’s GPT-3 model was estimated to be trained with approximately 45 TB of
data, exceeding 300 billion tokens. Despite this wealth of inputs, LLMs can’t answer specific questions about your business, because they don’t have access to your company’s data. The solution is to augment these models with your own information, resulting in more correct, relevant, and trustworthy AI applications.

The method for integrating your corporate data into an LLM-based application is called retrieval augmented generation, or RAG. The technique generally uses text information derived from unstructured, file-based sources such as presentations, mail archives, text documents, PDFs, transcripts, etc. The text is then split into manageable chunks and converted into a numerical representation used by the LLM in a process known as embedding. These embeddings are then stored in a vector database like Chroma, Pinecone, and Weviate. Interestingly, many traditional database vendors — such as PostgreSQL, Redis, and SingleStoreDB — also support vectors. Moreover, cloud platforms like Databricks, Snowflake, and Google BigQuery have recently added support for vectors, too.

Whether your source data is structured or unstructured, Qlik’s approach ensures that
quality data is readily consumable for your GenAI, RAG, or LLM-based applications.

The AI Trust Score

Having defined the six core principles of data readiness and suitability, the questions remain: can the principles be codified and easily translated for everyday use? And how can the readiness for AI be quickly discerned? One possibility is to use Qlik’s AI Trust Score as a global and understandable readiness indicator.

The AI Trust Score assigns a separate dimension for each principle and then aggregates each value to create a composite score, a quick, reliable shortcut to assessing your data’s AI readiness. Additionally, because enterprise data continually changes, the trust score is regularly checked and frequently recalibrated to track data readiness trends.

Your AI Trust Score aggregates multiple metrics into a single, easy to-understand readiness Score.

Qlik Talend data foundation for AI

The need for high-quality, real-time data that drives more thoughtful decisions, operational efficiency, and business innovation has never been greater. That’s why successful organizations seek market-leading data integration and quality solutions from Qlik Talend to efficiently deliver trusted data to warehouses, lakes, and other enterprise data platforms. Our comprehensive, best-in-class offerings use automated pipelines, intelligent transformations, and reliable Datasets quality to provide the agility data professionals crave with the governance and
compliance organizations expect.

So, whether you’re creating warehouses or lakes for insightful analytics, modernizing operational data infrastructures for business efficiency, or using multi-cloud data for artificial intelligence initiatives, Qlik Talend can show you the way.

Conclusion

Despite machine learning’s transformative power and generative AI’s explosive growth potential, data readiness is still the cornerstone
of any successful AI implementation. This paper described six key principles for establishing a robust and trusted data foundation that
combine to help your organization unlock AI’s true potential.

Next Steps

For more information or enquiries about Qlik products and services, feel free to contact us below.

Harness the Full Value of Your SAP Data for Cloud Analytics

Powered by Qlik and Snowflake

SAP is the Life Blood of Your Enterprise

Unlock your SAP data for faster time to insight and value

Today’s digital environment demands instant access to information for real-time decision-making, improved business agility, amazing customer service, and competitive advantage. Every person in your business, no matter what their role, requires easy access to the most accurate data set to make informed decisions – especially when it comes to SAP.

Industries like Manufacturing, Retail, CPG, Oil and Gas, and many others rely on actionable data from SAP to make strategic decisions and maintain competitive advantage

Today's Business Needs for Analytics

Faster Time to value

• Reduce time to insight with increased speed and agility
• More self-service analytics

Freedom

• Use SAP data anywhere
• Utilize 3rd party software tools most suited for the job at hand

Modern Analytics

• Combine SAP + non-SAP data
• Real-time, predictive (AI/ML)

Control Costs

• Facilitate scale with better cost control
• Reduce SAP Analytics TCO

Fully Leveraging SAP Data is Difficult

So why doesn’t every company embrace the opportunity to use new analytic platforms — by streaming live SAP data for real time analytics or combining it with other data sources in data lake and data warehouse platforms in the cloud?

SAP systems come with a number of unique challenges that are inherent to the platform. They are challenging to integrate because they are structurally complex with tens of thousands of tables that have intricate relationships as well as proprietary data formats making data inaccessible outside of SAP applications.

This complexity means that integrating data for analytics can be cumbersome, time consuming and costly.

SAP datasets are full of value, but they won’t do your organization any good unless you can easily use them in a cost-effective, secure way

The Fast Path to Extracting Value from SAP Data in the Cloud

Realize the value of all your SAP data with Qlik® and Snowflake

Qlik helps you efficiently capture large volumes of changed data from your source SAP systems, as well as other enterprise data sources, and deliver analytics ready data in real-time to Snowflake. Data sets are then easily cataloged, provisioned, and secured for all your analytic initiatives such as AI, machine learning, data science, BI
and operational reporting.

Qlik and Snowflake remove the business and technical challenges of complexity, time and cost, while gaining flexibility and agility for your SAP data. This unlocks your SAP data for different analytics use cases, and allows you to combine it with other enterprise data for even deeper insights and increased value.

SOLUTION USE CASE

Data Warehousing
Quickly load a new SAP data into the Snowflake Data Cloud in real-time gaining new and valuable insights.

Data Lake
Easily ingest or migrate SAP data into the Snowflake Data Cloud to support a variety of workloads unifying and securing all your organizations data in one place.

Data Science & AI/ML
Speed up workflows and transform SAP data into ML-powered insights using your language of choice with Snowflake.

The Benefits of Offloading SAP Data into Snowflake

The Snowflake platform provides a simple and elastic alternative for SAP customers that simultaneously ensures an organization’s critical information is protected. Snowflake was built from scratch to run in the cloud and frees companies from cloud provider lock-in.

Architectural Simplicity

Snowflake improves the ease of use of SAP with greater architectural simplicity. Snowflake can ingest data from SAP operations systems (both on-premises and cloud), third-party systems, and signal data – whether its in structured or semi-structured formats. This provides a foundation for customers to build 360-degree views of customers, products, and the supply chain enabling trusted business content to be accessible to all users.

Convenient Workload Elasticity

Snowflake’s multi-cluster shared data architecture separates compute from storage, enabling customers to elastically scale, up and down, automatically or on the fly. Users can apply dedicated compute clusters to each workload in near-unlimited quantities for virtually unlimited concurrency without contention. Once
a workload is completed, compute is dialed back down in seconds so customers don’t have to deal with the frustrations of throttling SAP BW queries.

Reliable Data Security

With Snowflake, simplicity also means data security. All data is always encrypted—in storage or in transit—as a built-in feature of the platform. Data is landed
once and views are shared out. This means one copy, one security model, and hundreds of elastic compute clusters with monitors on each one of them.

Accelerate the Delivery of Analytics-Ready SAP Data to Snowflake

Breaking the barriers to modernizing SAP data analytics

Ingest and deliver SAP data to Snowflake in real time.

Qlik accelerates the availability of SAP data to Snowflake in real-time with its scalable change data capture technology. Qlik Data Integration supports all core SAP and industry modules, including ECC, BW, CRM, GTS, and MDG, and continuously delivers incremental data updates with metadata to Snowflake in real time.

Automate the data warehouse lifecycle and pipeline.
Once your SAP data has landed in the Snowflake Data Cloud, Qlik automates the data pipeline without the hand coding associated with ETL approaches. You can efficiently model, refine and automate data warehouse or lake lifecycles to increase agility and productivity.

SAP Solution Accelerators
Qlik also provides a variety of Business Solutions such as Order to Cash. Unique SAP accelerators with preconfigured integrations and process flows leverage Qlik’s deep technical mastery of SAP structures and complexities. They enable real-time ingestion (CDC) and rapid integration of SAP data into Snowflake. The automated mapping and data model generation eliminate the need for expensive, highly technical and risky manual mapping and migration processes.

Discover bolder business insights.
Use business analytics tools such as Qlik Sense or any other BI tool to explore the data, create interactive dashboards, and carry out a variety of BI use cases for data-driven insights.

A Proven and Optimized Solution for Unlocking Insights from SAP

Real-time data ingestion (CDC) from SAP to Snowflake

Automated mapping and data model generation for analytics (data marts)

Prepackaged solution accelerators for common business use cases (order-to-cash, financials,
inventory management)

Decode SAP proprietary source structures (pool/cluster tables, HANA/CDS views)

Supports all core and industry-specific SAP modules

World-class SAP expertise to support presales and customer success

A Track Record of Collaboration and Success

Qlik is a Snowflake Elite partner with Snowflake Ready validated Solutions for Data Integration and Analytics. We accelerate time-to-insight with our end-to-end data integration and analytics solution taking you from your unrealized data value in your SAP landscape to informed action in your Snowflake Data Cloud. Qlik’s solution for Snowflake users automates the design, implementation and updates of data models while minimizing the manual, error-prone design processes of data modeling, ETL coding and scripting. As a result, you can accelerate
analytics projects, achieve greater agility and reduce risk – all while fully realizing the instant elasticity and cost advantages of Snowflake’s Data Cloud.

Next Steps

For more information or enquiries about Qlik products and services, feel free to contact us below.

Artificial Intelligence:
Our Strategy

How we infuse AI across our business,
from our products to how we operate

Introduction

A long-time leader and innovator in the data, analytics, and AI space, Qlik is perfectly positioned to fully embrace AI — not only in our products, but also in the way we conduct business — and do so responsibly. As the rise of generative AI accelerated the requirement for organizations to modernize their data fabric, it created new opportunities for Qlik to innovate in support of our customers’ efforts in developing and implementing their AI strategies. Over the past year, we have continued to lead through new acquisitions, product
innovation, talent development, technology investments, and by establishing new systems and processes.

Pillar 1

AI Foundation

AI can’t succeed without good data: It is fully dependent on an organization’s ability to establish a trusted data foundation. This was already the case with predictive AI, but the rise of generative AI — which relies on data to function — has accelerated the need for companies to modernize their data fabric. Our point of view is that there are six principles to follow for creating AI-ready data and our
product strategy for our data integration and quality portfolio fully aligns to them:

1. Data should be diverse (coming from a wide range of resources) to remove bias in AI systems

2. Data should be timely to make accurate and informed predictions

3. Data should be accurate to ensure reliability and trustworthiness in AI

4. Data should be secure to safeguard the reputation of your AI

5. Data should be discoverable to enable use of relevant and contextual data

6. Data should be consumable for ML training and LLM integration

Our Portfolio

Our data integration portfolio has always been designed to move data from any source to any target, in real time. As these destinations will often use AI on this data, this is data integration operating in the service of AI, including generative AI. Qlik’s differentiation is our ability to take the best-in-class capabilities that we are known for (real-time data integration and transformation at scale) and make
them available for generative AI use cases.

In July 2024, we launched Qlik Talend Cloud®. This new flagship offering combines the best functionality of legacy solutions Qlik Cloud® Data Integration, Talend® Cloud, and Stitch Data, and is designed to help our customers implement a trusted data foundation for AI.

Qlik Talend Cloud is built on Qlik’s cloud infrastructure platform, with the focus on managing the data integrity of our customers’ AI, analytics, and business operational projects. It offers a unified package of data integration and quality capabilities that enable data engineers and scientists to deploy AI-augmented data pipelines that deliver trusted data wherever it’s needed. This includes:

Support for vector databases and multiple LLMs that help build data pipelines to support Retrieval Augmented Generation (RAG) applications
Ability to use custom SQL to transform datasets for training machine learning models
Address the trust and compliance needs of our customers in their use of AI through data lineage, impact analysis, and the ability to assess the trustworthiness of AI datasets (providing a trust score) We have provided productivity-enhancing tools (like a co-pilot) for data engineers (prompt to SQL), with more coming later this year.

We have provided productivity-enhancing tools (like a co-pilot) for data engineers (prompt to SQL), with more coming later this year.

What’s Next

For the latter part of 2024, we plan to introduce a range of dedicated components to support RAG implementations with the leading vector databases, embedding models, and LLMs. This will offer data engineers implementing AI workloads the same reliability and scalability they expect when operationalizing all their other workloads.

Looking ahead, our 2025 plan includes further enhancements through generative AI to further improve data engineer productivity, including data pipeline design tasks, dataset auto-classifications, automated workflows, and AI-assisted record deduplication.

WHO’S IT FOR

Data Engineers and Data Architects

These professionals need to ensure that data that will be used for downstream AI processes is of high quality and trustworthy. They also want to be able to deliver that data throughout their organization using AI-augmented, no-code pipelines.

Pillar 2

AI-Powered Analytics

Enriching analytical applications and workflows with AI-powered capabilities promotes enhanced, data-centric decision making and accelerates insights. While there has been much hype around generative AI over the last year, our point of view is that it isn’t the solution to everything. Instead, we believe that both predictive AI (i.e. traditional AI), which processes and returns expected results such as analyses and predictions, and generative AI, which produces newly synthesized content based on training from existing data, hold huge potential.

Therefore, our product strategy for our analytics portfolio encompasses both predictive and generative AI.

Our Portfolio

AI has always been foundational to Qlik Cloud Analytics, our flagship analytics offering. From analytics creation and data prep to data exploration — with natural language search, conversational analytics, and natural language generation — Qlik Cloud® is designed to enhance everything users do with AI.

Today, we offer a full range of AI-powered, augmented analytics capabilities that deepen insight, broaden access, and drive efficiency. This includes:

Automated insights: auto-generate a broad range of analyses in a few clicks
Natural language analytics (Insight Advisor): get answers to questions with relevant text and visualizations in ten languages
Proactive insights: proactively notifies users when AI detects important changes

What’s Next

Our product roadmap for Analytics AI is about enhancing outcomes through automation and integrated intelligence, spanning the following tenets of AI-powered analytics:

AI-assisted analytics, which provide improved ways to author and engage with business-ready content such as sheets, analysis types, reports, etc.
Generating and communicating insights, which provide a range of diagnostic, predictive, and prescriptive insights automatically through annotations
Natural language assistance, which provide users assistance to engage with their data, platform, and operations through natural language

WHO’S IT FOR

Application Creators and Users

These professionals are looking to build and use AI-infused applications for more powerful data analysis to support decision making — and do it in a way that is intelligent, automated, embedded, and intuitive (hence easier to adopt).

Pillar 3

AI Deployment (Self-Service AI)

Companies today are looking to create value with AI by building and deploying AI models. But following the hype of generative AI in 2023, this year there has been a shift in focus1 from large language models, which necessitate significant investments, to smaller models that are more cost efficient, easier, and faster to build and deploy.

Qlik’s product strategy is perfectly aligned to this shift. We offer self-service AI solutions that enable companies to deliver an AI experience for advanced, specific use cases in a way that is efficient and affordable with fast time to value.

Our Portfolio

In July 2024, we launched Qlik Answers™, a plug and-play, generative AI-powered knowledge assistant. Qlik Answers is a self-service AI solution that can operate independently from other Qlik products and is sold separately.

This tool allows organizations to deploy an AI model that can deliver answers from a variety of unstructured data sources. The ability to analyze unstructured data enables Qlik to deliver unique value to our customers, as it’s commonly believed that 80% of the world’s data is unstructured2. A study that the firm ETR conducted on our behalf in April 2024 also found that while companies understood the value potential of being able to deliver insights from unstructured data, less than one-third felt their organization was well equipped to do so.

With Qlik Answers, organizations can now take advantage of an out-of-the-box, consolidated self-service solution that allows users to get personalized, relevant answers to their questions in real time with full visibility of source materials. As with all Qlik products, our customers can also be assured that their data stays private. Moreover, with Qlik Answers, users will only have access to data that is curated for a specific
use case. With multiple, domain-specific knowledge bases being accessible to assistants, organizations stay in control of what content users can access.

To help ensure a successful implementation, our pricing and packaging for Qlik Answers includes starter services delivered by our customer success organization.

Since 2021, Qlik has been offering another self-service AI solution for predictive analytics, Qlik AutoML®. Like Qlik Answers, Qlik AutoML can

be purchased separately.

Qlik AutoML provides a guided, no-code machine learning experience that empowers analytics teams to perform predictive analytics without the support of data science teams. With AutoML, users can:

Auto-generate predictive models with unlimited tuning and refinement
Select and deploy the best-performing models based on scoring and ranking
Make predictions with full explainability

Note: While AutoML runs inside of Qlik Cloud, it can also be used independent of Qlik Cloud Analytics. We have customers who use a real-time API to return predictions back to their own systems without having to access Qlik Cloud.

Finally, Qlik also offers connectors to enable its customers to integrate third-party generative AI models in their analytics apps, load scripts, and automations. Qlik Cloud customers have the option to leverage our AI Accelerator program to integrate large language models into their applications.

What’s Next

In September 2024, we introduced new enhancements to Qlik AutoML’s capabilities,
including augmented MLOps, model optimization, and analytics views, with plans
for additional upgrades through the end of the year and into 2025. Future improvements are focused on the ability to create time-aware models and the introduction of a full, end-to-end MLOps lifecycle for models developed on the platform to ensure they can be adequately monitored and governed.

Although Qlik Answers is a new product, we’ve already augmented its knowledge base and assistant capabilities, with more enhancements planned.

WHO’S IT FOR

Decision-Makers and End Users

These professionals want to leverage AI in a self-service way to get insights and answers that will help them make the best predictions and decisions for their area(s) of responsibility.

AI Advistory and Governance

In order to continue to develop innovative AI products and capabilities — and to ensure we do so with ethical integrity — we have put in place a rich ecosystem of AI expertise to help steer our strategy and direction. Above all, we are deeply committed to the responsible development and deployment of our technology in ways that earn and maintain people’s trust.

Principles for Responsible AI

We have created a set of principles guiding the responsible development and deployment of our technology, available publicly at qlik.com/Trust/AI. These principles are:

Reliability: We design our products for high performance and availability so customers can safely and securely integrate and analyze data and use it to make informed decisions.

Customer control: We believe customers should always remain in control of their data and how their data is used so we design our products with finegrain security controls, including down to the row (data) and object level.

Transparency and explainability: We design our products to make it clear when customers engage with AI. We strive to make clear the data, analysis, limitations, and/or model used to generate AI-driven answers so our customers can make informed decisions on how they use our technology

Observability: We design our products so customers can understand lineage, access, and governance of data, analytics, and AI models used to inform answers and automate tasks.

Inclusive: We believe diversity, equity, inclusion, and belonging drive innovation and will continue to foster these beliefs through our product design and development.

Qlik has a process and staff in place to monitor for any upcoming legislation that would impact our business, such as new AI laws. As legislative changes occur, we assess these laws and adjust our AI compliance program accordingly.

Next Steps

For more information or enquiries about Qlik products and services, feel free to contact us below.

Customer Story (Data Integration) — Vale

Vale achieves yearly benefit of $600m

“Everybody’s in the same place. They can talk to each other and see the same information on different dashboards updated in near real time. That’s the kind of interaction Qlik is enabling.”

Jordana Reis, Enterprise Integration Architect, Vale S.A.

Solution Overview

Customer Name

Vale S.A.

Industry

Mining

Geography

Brazil

Function

Sales, Supply Chain Management

Business Value Driver

New Business Opportunities, Reimagined Processes

Challenges

Improve visibility across previously manual and disconnected processes
Deliver near real-time access to critical
business information
Enable staff across different functions to carry out integrated planning

Solution

Using Qlik Data Integration to handle and automate ETL processes, Vale developed the Integrated Operations Center to provide a clear overview of the supply chain.

Results

Qlik Data Integration enables low latency ETL processes and ease of use
Business benefits topped $300 million after just one month of operation
Staff can now build their own custom dashboards in minutes

An end-to-end industry giant

Brazil’s primary economic sector comprises critical industries such as agriculture, forestry and mining, all of which act as key sources of food, fuel and raw materials. Business units range in size from subsistence smallholdings to global giants with worldwide operations. And at the apex of the mining industry sits Vale S.A.

Founded 80 years ago, the Brazil-based metals and mining corporation is the world’s largest producer of iron ore and nickel. Vale is also the most valuable business in Latin America, with an estimated market value of $111 billion and rising, and a presence in 30 countries.

While mining remains the core of its business, Vale’s operations also encompass logistics, including an extensive network of railroads, ports and blending terminals, and shipping which distributes the company’s products across the world. Also supporting its operations are Vale’s own power plants and iron pelletizing facilities.

Vale’s dry bulk supply chain is also a large-scale service, and one of the biggest transport and distribution operations in Brazil. Vale owns around 490 locomotives and more than 29,500 rail freight cars, and ships much of its iron ore and pellet output from Brazil, around the African coast, to China and Malaysia, often in its own or chartered vessels, including Very Large Ore Carriers (VLOCs).

Long distances and complex processes

Managing Vale’s global operation involves a series of complex and resource-intensive distribution processes. These were placed into sharp focus in 2015 when the business faced falling commodities prices and an increasingly competitive market.

“The geographic distances we cover, from the extraction of iron ore to delivery to customers, are very long,” says Jordana Reis, Enterprise Integration Architect at Vale. “That becomes an even bigger issue when our main competitors are closer to our buyers than we are.”

Vale’s operations were managed by a series of manual and largely disconnected processes, with different departments handling their own functions and using their own methodologies, often with legacy systems. “There were people looking at the mining aspect, people looking at ports, people looking at sales, but we didn’t have an integrated view of these operations,” explains
Richardson Nascimento, Data and AI Architect at Vale. “That was the process we needed to fix.”

This lack of an integrated view of the business was causing a range of challenges, including mismatches between production and transport capacity, logistical inefficiency and product quality management issues. “We were also missing out on valuable sales opportunities, simply because we didn’t know if we could fulfill them,” recalls Reis.

New ETL processes accelerate insight

Vale developed the Centro de Operações Integradas (Integrated Operations Center, or COI) as an operating model. One of its pillars is to provide a means of aggregating and processing the vast amounts of data it was generating but only partially using. The COI would then act as a central framework, updated in near real time, on which Vale could base decisions, better manage its production and supply chain and support its people and processes.

“When we realized how much data we would need to move to really enable COI, we started thinking about how we could automate the process,” says Nascimento. “The main driver was low latency replication. We had a target to move all this information in less than 15 minutes, and Qlik Data Integration was clearly the best option.”

Vale collaborated closely with both Microsoft and Qlik teams during the purchase process. “Both teams were very active and interested in making COI happen,” says Reis. “They gave us honest opinions and helped us to achieve our goals.”

COI uses Qlik Replicate IaaS with Microsoft Azure in tandem with a range of data repositories such as Azure SQL Database and Azure Synapse, with Qlik Replicate acting as the principal enabler of the process. Another key factor in the choice of Qlik Data Integration was agentless operation, and its efficiency in reading application databases and transaction logs without impacting their activity.

COI’s main data sources are Vale’s in-house Manufacturing Execution Systems (MES), responsible for each stage of the value chain (Mining, Rail, Ports and Pelletizing), all based on Oracle databases; the chartering system Softmar and VesselOps,
based on SQL Server; and Vale’s in-house value chain optimization systems, also based on Oracle databases.

Nascimento also points to Qlik Data Integration’s importance in supporting tools such as Azure Databricks as part of Vale’s strategy to use machine learning and artificial intelligence to augment human decisions. Vale is using several tools for big data processing, such as Azure Machine Learning. “That’s one of the tools that we’re trying to leverage more,” he notes. “Azure Machine Learning is simple to use and easy to teach.”

Importantly, Reis highlights Qlik’s ease of use and speed of implementation and operation. “It changed our extract, transform and load (ETL) process and how we make data available,” she notes. “We reduced the effort to make data available to build less complex dashboards, for instance, from four weeks to just four hours.”

Velocity and visibility of information

COI began to deliver benefits almost immediately on its launch in 2017. It enabled a new integrated planning process, giving staff across the business full visibility into the supply chain improving the ability to manage their respective operations
in a collaborative environment.

“Everything related to operations is now under COI’s umbrella,” says Nascimento. “It covers the mines, the ports, railroads, shipping and sales and freight negotiations. COI enables planning and optimization across the supply chain.”

Users can now define and build their own dashboards, while corporate dashboards also enable insights and support decisions at board level. COI’s value is neatly encapsulated in Vale’s videowalls, giant room-sized panels featuring custom dashboards that enable cross-functional collaboration. “Everybody’s in the same place,” says Reis.

“They can talk to each other and see the same information on different dashboards updated in near real time. That’s the kind of interaction Qlik is enabling.”

Nascimento also highlights Vale’s asset monitoring center, which uses a similar and connected operating model to COI that combines with other tools to provide insights into asset lifecycles, enabling preventive maintenance and extending the efficiency and working lives of machinery, plant, vehicles and more.

“It’s not just about the speed of the decisions, but that we can make different types of decisions,” Nascimento explains. “We can now adjust production in line with logistical capacities, for example. And that’s transformational.”

Multi-million dollar savings

The initial launch of COI in 2017 delivered staggering results almost immediately, enabling business benefits in terms of sales won, costs saved and efficiencies gained totaling $300 million after just one month of operation and $600 million annual savings.

This, however, is just the start. COI is what Reis describes as “a lighthouse project”, with the data architecture implemented by the Integrated Operations Center and enabled by Qlik now used across multiple other projects covering areas such as safety,
geotechnical methods and autonomous machinery.

“Our long-term strategy is based on Qlik and Microsoft Azure. Once we saw the benefits on COI, we set Qlik Data Integration as our target information integration architecture for the whole enterprise,” concludes Reis. “We also have a program to migrate as many systems as possible to Microsoft Azure, including our data repositories for analytics. And of course, we will use Qlik Data Integration and Qlik Compose there too.”

Next Steps

For more information or enquiries about Qlik products and services, feel free to contact us below.

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Predictive and Prescriptive Analytics

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Inventory Management

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Tools for Effective Retail Analytics

Point of Sale (POS) Systems

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Business Intelligence Tools

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Integrating Multiple Data Sources

Prioritizing Key Performance Indicators (KPIs)

Utilizing Advanced Analytics Solutions

Future Trends in Retail Analytics

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