The Business Case for Data Fabric

A data fabric is an architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning hybrid multi-cloud environments. It is a powerful architecture that standardizes data management practices and practicalities across cloud, on premises, and edge devices.

In simplest terms, a data fabric is a single environment consisting of a unified architecture, and services or technologies running on that architecture, that helps organizations manage their data. The ultimate goal of data fabric is to maximize the value of your data and accelerate digital transformation.

The data fabric approach enables companies to move forward with any architecture quickly with an automated integration regime that allows for multiple delivery styles, including CDC (change data capture), replication, virtualization, and ELT (extract, load, and transform), rather than just traditional ETL (extract, transform, and load). Among the benefits of a data fabric:

• Reduce data engineer and IT FTE workload by 25% by aggregating massive amounts of data from multiple sources.
• Cut time on task for data analysts and engineers for data discovery, analysis, and implementation by 65% to 70%.
• Recognize data management efficiency increases of 20% in year two and up to 33% in year three.
• Realize data integration and governance benefits of 60% in year two and an additional 25% in year three.
• Get more value and perspective from first-, second-, and third-party data in real time.

A data fabric can unify, integrate, and govern disparate data from multiple sources in multiple locations while enabling secure access to data regardless of source and location. It also enhances the efficiency of ingestion, preparation, and orchestration via automation.

Data fabrics provide a single point of control for data while supporting data mobility and sharing across the enterprise. They can be used to support various business scenarios, including data consolidation, data protection, data archiving, and disaster recovery. Data fabric can also be applied for data lake management, data warehouse modernization, and data integration. In each case, the data fabric provides a central platform for data management that improves data quality and facilitates data-driven decision-making.

Specific business use cases include innovation, preventive maintenance, customer experience/customer 360, and enhanced governance—all of which can meet data privacy, security, and compliance standards while eliminating data silos.

A data fabric can provide a foundation for developing new data-driven applications and services. As organizations increasingly seek to leverage data as a strategic asset, data fabrics are expected to play a critical role in enabling them to meet their business objectives.

Without a data fabric, enterprises are left to author and manage significant data movement pipelines to populate an enterprise data warehouse. They must also implement multiple departmental data marts and/or build a relatively fragmented data lake. Each of these alternatives is time-consuming without automation and has inherent weaknesses in facilitating comprehensive analysis and insights or driving the best actions and business outcomes for large-scale data aggregation and analysis.

As such, it makes it difficult to get a holistic view of the business and make informed decisions based on all the available data. With a data fabric in place, enterprises can easily access the data they need when they need it. Data fabrics help organizations embrace a data mesh architecture as well as many other data architectures. This makes it possible to rapidly respond to changes in the market and make decisions that drive business growth.

Data fabric has several challenges that must be addressed to be adopted successfully.

• Legacy approaches to data integration, such as ETL, are insufficient for data fabric. Data fabric requires a shift to ELT to process data at the required speed and scale.
• Rapidly evolving technology stacks and architectures, such as lakehouses and mesh, can raise the stakes for data fabric implementations. Data fabrics must integrate with these new technologies to be successful.
• There is a skills gap in the market for data fabric. There is a lack of trained professionals to design and implement data fabrics, and this challenge must be addressed for successful data fabric adoption and ongoing management.

Rolling out a data fabric requires the collaboration of many different groups, including software and IT teams. It’s important to involve all stakeholders, including business and security, in this process so they know what changes will be made to their work environments.

30 Days: The first month should focus on assessing the data landscape and developing a data foundation. This involves collecting and analyzing all metadata, converting passive metadata to active metadata, and creating an enterprise data catalog.

60 Days: The next 30-day span should address implementation of the data fabric platform and begin data integration. This stage includes creating and curating enterprise catalogs, enriching data with semantics, and integrating all data sources and consumers.

90 Days: Finally, attention should turn to expanding data integration to include all data sources and consumers to ensure an ongoing and robust foundation for data integration.

Qlik Data Integration helps organizations move, transform, and make data analytics ready and available to users where and when needed. Unlike traditional batch data movement and ETL scripting—which are slow, inflexible, and labor intensive—Qlik’s Data Integration Platform automates the creation of real-time data streams from core transactional systems and applications. It automates the movement and transformation of data into warehouses and lakes—on-premises and in the cloud—and makes data immediately available to all users through an intuitive catalog.

How does the solution mitigate risk? First, it affords greater agility and flexibility for rapidly changing tech stacks. It supports a broad range of data sources (relational, mainframe, SAP, SaaS apps, and many more) to various targets, including multi-cloud, warehouses, lakes, object stores, databases, and streaming platforms. Qlik maintains deep technical and commercial relationships with major technology vendors to ensure the best experience for our joint customers.

Second, Qlik enables automation for greater operational efficiency and reduced risk. The automation of coding-intensive tasks like cloud data warehouse development and maintenance minimizes error-prone processes and frees time for data engineers to focus on higher-value tasks.

So how does Qlik compare to the industry for cost or ROI? Customer interviews and independent financial analysis have found that a composite organization experiences benefits of $12.6 million over three years versus costs of nearly $3.1 million. That yields a net present value (NPV) of $9.5 million and an ROI of 306%.

This GigaOm report was commissioned by Qlik.