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 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:
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
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:
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:
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
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:
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.
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.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
“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.
Customer Name
Vale S.A.
Industry
Mining
Geography
Brazil
Function
Sales, Supply Chain Management
Business Value Driver
New Business Opportunities, Reimagined Processes
Challenges
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
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).
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.
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.”
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.”
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.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
Being able to analyze high-volume, varied datasets is essential in nearly all industries. From fraud detection and real-time customer offers to market trend and pricing analysis, analytics use cases are boosting competitive advantage. In addition, the advent of the Internet of Things (IoT) and Artificial Intelligence (AI) are also driving up the volume and variety of data that organizations like yours want and need to analyze. The challenge: as the speed of business accelerates, data has increasingly perishable value. The solution: real-time data analysis.
Data lakes have emerged as an efficient and scalable platform for IT organizations to harness all types of data and enable analytics for data
scientists, analysts, and decision makers. But challenges remain. It’s been too hard to realize the expected returns on data lake investments, due to several key challenges in the data integration process ranging from traditional processes that are unable to adapt to changing platforms and data transfer bottlenecks to cumbersome manual scripting, lack of scalability, and the inability to quickly and easily extract source data.
Qlik®, which includes the former Attunity data integration portfolio, helps your enterprise overcome these obstacles with fully automated, high-performance, scalable, and universal data integration software.
Combining efficient distributed processing with cost-effective storage for mixed data sets analysis forever redefined the economics and possibilities of analytics. Data lakes were initially built on three pillars: the Hadoop foundation of MapReduce batch processing, the Hadoop Distributed File System (HDFS), and a “schema on read” approach that does not structure data until it’s analyzed. These pillars are evolving:
While the pillars are evolving, the fundamental premise of the data lake remains the same:
organizations can benefit from collecting, managing, and analyzing multi-sourced data on distributed commodity storage and processing resources.
As deployments proceed at enterprises across the globe, IT departments face consistent challenges when it comes to data integration. According to the TDWI survey (Data Lakes: Purposes, Practices, Patterns and Platforms), close to one third (32%) of respondents were concerned about their lack of data integration tools and related Hadoop programming skills.
Traditional data integration software tools are challenging, too, because they were designed last century for databases and data warehouses. They weren’t architected to meet the high-volume, real-time ingestion requirements of data lake, streaming, and cloud platforms. Many of these tools also use intrusive replication methods to capture transactional data, impacting production source workloads.
Often, these limitations lead to rollouts being delayed and analysts forced to work with stale and/or insufficient datasets. Organizations struggle to realize a return on their data lake investment. Join the most successful IT organizations in addressing these common data lake challenges by adopting the following five core architectural principles.
1. Plan on Changing Plans
Your architecture, which likely will include more than one data lake, must adapt to changing requirements. For example, a data lake might start out on premises and then be moved to the cloud or a hybrid environment. Alternatively, the data lake might need to run on Amazon Web Services, Microsoft Azure, or Google platforms to complement on-premises components.
To best handle constantly changing architectural options, you and your IT staff need platform flexibility. You need to be able to change sources and targets without a major retrofit of replication processes.
Qlik Replicate™ (formerly Attunity Replicate) meets these requirements with a 100% automated process for ingesting data from any major source (e.g., database, data warehouse, legacy/mainframe, etc.) into any major data lake based on HDFS or S3. Your DBAs and data architects can easily configure, manage, and monitor bulk or real-time data flows across all these environments.
You and your team also can publish live database transactions to messaging platforms such as Kafka, which often serves as a channel to data lakes and other Big Data targets. Whatever your source or target, our Qlik Replicate solution provides the same drag-and-drop configuration
process for data movement, with no need for ETL programming expertise.
Two Potential Data Pipelines — One CDC Solution
2. Architect for Data in Motion
For data lakes to support real- time analytics, your data ingestion capability must be designed to recognize different data types and multiple service-level agreements (SLAs). Some data might only require batch or microbatch processing, while other data requires stream processing tools or frameworks (i.e., to analyze data in motion). To support the complete range, your system must be designed to support technologies such as Apache Kafka, Amazon Kinesis, Azure Event Hubs, and Google Cloud Pub/Sub as needed.
Additionally, you’ll need a system that ensures all replicated data can be moved securely, especially when sensitive data is being moved to a cloud-based data lake. Robust encryption and security controls are critical to meet regulatory compliance, company policy, and end-user
security requirements.
Qlik Replicate CDC technology non-disruptively copies source transactions and sends them at near-zero latency to any of the real- time/messaging platforms listed above. Using log reader technology, it copies source updates from database transaction logs – minimizing impact on production workloads – and publishes them as a continuous message stream. Source DDL/schema changes are injected into this stream to ensure analytics workloads are fully aligned with source structures. Authorized people also can transfer data securely and at high speed across the wide-area network (WAN) to cloud-based data lakes, leveraging AES-256 encryption and dynamic multipathing.
As an example, a US private equity and venture capital firm built a data lake to consolidate and analyze operational metrics from its portfolio companies. This firm opted to host its data lake in the Microsoft Azure cloud rather than taking on the administrative burden of an on-premises infrastructure. Qlik Replicate CDC captures updates and DDL changes from source databases (Oracle, SQL Server, MySQL, and DB2) at four locations in the US. Qlik Replicate then sends that data through an encrypted File Channel connection over a WAN to a virtual machine–based instance of Qlik Replicate in Azure cloud.
This Qlik Replicate instance publishes the data updates to a Kafka message broker that relays those messages in the JSON format to Spark. The Spark platform prepares the data in microbatches to be consumed by the HDInsight data lake, SQL data warehouse, and various other
internal and external subscribers. These targets subscribe to topics that are categorized by source tables. With the CDC-based architecture, this firm is now efficiently supporting real-time analysis without affecting production operations.
3. Architect for Data in Motion
Your data lake runs the risk of becoming a muddy swamp if there is no easy way for your users to access and analyze its contents. Applying technologies like Hive on top of Hadoop helps to provide an SQL-like query language supported by virtually all analytics tools. Organizations like yours often need both an operational data store (ODS) for up-to-date business intelligence (BI) and reporting as well as a comprehensive historical data store (HDS) for advanced analytics. This requires thinking about the best approach to building and managing these stores to deliver the agility the business needs.
This is more easily said than done. Once data is ingested and landed in Hadoop, often IT still struggles to create usable analytics data stores. Traditional methods require Hadoop-savvy ETL programmers to manually code the various steps – including data transformation, the creation of Hive SQL structures, and reconciliation of data insertions, updates, and deletions to avoid locking and disrupting users. The administrative burden of ensuring data is accurate and consistent can delay and even kill analytics projects.
Qlik Compose™ for Data Lakes (formerly Attunity Compose for Data Lakes) solves these problems by automating the creation and loading of Hadoop data structures, as well as updating and transforming enterprise data within the data store. You, your architects, or DBAs can automate the pipeline of BI ready data into Hadoop, creating both an ODS and HDS. Because our solution leverages the latest innovations in Hadoop such as the new ACID Merge SQL capabilities, available today in Apache Hive you can automatically and efficiently process data insertions, updates, and deletions. Qlik Replicate integrates with Qlik Compose for Data Lakes to simplify and accelerate your data ingestion, data landing, SQL schema creation, data transformation, and ODS and HDS creation/updates.
As an example of effective data structuring, Qlik works with a major provider of services to the automotive industry to more efficiently feed and transform data in a multi-zone data lake pipeline. The firm’s data is extracted from DB2 iSeries and then landed as raw deltas in an Amazon S3-based data lake. In the next S3 zone, tables are assembled (i.e., cleansed and merged) with a full persisted history available to identify potential errors and/or rewind, if necessary. Next these tables are provisioned/presented via point-in-time snapshots, ODS, and comprehensive change histories. Finally, analysts consume the data through an Amazon Redshift data warehouse. In this case, the data lake pipeline transformed the data while structured data warehouses perform the actual analysis. The firm is automating each step in the process.
A key takeaway here is that the most successful enterprises automate the deployment and continuous updates of multiple data zones to reduce time, labor, and costs. Consider the skill sets of your IT team, estimate the resources required, and develop a plan to either fully staff your project or use a technology that can reduce anticipated skill and resource requirements without compromising your ability to deliver.
Automating the Data Lake Pipeline
Your data management processes should minimize production impact and increase efficiency as your data volumes and supporting infrastructure grow. Quantities of hundreds or thousands of data sources affect implementation time, development resources, ingestion patterns (e.g., full data sets versus incremental updates), the IT environment, maintainability, operations, management, governance, and control.
Here again organizations find automation reduces time and staff requirements, enabling staff to efficiently manage ever- growing environments. Best practices include implementing an efficient ingestion process, eliminating the need for software agents on each source system, and centralizing management of sources, targets, and replication tasks across the enterprise.
With Qlik Replicate, your organization can scale to efficiently manage data flows across the world’s largest enterprise environments. Our zero-footprint architecture eliminates the need to install, manage, and update disruptive agents on sources or targets. In addition, Qlik Enterprise
Manager™ (formerly Attunity Enterprise Manager) is an intuitive and fully automated, single console to configure, execute, monitor, and optimize thousands of replication tasks across hundreds of end points. You can track key performance indicators (KPIs) in real time and over
time to troubleshoot issues, smooth performance, and plan the capacity of Qlik Replicate servers. The result: the highest levels of efficiency and scale.
Whenever possible, your organization should consider adopting specialized technologies to integrate data from mainframe, SAP, cloud, and other complex environments. Here’s why:
Enabling analytics with SAP-sourced data on external platforms requires decoding data from SAP pooled and clustered tables and enabling business use on a common data model. Cloud migrations require advanced performance and data encryption over WANs.
And deep integration with mainframe sources is needed to offload data and queries with sufficient performance. Data architects have to take these and other platform complexities into account when planning data lake integration projects.
Qlik Replicate provides comprehensive and deep integration with all traditional and legacy production systems, including Oracle, SAP, DB2 z/ OS, DB2 iSeries, IMS, and VSAM. Our company has invested decades of engineering to be able to easily and non-disruptively extract and decode transactional data, either in bulk or real time, for analytics on any major external platform.
When decision makers at an international food industry leader needed a current view and continuous integration of production-capacity data, customer orders, and purchase orders to efficiently process, distribute, and sell tens of millions of chickens each week, they turned to Qlik. The company had struggled to bring together its large datasets, which were distributed across several acquisition-related silos within SAP Enterprise Resource Planning (ERP) applications. The company relied on slow data extraction and decoding processes that were unable to match orders and production line-item data fast enough, snarling plant operational scheduling and preventing sales teams from filing accurate daily reports.
The global food company converted to a new Hadoop Data Lake based on the Hortonworks Data Platform and Qlik Replicate. It now uses our SAP-certified software to efficiently copy SAP record changes every five seconds, decoding that data from complex source SAP pool and cluster tables. Qlik Replicate injects this data stream – along with any changes to the source metadata and DDL changes – to a Kafka message queue that feeds HDFS and HBase consumers subscribing to the relevant message topics (one topic per source table).
Once the data arrives in HDFS and HBase, Spark in-memory processing helps match orders to production on a real-time basis and maintain referential integrity for purchase order tables within HBase and Hive. The company has accelerated sales and product delivery with accurate real-time operational reporting. Now, it operates more efficiently and more profitably because it unlocked data from complex SAP source structures.
The global food company converted to a new Hadoop Data Lake based on the Hortonworks Data Platform and Qlik Replicate. It now uses our SAP-certified software to efficiently copy SAP record changes every five seconds, decoding that data from complex source SAP pool and cluster tables. Qlik Replicate injects this data stream – along with any changes to the source metadata and DDL changes – to a Kafka message queue that feeds HDFS and HBase consumers subscribing to the relevant message topics (one topic per source table).
Once the data arrives in HDFS and HBase, Spark in-memory processing helps match orders to production on a real-time basis and maintain referential integrity for purchase order tables within HBase and Hive. The company has accelerated sales and product delivery with accurate real-time operational reporting. Now, it operates more efficiently and more profitably because it unlocked data from complex SAP source structures.
Streaming Data to a Cloud-based Data Lake and Data Warehouse
By adhering to these five principles, your enterprise IT organization can strategically build an architecture on premises or in the cloud to meet historical and real-time analytics requirements. Our solution, which includes Qlik Replicate and Qlik Compose for Data Lakes, addresses key challenges and moves you closer to achieving your business objectives.
The featured case studies and this sample architecture and description show how Qlik manages data flows at each stage of a data lake pipeline.
Your Data Lake Pipeline
Take a closer look, starting with the Landing Zone. First, Qlik Replicate copies data – often from traditional sources such as Oracle, SAP, and mainframe – then lands it in raw form in the Hadoop File System. This process illuminates all the advantages of Qlik Replicate, including full load/CDC capabilities, time-based partitioning for transactional consistency and auto-propagation of source DDL changes. Now, data is
ingested and available as full snapshots or change tables, but not yet ready for analytics.
In the Assemble Zone, Qlik Compose for Data Lakes standardizes and combines change streams into a single transformation-ready data store. It automatically merges the multi-table and/or multi-sourced data into a flexible format and structure, retaining full history to rewind and identify/remediate bugs, if needed. The resulting persisted history provides consumers with rapid access to trusted data, without having to understand or execute the structuring that has taken place. Meanwhile, you, your data managers, and architects maintain central control of the entire process.
In the Provision Zone, your data managers and architects provision an enriched data subset to a target, potentially a structured data warehouse, for consumption (curation, preparation, visualization, modeling, and analytics) by your data scientists and analysts. Data can be continuously updated to these targets to maintain fresh data.
Our Qlik software also provides automated metadata management capabilities to help your enterprise users better understand, utilize, and trust their data as it flows into and is transformed within their data lake pipeline. With our Qlik Replicate and Qlik Compose solutions you can add, view, and edit entities (e.g., tables) and attributes (i.e., columns). Qlik Enterprise Manager centralizes all this technical metadata so anyone can track the lineage of any piece of data from source to target, and assess the potential impact of table/column changes across data zones. In addition, Qlik Enterprise Manager collects and shares operational metadata from Qlik Replicate with third-party reporting tools for enterprise-wide discovery and reporting. And our company continues to enrich our metadata management capabilities and contribute to open-source industry initiatives such as ODPi to help simplify and standardize Big Data ecosystems with common reference specifications.
You improve the odds of data lake success by planning and designing for platform flexibility, data in motion, automation, scalability, and deep source integration. Most important, each of these principles hinge on effective data integration capabilities.
Our Qlik technology portfolio accelerates and automates data flows across your data lake pipeline, reducing your time to analytics readiness. It provides efficient and automated management of data flows and metadata. Using our software, you and your organization can improve SLAs, eliminate data and resource bottlenecks, and more efficiently manage higher-scale data lake initiatives. Get your analytics project back on track and help your business realize more value faster from your data with Qlik.
Next Steps
For more information or enquiries about Qlik products and services, feel free to contact us below.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
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.
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.
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.
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.
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.
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.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
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.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
Construction Business Trends: Modernizing Outdated ITSM Systems
The construction industry is becoming more dependent on IT to manage employee needs and resources. However, without effective IT systems in place, projects can suffer from wasted time, missed growth opportunities, and ultimately, reduced profitability. A modern IT Service Management (ITSM) solution can help streamline processes, improve service delivery, and foster growth.
IT Roadblocks that Construction Businesses Face Today:
As construction companies grow, the volume of IT service requests increases, and without automation, this can result in a growing backlog of tickets. This backlog not only hampers IT efficiency but also negatively impacts overall employee productivity.
Construction projects are time-sensitive, and IT delays can lead to costly setbacks. Ensuring prompt resolution of IT issues is essential to keep construction operations on track and avoid unnecessary disruptions.
Effective IT management goes beyond solving problems; it provides valuable insights into service performance, SLAs, top issues, and more
Freshservice ITSM Solutions we provide:
Customer Stories (Construction Business)
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
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.
Share
Related contents for you
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
Is your data strategy ready to drive growth and innovation?
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.
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.
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.
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
Share
Related contents for you
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
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.
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.
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.
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
Share
Related contents for you
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
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.
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 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.
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
Share
Related contents for you
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
