Today, organizations are experiencing relentless data growth spurred by the digital acceleration of the past two years. While this period presents a great opportunity for data management, it has also created phenomenal complexity as businesses take on hybrid and multicloud environments.
When it comes to selecting an architecture that complements and enhances your data strategy, a data fabric has become an increasingly hot topic among data leaders. This architectural approach unlocks business value by simplifying data access and facilitating self-service data consumption at scale.
A data fabric orchestrates various data sources across a hybrid and multicloud landscape to provide business-ready data in support of analytics, AI and other applications. This powerful data management concept breaks down data silos, allowing for new opportunities to shape data governance and privacy, multicloud data integration, holistic 360-degree customer views and trustworthy AI, among other common industry use cases.
How IBM built its own data fabric
When I rejoined IBM in 2016, enterprise-level data and its use was having a pivotal moment. Because of advances in cloud computing and AI, it was clear that data could play a much bigger role beyond being a necessary output. Data was emerging as an asset that could benefit all aspects of an organization.
As the newly appointed Chief Data Officer (CDO), I was charged with creating a business data strategy built around making IBM a hybrid cloud and AI-driven enterprise. My goal was to implement a data strategy and architecture that gave appropriate users access to data. The key aspects were that the data had to be trusted and secure, and it had to deliver insights that drive business value through analytics without sacrificing privacy.
An important part of our evolution also included preparing for the EU’s General Data Protection Regulation (GDPR), which went into effect in May 2018. Our journey to build security, governance and compliance into our data strategy still serves as a digital solution for our clients and customers to achieve GDPR readiness.
Amid the ever-evolving complexity of our environment, we recognized a need for a data fabric architecture to deliver on our data strategy. The critical benefit of a data fabric is that it provides an augmented knowledge graph detailing where the data is, where it lies, what it’s about and who has access to it. Once we established an augmented knowledge graph, which is the main component of a data fabric, we were in a strong position to intelligently automate across the enterprise, infusing AI into all our major processes, from supply chain to procurement to quote-to-cash. This eventually delivered major reductions in cycle time. Plus, we soon realized that our own business transformation doubled as a blueprint for our clients and customers. But what else can a data fabric do?
How data fabric lays the foundation for data mesh
A data fabric not only acts as a central pane of glass that creates visibility; it also provides a flexible foundation for a component such as a data mesh. A data mesh breaks large enterprise data architectures into subsystems that can be managed by various teams.
By laying a data fabric foundation, organizations no longer have to move all their data to a single location or data store, nor do they have to take a completely decentralized approach. Instead, a data fabric architecture allows for a balance between what needs to be logically or physically decentralized and what needs to be centralized.
A data fabric sets the stage for data mesh in several ways, such as providing data owners with self-service and creation capabilities, including cataloging data assets, transforming assets into products and following federated governance policies.
The future of data leadership
Today’s data leaders are primarily focused on one of three strategic drivers: mitigating risk, growing the top line and enhancing the bottom line. What I find exciting about building a strategy around a data fabric architecture is that it allows data leaders to act as change agents by addressing these business needs all at the same time.
At the IBM summit, data leaders all concurred that we’re entering a new phase, where there will be much more decentralization in the world of data management. We expect to see concepts such as data fabric and data mesh play a critical role in strategy, as they can empower teams to access resources and tools they need on-demand to support them throughout the data product lifecycle.
But there’s more discussion to be had. In the newly-released guide for data leaders, The Data Differentiator, you’ll find our six-step approach for designing and implementing a data strategy. This information is continually tested and optimized by IBM experts during client engagements, and we wanted to share it with the community to help facilitate conversation about what it takes to succeed with data. You’ll also find a discussion of the role your data management architecture plays.
To hear more from data leaders around data fabric and how to deliver on your data strategy, watch the replay of the CDO/CTO Summit.