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March 7, 2022

Data Fabrics: The Killer Use Case for Knowledge Graphs

Gartner and Forrester agree that to achieve agile, scalable data integration, the value derived from a data fabric architecture is worthy of investigation. According to Forrester Analyst Noel Yuhanna data fabrics powered by knowledge graph may very well be the best fit solution to deliver the desired state of enterprise-scale data integration. But how do you get there?

Sometimes the “killer use case” for a new technology or idea seems obvious. Take bubble wrap for example. Bubble wrap was originally intended to be new “cool” textured wallpaper. It wasn’t until IBM launched the 1401 computer that bubble wrap was first used for the purpose of keeping products safe in transit.

Similarly, knowledge graph has been around for awhile, but it seems to finally be finding its killer use case: data fabric architecture.

First conceived in the 1970s, now, knowledge graphs are part of our daily lives. Digital platforms like Google, LinkedIn, and Apple who use them to create experiences such as search results, professional connections, or a virtual assistant “Siri”.

A Knowledge Graph is a connected graph of data and associated metadata applied to model, integrate and access an organization’s information assets. The knowledge graph represents real-world entities, facts, concepts, and events as well as all the relationships between them yielding a more accurate and more comprehensive representation of an organization’s data.

 Data fabrics, in contrast, are a newer idea, first formally defined in 2016 by Forrester Analyst Noel Yuhanna, in his report “Big Data Fabric Drives Innovation and Growth.” A data fabric is a modern data management architecture that allows organizations to agily use more of their data more often to fuel analytics, digital transformation, and other high stakes business processes. It enables these advantages by proactively preparing, integrating, and modeling data from numerous, diverse, enterprise source systems into an integrated platform of business-ready data for on-demand access.

Within a data fabric architecture, a knowledge graph brings much needed capabilities in several key areas:

  • They connect related data across silos with enormous flexibility, linking thousands or millions of related points of data from across the business with unprecedented granularity and flexibility in the data integration process.
  • Knowledge graphs make complicated data easier to understand and use, by establishing a semantic layer of business definitions and terms on top of the often cryptic and highly technical names assigned to individual fields of data at the schema or application layer.
  • They allow organizations to tap into more data reserves/sources, structured, semi-structured, or unstructured to fuel analytics.
  • And finally, knowledge graphs make the overall data fabric more flexible and easier to build out incrementally over time, thus lowering risk, speeding deployment, and delivering immediate value.

This is accomplished by building the data fabric in stages – starting with one data domain, or high value use case, and building that into the initial knowledge graph model, then incrementally expanding over time with more data, use cases, and users.

What does it take?

To deliver these capabilities, the knowledge graph layer within the data fabric requires some specific attributes:

  • Any Data in Any Format – structure, source, format agnostic
  • Automated Data Onboarding – agile, performant loading and storage
  • Flexible Deployment – wherever: on-premises, cloud or hybrid model
  • Interactive Query at Scale – MPP models and in-memory query execution
  • Use Open Standards – easy integration for all data, models, and metadata
  • Enterprise Security and Governance – fine-grained access and authorization controls

Where to Begin

Thought leaders like Etisham Zaidi at Gartner and Noel Yuhanna at Forrester strongly recommend organizations include a knowledge graph in any data fabric stack. But for ordinary IT and data leaders, often the more pragmatic questions are: “How do I get started?” ”What is the best use case?” “What skills will I need?” and perhaps most vexing, “How do I build a graph representation of my data, something that we have never done before?”

A great place to start is by perusing O’Reilly Media’s The Rise of the Knowledge Graph, an ebook that covers everything knowledge graph, soup to nuts.