The Analytics Times

Data Drives Business

Data Integration Considerations for ISVs and Data Providers

Real-Time Data and AI Drive Businesses Today

Data is an extremely valuable asset to almost every organization, and it informs nearly every decision an enterprise makes. It can be used to make better decisions at almost every level of the enterprise—and to make them more quickly. But to take full advantage of the data and to
do so quickly requires artificial intelligence (AI). So, it is no surprise that nearly all participants in our research (87%) report that they have enabled or piloted AI features in analytics and business intelligence applications. Today, data is collected in more ways and from more
devices and more frequently than ever before. It can enable new methods of doing business and can even create new sources of revenue. In fact, the data and analyses themselves can be a new source of revenue.

Independent software vendors (ISVs) and data providers understand the importance of data in AI-based processes, and they are designing products and services to help enterprises step in and harness all this data and AI-generated business energy. To maximize the opportunities,
ISVs and data providers need to recognize that enterprises use various types of data, including data from both internal and external
sources. In fact, our research shows that the majority of enterprises (56%) are working with 11 or more sources of data. Governing the various data sources becomes critical because poor quality data leads to poor AI models. Our research shows the top benefit of investing in data governance, reported by three-quarters of participants (77%), is improved data quality.

Real-Time Data and AI Drive Businesses Today

The most common types of collected data include transactional, financial, customer, IT systems, employee, call center, and supply
chain. But there are other sources as well, many external to the enterprise. Nine in 10 enterprises (90%) are working with at least one
source of external data, which could mean location data, economic data, social media, market data, consumer demographics government data, and weather data. To be useful, all of that must be integrated.

“Data integration” is the process of bringing together information from various sources across an enterprise to provide a complete, accurate, and real-time set of data that can support
operational processes and decision-making. But nearly one-third of enterprises (31%) report that it is hard to access their data sources, and more than two-thirds (69%) report that preparing their data is the activity where they spend the most time in their analytics
processes. The process of data integration often places a burden on the operational systems upon which enterprises rely.

At the same time, enterprises also need to be able to integrate applications into their data processes. ISVs and data providers must bring data together with applications so it is easier for enterprises to access and use the very data they provide.

Data Integration Is Not Easy

Simple linkages such as open database connectivity and Java database connectivity (ODBC/JDBC), or even custom-coded scripts, are not sufficient for data integration. While ODBC/JDBC can provide the necessary “plumbing” to access many different data sources, it offers little assistance to application developers in creating agile data pipelines. Simple connectivity also does nothing to assist with consolidating or transforming data to make it ready for analytics, for instance, in a star schema. Nor does simple connectivity provide any assistance in dealing with slowly changing dimensions which must be tracked for many types of AI analyses.

Simple connectivity does little to help enterprises transform the data to ensure its standardization or quality. Data from various sources often contains inconsistencies, for instance in customer reference numbers or product codes. Accurate analyses require that these inconsistencies be resolved as the data is integrated. Similarly, data quality is an issue that must be addressed as the data is integrated. Our research shows these two issues of data quality and consistency are the second most common time sinks in the analytics process.

Nor does simple database connectivity help enterprises effectively integrate data from files, applications or application programming interfaces (APIs). With the proliferation of cloudbased applications, many of which only provide API access, ODBC/JDBC connectivity may not be an option. And many enterprises still need to process flat files of data, as our research shows that these types of files are the second most common source of data for analytics.

Data integration is not a one-time activity, either. It requires the establishment of data pipelines that regularly collect and consolidate
updated data. A greater infrastructure is needed around these pipelines to ensure that they run properly and to completion. ISVs and data providers that rely only on simple connectors must create and maintain this extra infrastructure themselves.

Those data pipelines also need to be agile enough to support a variety of styles of integration. Batch updates are still useful for bulk transfers of data, but other more frequent styles of updating are needed as well. Our research shows that nearly one-quarter of enterprises (22%) need to analyze data in real time. Since the most common sources of information are transactional and operational applications, it is important to create pipelines that can access this data as it is generated. Incremental updates and change data capture (CDC) technology can solve this problem and these are becoming competitive necessities.

Real-time requirements are even more demanding when we consider event data, where nearly one-half (47%) of enterprises process it within seconds. Then, as applications and organizational requirements change, the data pipelines must reflect those changes. Therefore, the tools used to support such a wide variety of ever-changing sources need to be open enough to be easily incorporated into a wide variety of processes.

But if ISVs and data providers focus their energies on maintaining data pipelines, it distracts resources from the core business. Creating data pipeline infrastructure that is highly performant and efficient requires years of engineering. Simple bulk movement of entire data sets is slow and inefficient, even though it may be necessary for initial data transfers. Subsequent data transfers, however, should use a data replication scheme or CDC approach, creating much smaller data transfers and much faster processes.

Advantages of a Modern Data Fabric

A modern data fabric is based on a cloud-native architecture and includes orchestration and automation capabilities that enhance the design and execution of data pipelines that consolidate information from across the enterprise. As data becomes a new source of revenue, sometimes referred to as “data as a product,” a modern data fabric must also enable easy access to, and consumption of, data. A key component to delivering data in this fashion is strong data catalog capabilities. AI assisted search, automated profiling and tagging of data sources, and tracking the lineage of that data through its entire life cycle make it easier to find and understand the data needed for particular operations and analyses. Collecting and sharing this metadata in a data catalog not only provides better understanding and access to the data, but also improves data governance. Our research shows that enterprises that have adequate data catalog technology are three times more likely to be satisfied with their analytics and have achieved greater rates of self-service analytics.

Orchestration and access via APIs are also critical to ISVs and data providers as these allow the remote invocation of data pipelines needed for the coordination and synchronization of various interrelated application processes, even when they are distributed across different cloud applications and services. These APIs need to span all aspects from provisioning to core functionality for orchestration to be effective. Automation of these orchestration tasks can enhance many aspects of data pipelines to make them both more efficient and more agile.
Automated data mapping, automated meta data creation and management, schema evolution, automated data mart creation, and data warehouse and data lake automation can quickly and efficiently create analytics-ready data. When combined with orchestration, automation can also provide “reverse integration” to update data in source systems when necessary and appropriate.

Modern data integration platforms employ AI/ML to streamline and improve data processing. AI/ML can be used to automatically detect anomalies in data pipelines, such as whether the pipelines suddenly processed an unusually small number of records. Such an anomaly could indicate a problem somewhere else in the pipeline. AI/ML can also be used to automatically deal with errors in pipelines and routine changes, such as those in the sources or targets. AI/ML can also determine the optimal execution of pipelines, including the number of instances to create or where different portions of the pipeline should be processed. AI/ML can be used to enrich data with predictions, scoring or classifications that help support more accurate decision-making. We assert that by 2027, three-quarters of all data processes will use AI and ML to accelerate the realization
of value from the data.

Modern data integration platforms must also incorporate all appropriate capabilities for data governance. Data sovereignty issues may require that data pipelines be executed only within certain geographies. Compliance with internal or regulatory policies may require single sign-on or the use of additional credentials to appropriately track and govern data access and use. Therefore, a platform with built-in capabilities for governance can help identify personally identifiable information and other sensitive or regulated data. But implementing any of these modern data integration platform requirements can impose a significant burden on ISVs and data providers.

Illustrative Use Cases

Product Distributors

For organizations with hundreds of thousands of SKUs and hundreds of thousands of customers, managing orders and inventories can be a time consuming process. Using a modern data-as-a-product approach with standardized data governance and a centralized data catalog can reduce costs dramatically and enable self-service online ordering. This approach also creates more agility to meet customer needs and provides better, more timely visibility into operations.

Insurance Industry

Insurance technology data providers can use data integration to help their customers be more competitive by providing access to up-to-date information that enables online quotes. Data is the key to the accurate pricing of insurance liabilities, and many of the sources and targets exist in the cloud, but they require support for a variety of endpoints. By using CDC-based replication, however, both claims and market data can be collected, consolidated, and distributed within minutes. As a result, millions of quotes can be generated each day where each incorporates real-time analysis of vast volumes of data.

Other Applications

Data integration can be the key to many other ISVs and data providers. Mobile application providers can integrate location data with transaction data to provide broader market data on consumer behavior. Talent management ISVs can integrate data relating to internal performance and compensation with external market data to improve employee acquisition and retention. Foreclosure data can be collected, consolidated, and distributed to support loan origination and servicing operations. Vendor data can be collected and provided to improve procurement processes augmenting supplier performance analyses with risk, diversity, sustainability and credit scores. And regardless of the vertical industry or line-of-business function, faster access to more data generally produces better results.

Other Considerations

Once data is integrated, it can provide the basis for a broad range of analytics and AI. By supporting these analyses and data science, ISVs and data providers can extend the value of their capabilities and therefore increase their revenue opportunities. Choosing a data integration platform that also supports analytics and AI will make it easier for enterprises to capture this revenue. In fact, our research shows that reports and dashboards are the most common types of analytics used by more than 80% of enterprises. However, when considering analytics providers, look at those that support other newer techniques as well, such as AI/ML and natural language processing, which are projected to be required by 80% of enterprises in the future.

Enterprises need to use data to help drive actions. Data can help them understand what has happened and why, but they ultimately need to process what they have learned and then take action. In many situations, however, there is simply no time to review data to determine what
course of action to take. ISVs and data providers can help their customers derive more value from data by using real-time information to trigger the appropriate actions.

ISVs and data providers are using technology to add value to business processes. While all business processes typically require data, data integration itself is merely a means to the end. If the process is not done properly, it can detract from the overall approach, so it requires careful design and development. Enterprises should ideally spend their time on core competencies, not on developing data integration technology. By using a full-featured, purpose-built data integration platform, they can ensure that the data needed by ISVs and data providers is robust and available in a timely manner.

Next Steps

Explore all available data sources, along with their accessibility, that can boost the value of your services.
Recognize the value of data catalog and data governance in enabling data-as-a-product.
Consider platforms that go beyond simple connections to data sources and that minimize the amount of development and maintenance work required.
To maximize performance and minimize the impact on production systems, create repeatable and agile pipelines that operate efficiently.
Look for platforms with significant automation capabilities to maximize productivity and responsiveness.
Ensure that your architecture provides a modern, cloud-native approach.

Move From Worksheets to Workflows with Automation

How 5 Companies Automate Data Prep and Analytics

For business analysts who spend their days mired in tedious data tasks, the struggle is real. Manually cleansing, blending and analyzing a growing volume of complex data is taking more time than ever. While spreadsheets are useful for basic tasks, when data gets messy or large, they quickly become slow and cumbersome to work with — not to mention prone to errors. That’s why more organizations are arming their analysts with easy-to-use automated solutions that enable them to deliver fast, accurate, data-driven insights and eliminate the burden of time consuming manual processes.

In the following real-world customer stories, you’ll learn how analysts and business professionals use Alteryx to simplify and automate complex analytical processes with an intuitive drag and-drop interface and built-in AI-guidance. Read how they are saving time, achieving bottom line results, and adding more value using their business expertise combined with advanced analysis. Plus, learn about an opportunity to simulate AI-powered solutions with your own use case.

DoorDash is the largest food delivery service in the U.S., supporting hundreds of thousands of merchants and millions of customers in more than 500 cities across North America.

Business Challenge

The accounting team at DoorDash was dealing with a growing volume of complex data with mounting pressure to speed up processes. They also had to meet the rigorous standards of SOX compliance.

Analysts relied on manual processes and spreadsheets to collect, reconcile, and analyze massive amounts of data — a time-consuming process that was prone to errors.

Alteryx Solution

DoorDash uses Alteryx to automate and streamline operational processes, data acquisition, and in-depth financial analysis. By replacing manual processes with easy to-build automated workflows, the finance team now saves 25,000 hours annually.

The end-to-end automation solution also frees up financial analysts to focus on value-added, strategic tasks that drive more accurate accounting.

Results

Mayborn Group reduces manual processes by 90% and optimizes product promotion offers using Alteryx.

Baker Tilly is a top ten accounting firm that offers specialized federal tax compliance and planning expertise to help businesses optimize value while
minimizing their tax burden.

Business Challenge

The tax team responsible for unclaimed property reporting had to collect and process hundreds of thousands of files with up to a million lines or records of data — all coming from multiple disparate sources.

The process of collecting, cleaning, and analyzing the massive files took anywhere from several days to weeks to complete. There was also a risk of exposure for the client if any amount of unclaimed property was missed due to human error.

Alteryx Solution

Baker Tilly now uses Alteryx to automate data prep, processing, and reporting. Non-technical teams in the unclaimed property department were able to build their own workflows to consolidate files and apply analytics, reducing the time spent preparing deliverables by 50%.

By upskilling domain subject matter experts in easy-to-use analytics, they
eliminated the need to rely on technical experts for dashboard and report building.

Results

Automating data prep and analytics with Alteryx reduced reporting processing time by 50% and decreased regulatory errors by 70%.

Bank of America a multinational investment bank and financial services company serving approximately 56 million U.S. consumer and small business relationships.

Business Challenge

The enterprise testing team at Bank of America must ensure that all regulators are notified of any applicable transactions. The team was manually prepping and cleansing tens of millions of transactions for quality assurance every day.

The entire process, from the time of the transactions to the moment the
regulators were notified, took about two months. The delayed response left the organization susceptible to costly regulatory fines.

Alteryx Solution

Bank of America added Alteryx to its data stack of Tableau, Qlik, and MicroStrategy to create a streamlined workflow that alerts the testing team when they need to take corrective action on any regulatory measures.

Using Alteryx, the quality assurance process has transformed from reactive to proactive, with the ability to address issues as they occur, rather than waiting two months for the results. The testing team can also easily share the reports for regulatory transparency

Results

Automated, real-time data prep with Alteryx reduces quality assurance processing time by 60 days at Bank of America.

Located in 90 countries, Siemens Energy operates across the energy landscape, from conventional and renewable power to grid technology and electrifying complex industrial processes

Business Challenge

The transmission unit at Siemens struggled to efficiently collect and analyze production, logistics and financial data from 36 factories worldwide. Analysts were tied up in spreadsheets for hours, consolidating, validating, and prepping data.

The time-consuming manual processes prevented the organization from
achieving full value from its data and kept analysts from spending time on higher impact, strategic initiatives.

Alteryx Solution

Siemens adopted Alteryx to automate and scale large and complex data projects. With intuitive, drag-and-drop features, domain experts could build their own workflows and share insights with ease.

In less than six months, the team created 350 automated workflows in Alteryx and saved thousands of hours eliminating manual processes. Alteryx users at Siemens are also helping drive a wider culture of analytics across the organization

Results

Using Alteryx, analysts at Siemens built 350 automated processes and saved thousands of hours.

Mayborn Group is an award-winning retail brand that produces a broad range of baby products available in 60 countries worldwide.

Business Challenge

As a global brand with hundreds of products and retailers, Mayborn had data coming in from more than 100 internal and external sources. Consolidating the data for a holistic view to understand market and customer behavior at a regional and retailer level was a significant challenge.

Analysts had to manually merge and process data by individual retailers, region by region, with very specific views in isolation of one another. The analytics team found it impossible to scale the process efficiently with manual processes.

Alteryx Solution

Mayborn uses Alteryx to automate the process of blending and analyzing disparate data sets. Now, they have a centralized, global view of point-of-sale data that allows them to better focus on product quality and competitive strategies.

The time saved using Alteryx allows the analytics team to focus on strategic initiatives including product promotions. They realized significant ROI by using Alteryx to analyze and optimize promotional offers that increased sales.

Results

Mayborn Group reduces manual processes by 90% and optimizes product promotion offers using Alteryx.

Suite of Analytics Solutions for Active Ageing Centres

Embrace Transformation for Enhanced Care

“To strengthen support for seniors in the community, we will need to raise the capabilities of our health and social ecosystems. Digitalization of the Community Care sector will be a key pillar in this effort” – Mr. Ng How Yue, Permanent Secretary, Ministry of Health

Active Aging Centres (AACs) across Singapore play a crucial role in supporting our senior community. However, resource and manpower challenges have long been an issue, and these will become more pressing as Singapore’s population rapidly ages by 2030.

This is where technologies can empower AACs to become data-driven-ready, ensuring that operations and resources are always optimized. By leveraging AI and other advanced technologies, AACs can make data-driven decisions to enhance their services.

SIFT has been assisting in transforming the social service sector, and we invite AAC leaders to connect with us for ideas on how to integrate digitalization into their organizations. You will gain insights from real use cases, and we can help you develop a strategic digitalization roadmap that provides clarity and aligns with your organizational goals.

Client Challenges

SIFT Analytics Group has designed a customized analytics solution for a non-profit social service organization seeking to optimize manpower management and streamline daily activities. This solution consolidates operational data with real-time activity tracking, offering a centralized platform for efficient management of both staff and volunteers. In addition to improving operational efficiency, it monitors costs, revenue, and other key metrics, empowering the organization to make data-driven decisions that enhance overall service delivery.

Demographic Insights

Age Distribution: Analyzing the age range of participants to tailor programs accordingly (e.g., physical activities for 60-70, cognitive programs for 70+).

Health Status: Tracking common health conditions and mobility levels to design appropriate fitness, wellness, or mental health programs.

Program Participation

Attendance Tracking: Analyzing which activities (e.g., fitness classes, social events, workshops) are most popular.

Engagement Trends: Identifying times of day or year when attendance peaks, helping to plan more effective schedules.

Program Outcomes: Evaluating the effectiveness of health, social, or learning programs by measuring improvements in participants’ well-being.

Volunteer and Staff Contributions

Volunteer Hours and Activities: Tracking the number of hours worked by volunteers and the areas in which they contribute.

Staff-to-Participant Ratio: Ensuring sufficient staff or volunteer resources are available for high-demand activities.

Health & Wellness Outcomes

Physical Health Metrics: Monitoring fitness assessments, mobility, or vital health indicators (e.g., heart rate, blood pressure) before and after participation.

Mental Health & Cognitive Function: Surveying participants’ cognitive function or emotional well-being over time.

Social Impact

Social Engagement: Measuring increases in social interactions and community integration through surveys or participation in group activities.

Resource Allocation

Cost per Participant: Analyzing the cost of delivering each program or service to ensure resources are being used effectively.

Funding Sources & Impact: Connecting donor funding or grants to specific initiatives and their outcomes.

Satisfaction and Feedback

Participant Feedback: Regularly surveying participants to gauge satisfaction with programs, facilities, and services.

Program Improvement: Using feedback to make data-driven decisions on improving existing offerings or introducing new activities.

What is Your Data Strategy?

Is your data strategy ready to drive growth and innovation?

What is Your Data Strategy:

A data strategy is more than just a roadmap—it’s a strategic framework for navigating the complexities of data management, governance, and analytics. It outlines how your organization collects, stores, processes, and leverages data to achieve key business objectives.

In an era where advanced analytics and AI are reshaping industries, a robust data strategy is essential to staying ahead of the competition.

Building a Comprehensive and Advanced Data Strategy

The foundation of an advanced data strategy starts with aligning your data initiatives to business outcomes. Whether you aim to enhance customer engagement, drive innovation, or optimize efficiencies, your strategy must be tailored to those specific goals. It involves evaluating the current data landscape, identifying gaps such as data silos, poor data integration, or suboptimal quality, and establishing a clear path to address these challenges.

At the core of a high-performing data strategy are advanced components like data governance, ensuring data quality, security, and regulatory compliance, and data architecture that supports scalability, agility, and real-time analytics. The integration of cutting-edge technologies such as AI, machine learning, and predictive analytics can propel your data strategy to new heights, enabling deeper insights and more informed decision-making.

The Role of Advanced Technologies

As the demand for real-time insights grows, modern data strategies must incorporate advanced tools and technologies that facilitate data processing at scale. SIFT Analytics leverages state-of-the-art platforms and solutions, enabling organizations to integrate machine learning and AI to extract powerful insights from large datasets, predict trends, and drive data-driven innovation.

Consult SIFT for Your Data Strategy

SIFT Analytics provides end-to-end expertise in designing advanced data strategies, ensuring your data architecture is optimized for both current needs and future growth. With their scalable, flexible solutions, SIFT empowers organizations to move beyond traditional analytics and unlock the full potential of their data ecosystem.

👉 Consult SIFT

What is Automation Analytics

Explore how automation can not only streamline your data workflows but also empower your organization to harness the true potential of data-driven decision-making.

What is Automation Analytics :

Automation Analytics is about using technology to perform data-related tasks without human intervention. It streamlines and automates repetitive data processes to improve efficiency and accuracy. In a world where businesses are generating and handling vast amounts of data, data automation is becoming increasingly essential.

Data automation involves using software tools and algorithms to automate tasks such as data collection, cleaning, transformation, and analysis. This not only saves time but also reduces the risk of errors associated with manual data handling. For instance, instead of manually collecting data from different sources and entering it into a database, businesses can use data automation tools to automate this process, ensuring accurate and real-time data collection.

Automation Analytics Key Benefits

One key benefit of Automation Analytics is improved efficiency. By automating repetitive tasks, businesses can free up valuable time and resources, allowing employees to focus on more strategic tasks. For example, instead of spending hours manually cleaning and transforming data, employees can focus on analyzing the data and generating insights that drive business decisions.

Automation Analytics also improves accuracy. Manual data handling is prone to errors, which can lead to inaccurate analysis and misguided decisions. Automating these processes ensures that data is handled accurately and consistently, leading to more reliable insights and better decision-making.

Another benefit of Automation Analytics is scalability. As businesses grow and generate more data, manually handling data becomes increasingly challenging. Data automation tools can handle large volumes of data, ensuring that data processes can scale with the business. This is particularly important in today’s data-driven world, where businesses need to handle and analyze vast amounts of data to stay competitive.

Automation Analytics Challenges

Implementing Automation Analytics is not without challenges. It requires robust infrastructure, advanced tools, and technical expertise. Poor implementation can lead to unreliable automation processes and inaccurate data handling. There’s also the challenge of integrating automation tools with existing systems and processes.

Automation Analytics is essential for businesses looking to improve efficiency, accuracy, and scalability in their data processes. By automating repetitive tasks, businesses can free up valuable time and resources, ensuring accurate and consistent data handling. With SIFT Analytics, businesses can effectively implement and leverage data automation, driving innovation and staying ahead of the competition.

Ask SIFT on Automation Analytics

SIFT Analytics helps businesses navigate these challenges by providing expertise in implementing automation tools and integrating them with existing systems. They also offer training and support to ensure businesses can effectively manage and maintain their automated data processes. Their solutions are designed to be scalable and flexible, accommodating the evolving needs of the business.

👉 Consult SIFT

What is Machine Learning and its challenges

Machine Learning learn from data to make predictions or decisions. The challenge? It is the need for high-quality data.

What is Machine Learning:

Let’s dive into machine learning, a fascinating branch of artificial intelligence. Imagine teaching computers to learn from experience, just like humans do. This is what machine learning is all about—training systems to understand patterns in data and make decisions based on that information. Whether it’s recommending movies based on your viewing history or diagnosing diseases from medical images, machine learning has a vast array of applications.

Improving Operations with Machine Learning

Think about how, when you order a product online and it arrives at your doorstep the next day, that’s supply chain efficiency at work, enhanced by machine learning capabilities. It involves a network of processes and resources coordinating seamlessly to ensure timely delivery. Machine learning algorithms analyze vast amounts of data to predict demand, optimize inventory levels, and streamline logistics.

The core of a successful supply chain now lies in its ability to leverage these advanced technologies to manage and optimize the flow of goods, information, and finances from the supplier to the customer, all without a hitch.In the business world, machine learning is revolutionizing operations. For instance, a retail company can analyze customer purchase histories to predict demand, optimize inventory, and personalize marketing campaigns. This level of insight, once unimaginable, is now a reality.

Moreover, machine learning can uncover insights that might be missed by human analysts. It can analyze customer feedback in real-time, identifying common issues and suggesting improvements faster than traditional methods. This ability to process and analyze vast amounts of data quickly is transforming industries across the board.

Machine Learning Challenges

Machine learning comes with its challenges. High-quality data is crucial; poor data quality can lead to inaccurate models and unreliable predictions. There’s also the issue of interpretability—understanding how a machine learning model makes its decisions is essential, especially in areas like healthcare or finance.

Machine learning is not a magic bullet, but it is a powerful tool that can drive significant value when used correctly. By enhancing human capabilities, it allows businesses to make smarter, faster decisions. With SIFT Analytics, companies can harness the power of machine learning to stay ahead of the competition.

AskSIFT on Improve Your Machine Learning Model

SIFT Analytics can help businesses navigate these challenges. With expertise in data collection, preparation, and model training, they ensure that the machine learning models are built on solid foundations. They also offer tools to interpret and explain model decisions, providing the transparency businesses need.

👉 Consult SIFT

Enhance Data Quality and Consistency

Improving data quality is essential for making informed decisions, optimizing operational efficiency, and increasing customer satisfaction.

How Organizations Improve Data Quality and Consistency:

In today’s digital era, businesses are awash with data. From customer transactions to social media interactions, data streams from all directions. Yet, not all data is created equal. Ensuring data quality and consistency is essential for informed decision-making and business success. Here’s a detailed guide on mastering this crucial aspect of data management.

Understanding Data Quality

Data quality is about more than just having data—it’s about having data that is accurate, complete, reliable, and relevant. High-quality data is the bedrock of meaningful analysis and insights. To ensure data quality, businesses need to focus on several key dimensions.

Accuracy is paramount. Data must accurately represent real-world values, as inaccuracies can lead to erroneous conclusions and misguided strategies. Completeness is equally critical; missing data can skew analyses and lead to incorrect insights. Reliability is another vital aspect—data should be consistent across different systems and over time, building trust in your analytics.

Lastly, relevance cannot be overlooked. Data must be pertinent to the business context, as irrelevant data can clutter systems and divert attention from critical insights.

Steps to Ensure Data Quality

The first step in ensuring data quality is data profiling and assessment. Start by profiling your data to understand its current state and assess it for accuracy, completeness, and consistency. This initial evaluation helps identify areas needing improvement.

Next is data cleansing. This involves correcting inaccuracies, filling in missing values, and removing duplicates. Implementing automated tools can streamline this process, ensuring ongoing data quality. Following cleansing, standardization is crucial. Data formats, definitions, and naming conventions should be standardized across the organization to eliminate discrepancies and ensure consistency.

Data validation is another essential step. Implementing validation rules during data entry and processing can maintain data integrity by enforcing constraints such as mandatory fields, data type restrictions, and value ranges.

To oversee these efforts, establish a data governance framework. Define roles and responsibilities for data stewardship, and create policies for data management. Governance ensures accountability and adherence to data quality standards

Maintaining Data Consistency

Consistency in data means maintaining uniformity across different systems and over time. Inconsistent data can lead to conflicting reports and hinder decision-making. Integrating data from various sources into a centralized system is crucial. Using ETL (Extract, Transform, Load) processes can harmonize data and ensure consistency during integration.

Implementing Master Data Management (MDM) is another critical step. MDM creates a single source of truth for critical business data, maintaining consistent data across the organization by synchronizing updates and resolving conflicts.

Regular audits and monitoring are also vital. Conducting regular audits can identify and rectify inconsistencies. Automated monitoring tools can detect anomalies, ensuring ongoing data integrity. Additionally, implementing version control for datasets tracks changes and maintains historical records, helping understand data evolution and resolve discrepancies.

Training and awareness play a significant role in maintaining data quality and consistency. Educating employees about the importance of data management best practices and tools fosters a culture of data stewardship across the organization.

Fostering Best Practices and Culture

Ensuring data quality and consistency is not a one-time task but an ongoing commitment. By profiling and cleansing data, standardizing processes, implementing robust validation, and maintaining strong governance, businesses can achieve high data quality. Additionally, integrating data, managing master data, conducting regular audits, and fostering a culture of data stewardship ensures consistent data.

With these practices in place, organizations can harness the power of their data to drive informed decisions, enhance operational efficiency, and gain a competitive edge in the market.

Plan Your Data Integration Strategy

For a successful data integration strategy, you should clearly define the types of data integrated and who has access to them.

Plan Your Data Integration Strategy:

Businesses are generating an unprecedented amount of data. Yet, this data is often siloed across multiple platforms, making it challenging to extract actionable insights. Everyone needs a robust data integration strategy and here’s how to plan yours effectively.

If you do not want to go through this lengthy content, why not just #AskSIFT directly?

Understand Your Data Sources

First, identify all the data sources your organization relies on. This could range from CRM systems, social media analytics, ERP systems, to cloud storage services. Understanding where your data resides is the foundational step to creating a seamless integration strategy.

Define Your Objectives

What do you aim to achieve with data integration? Whether it’s enhancing customer insights, improving operational efficiency, or fostering innovation, clear objectives will guide your integration process. Setting specific goals helps in measuring the success of your strategy.

Choose the Right Tools

With numerous data integration tools available, selecting the right one can be overwhelming. Look for a tool that supports various data sources, ensures data quality, and offers real-time integration capabilities. A tool with a user-friendly interface and strong technical support can make a significant difference.

Ensure Data Quality and Consistency

Integrating data from multiple sources can lead to inconsistencies. Establish data governance protocols to maintain data quality. Implementing data validation and cleansing processes ensures that the integrated data is accurate and reliable.

Prioritize Data Security

Data security should be a top priority in your integration strategy. Ensure compliance with data protection regulations such as GDPR or CCPA. Use encryption, access controls, and regular audits to protect sensitive information from breaches.

Enable Real-Time Data Integration

In the age of instant information, real-time data integration is no longer a luxury but a necessity. Real-time integration allows businesses to respond swiftly to market changes, customer needs, and operational challenges. Choose solutions that offer real-time data synchronization to stay ahead of the curve.

Foster Collaboration

Data integration is not solely an IT initiative. It requires collaboration across departments to understand different data needs and workflows. Encourage a culture of data-sharing and cross-functional collaboration to maximize the benefits of your integrated data.

Monitor and Optimize

Once your data integration strategy is in place, continuous monitoring and optimization are essential. Regularly review the performance of your integration processes and tools. Identify areas for improvement and adapt to changing business needs to ensure long-term success.

#AskSIFT

A well-planned data integration strategy can transform your business by providing a holistic view of your operations, enhancing decision-making, and driving innovation. By understanding your data sources, defining clear objectives, choosing the right tools, ensuring data quality, prioritizing security, enabling real-time integration, fostering collaboration, and continuously optimizing, you can unlock the full potential of your data.

Common Challenges in Data Analytics

The Ultimate Guide by SIFT Analytics

Common Challenges in Data Analytics:

Why do data analytics initiatives succeed, and others fail? When deploying data analytics, it comes with numerous challenges that can hinder successful implementation and achieving goals. This guide simplifies these challenges and provides insights into overcoming them. However, if you are encountering a specific challenge that is not covered here, feel free to reach out to SIFT Analytics.

Challenges in Data Literacy

Data literacy involves understanding data sources, infrastructure, analytical methods, and the ability to describe scenarios and resulting business outcomes. Improving data literacy within an organization is crucial for effective data analytics. SIFT periodically conducts data literacy workshops that you can and should request to enhance your team’s skills.

👉 Request for a Data Literacy Workshop

Challenges in Technical Knowledge and Skills

Even with powerful no-code analytics tools designed for business users, some technical knowledge and skills are necessary. These tools enable users to focus on interpreting data, refining strategies, and making informed decisions critical to the business’s success. Continuous training and upskilling are essential to keep pace with evolving tools and technologies.

Challenges in Finding the Right Analytics Tools

Choosing the right data analytics tool is challenging as no single tool fits every need. Popular analytics tools vary, and selecting the right one involves assessing many factors in line with your organization’s needs.

So, how do you identify one that’s a good fit for your company? Start by considering your organization’s business needs and knowing who will be using this analytics tool. Will it be used by data scientists, data analysts, or by non-technical users who need an intuitive interface, or should it suit both kinds of users? Some platforms provide an interactive experience for iterating on code development while others focus more on point-and-click for less technical users.

Next, what are your goals and which stage are you at in the digital transformation journey? Are you considering building real-time analytics, or do you have many incoming data through different sources where you want what we call one source of truth, ensuring data quality, governance, or migrating your data from one platform to another or automation?

You see, there are many goals. Some platforms specialize in some areas, while others can deliver seamless end-to-end implementation. So you will have to do intensive research, and the best way to start finding information is by reaching out to consultancy like SIFT Analytics to guide and provide you with proven strategies and a roadmap.

Finally, consider price and licensing. If you need advice, #AskSIFT

Challenges in Advanced Analytics

Advanced analytics, including Artificial Intelligence (AI), Machine learning (ML), Business Process Automation, Data Integration and Migration, Data Mining, and more complex analysis.

There are powerful tools to deploy advanced analytics; however, there is a need for data managers and subject matter experts, and management to be very involved to ensure that the goals are aligned and the suitable strategy.

There are a few things to take care of before evaluating the available tools. You should first understand the types of data your enterprise wants to analyze and, by extension, your data integration requirements. In addition, before you can begin analyzing data, you’ll need to select data sources and the tables and columns within them and replicate them to a data warehouse to create a single source of truth for analytics.

You’ll want to assess data security and data governance as well. If data is shared between departments, for example, there should be access control and permission systems to protect sensitive information.

Challenges in Integrating Multiple Sources

Companies often have data in various systems (e.g., CRM, Excel, social media, SAP, POS). Manually combining this data into one source can be time-consuming and error-prone.

👉 Find out more about Data Integration here

Challenges in Data Quality Issues

Maintaining high data quality is crucial. Challenges include dealing with incomplete, inconsistent, or inaccurate data. Implementing robust data quality management processes and tools is essential for reliable analytics.

Challenges in Machine Learning

Machine learning involves selecting the right algorithms, handling large datasets, and ensuring model accuracy. Continuous monitoring and updating of models are necessary to adapt to new data and maintain performance.

Challenges in Data Security and Privacy

Ensuring data security and privacy is critical, especially with increasing regulatory requirements. Implementing strong security measures and compliance protocols is essential to protect sensitive data.

#AskSIFT

Need help with any of these challenges? Reach out to SIFT Analytics for expert guidance and solutions.

The Analytics Times

Data Drives Business

Data Integration Considerations for ISVs and Data Providers

Real-Time Data and AI Drive Businesses Today

Real-Time Data and AI Drive Businesses Today

Data Integration Is Not Easy

Advantages of a Modern Data Fabric

Illustrative Use Cases

Other Considerations

Next Steps

More Data-Related Topics That Might Interest You

The Analytics Times

Move From Worksheets to Workflows with Automation

How 5 Companies Automate Data Prep and Analytics

More Data-Related Topics That Might Interest You

High Volume Ticketing

Timeliness IT Services

Limited Reporting and Tracking

More Data-Related Topics That Might Interest You

The Analytics Times

Suite of Analytics Solutions for Active Ageing Centres

More Data-Related Topics That Might Interest You

The Analytics Times

What is Your Data Strategy?

What is Your Data Strategy:

Building a Comprehensive and Advanced Data Strategy

The Role of Advanced Technologies

Consult SIFT for Your Data Strategy

More Data-Related Topics That Might Interest You

The Analytics Times

What is Automation Analytics

What is Automation Analytics :

Automation Analytics Key Benefits

Automation Analytics Challenges

Ask SIFT on Automation Analytics

More Data-Related Topics That Might Interest You

The Analytics Times

What is Machine Learning and its challenges

What is Machine Learning:

Improving Operations with Machine Learning

Machine Learning Challenges

AskSIFT on Improve Your Machine Learning Model

More Data-Related Topics That Might Interest You

The Analytics Times

Enhance Data Quality and Consistency

How Organizations Improve Data Quality and Consistency:

Understanding Data Quality

Steps to Ensure Data Quality

Maintaining Data Consistency

Fostering Best Practices and Culture

More Data-Related Topics That Might Interest You

The Analytics Times

Plan Your Data Integration Strategy

Plan Your Data Integration Strategy: