4 Ways a Data Fabric Helps Support More, and More Actionable, Insights

In 2021, leading industry analysts added data fabric to their “watch lists” for hype and market trends. In 2022, every business leader and stakeholder should become familiar with the ways in which it can help support more data-driven insights and actions.

A definition of data fabrics for business leaders

Data fabrics are an emerging technology, a modern distributed data framework encompassing architecture as well as data management and integration software, designed to help organizations manage their data. Data fabrics can help get more—and more relevant—data into workflows faster for business intelligence and reporting. Much the way businesses use IT architectures to manage and maintain IT assets, data fabrics help businesses manage and get value from data.

Using metadata, models, and pipelines, a data fabric accesses, combines, and transforms data, whether in-motion or at-rest, from across a diverse, distributed data landscape—whether data sources are on-prem, in hybrid or multi-cloud, or streaming.

Even though it’s early days for data fabric adoption, this new approach has a projected CAGR forecast of more than 10% from now through 2028. This is no surprise, given the wide range of analytic, operational, transactional, and governance use cases for data fabric architectures.

4 key advantages of data fabric frameworks

Some key advantages of data fabrics related to innovation and insights include:

  1. Enabling faster production of the insights that matter most. If data is the new oil, then data management is the refinery—and data fabrics can automate and accelerate the refinery’s output through applied artificial intelligence (AI) and machine learning (ML) in data management workflows.
  2. Driving disruptive innovation through data. A robust data fabric enables organizations to put all the data needed to work when creating and taking new products and services to market. One example: a next-generation, data-driven recommendations solution for retailers that orchestrates and manages data from a variety of historic and real-time sources, in order to provide customers an AI-infused shopping concierge experience.
  3. Supporting governance and compliance needs. To meet increasingly complex regulatory and compliance needs, data management and use require controlled access, auditability, and traceability for users, processes, and data. This is especially important as key areas of insight value will come from AI, and AI is the focus of coming EU regulation.
  4. Ensuring trustworthiness. By unifying data management processes, data fabrics can ensure only curated, trusted data is accessible for analysis. Further, by clearly documenting the distributed data architecture, transparency across the insights workflow is enhanced.

Areas of opportunity for data fabric implementation

For organizations that have plans for digital transformation, have grown through mergers or acquisitions, or are using or planning for AI, a data fabric framework may be useful.

For example, Koch Industries is an enterprise of enterprises, comprising a dozen subsidiary companies including Georgia-Pacific, Invista, and Molex, all of which had their own data systems and siloes. As Koch’s separate businesses modernized and progressed in their digital transformation journey, it became clear that many needed data quality improvement, more transparent governance, and better overarching data management.

By implementing a new data fabric approach, the Koch team was able to design autonomous data spaces for each business, enable a shared data platform, democratize data management, and improve data sharing across businesses within the wider Koch enterprise.

The value generated at the pilot stage was apparent enough that Koch continued its roll-out. Since piloting data fabrics, Koch has built 10 data fabric tenants with eight in production, and reports that each new tenant shows faster time-to-value than the one before.

Through a data fabric approach, Koch drives collaboration across its disparate businesses. However, the end goal is not limited to greater collaboration, or improving governance and data management—Koch is seeking additional benefits, including faster time-to-insight through AI-infused operational optimization. In this way, they plan to increase the business value of their data for innovation, market disruption, and greater resilience.

There is yet another advantage offered by taking a data fabric approach. When data is entering systems from a variety of touchpoints and through automated processes, it’s difficult to build a 360-degree view of the customer. These “customer blind spots” are often compounded by poorly integrated data storage across multiple systems, whether storage is on-premise or in the cloud. It’s imperative that businesses with similar data management challenges address these issues before there’s a noticeable, negative impact on customer loyalty and insights.

By adopting a data fabric framework, not only is the need for improved governance and resilience addressed, the organization gains a transformative edge for improving relationships with customers.

A case in point is Jumbo Supermarkten B.V. Their growth-through-acquisition strategy mirrors that of many modern businesses—and so did the accompanying data management challenges experienced by Jumbo. They needed a framework for data management to bring all their acquisitions’ data assets together to enable a unified focus on customer loyalty. Now, Jumbo has a base for data governance, reference data, and analytics so it can create a 360-degree view of its customers.

Other businesses can replicate the success Koch Industries and Jumbo Supermarkten B.V. are finding by adopting these data fabric supporting best practices:

  • Ensure there’s executive sponsorship for data and analytics initiatives beyond IT. As discussed, data is the lifeblood of a modern business. Its value goes beyond IT’s purview, with implications for market disruption and innovation. Business leadership needs to ensure the organization aligns on the strategic value of well-managed data, and can do this through executive sponsorship of data management and analytics projects.
  • Build towards a Data and Analytics Center of Excellence (CoE). Even at the earliest stages of your data fabric journey, bring cross-departmental stakeholders together to share a vision for value and strategy. Whether starting small with a steering committee, or going big with a fully chartered CoE, you need to break through silos and ensure stakeholders, beneficiaries, subject matter experts, and underrepresented groups all can participate in the journey to turning data into business advantage.
  • Identify the highest value use cases where data-driven insights can make a measurable impact. Avoid “boiling the ocean” when weaving your data fabric. Too many data management projects flounder when the scope is too broad, or when striving for perfection becomes a blocker for generating value now. For example, data quality projects often are scoped broadly, centering on the need for superior data quality in every data warehouse, repository, or workstream. If the highest business value will come from improved customer experience, focus data management and data quality on just the data relevant to that area, such as CRM data, support case data, and customer churn data.
  • Go all-in on one use case, then expand. “Toe-in-the-water” approaches, especially involving data for AI and advanced analytics, have not produced business value compared to “all-in” commitments, according to leading industry analysts. If you do a pilot program, commit to one use case for a full build-out. Give yourselves a time frame generous enough to enable real transformation. A two year “from start to measurable impact” timeframe is not uncommon for transformative, insights-driven success.

Modern business growth is fueled by data, and the insights data can yield. As shown by Koch Industries and Jumbo Supermarkten B.V., data fabric frameworks not only make data governance easier and more transparent, these approaches can enable the data-driven agility enterprises need now and for the future.