It’s now well established that the underpinning of your data stack, the first tool you purchase to kickstart your data governance initiatives, should be a data catalog. According to Gartner, data catalogs are the foundation of modern data and analytics.
But not all data catalogs are created equal, as those powered by antiquated relational technology are rigid, inflexible, and limited in their functionality. Modern data catalogs, however, powered by a knowledge graph with SPARQL automation, offer myriad advantages over their long-in-the-tooth predecessors.
Automatic Business Insights
A data catalog built on a knowledge graph with SPARQL automation empowers you to query your metadata and automatically generate business insights to drive immediate optimization and improvement.
For example, your data catalog can help:
Drive efficiency during a cloud migration: A SPARQL query can help you generate a prioritized backlog of the resources that need to be migrated, like those related to the most important business reports. It can also identify bottlenecks in the data infrastructure that should be addressed first: nodes that have an overabundance of incoming and outgoing edges.
Reduce cost and risk: Your knowledge-graph-based catalog can identify complex resources that should be simplified in order to reduce cost and mitigate risk, like duplicate data or data that is isolated from the larger landscape, both of which could be “turned off” to save money. Or by gaining a view of role-based access control, you could learn who has access to what data to ensure security and compliance.
Assist with employee knowledge transfer: You’d also be able to identify resources managed or dependent by an employee who is leaving your organization, then transfer those resources to another stakeholder who can take over responsibility.
On top of that, building your data catalog on a knowledge graph gives you the technical flexibility to catalog anything! I’m not talking about only the tables, columns, and dashboards you’ve already considered, but even proprietary data systems, new technology, business questions, decisions, business processes, metrics, ML models… even your people! And you can easily extend a knowledge graph model to represent new concepts, relationships, and data types that you haven’t before defined. No ifs, ands, or buts.
How does this benefit you? Consider some use cases data catalogs support:
Integrating technical metadata with employee skills: If your organization is migrating to the cloud and needs to understand the technical metadata from your monolithic legacy systems, how can you be sure you have enough people in house who understand those technologies? And if so, when will those people be retiring? Do we have enough expertise in house for this migration project for the foreseeable future?
Translating data lineage to business lineage: What are the business questions that your different stakeholders are asking, and how are they related to critical business topics and metrics? Is there a way you can consolidate and base decisions on a smaller number of metrics?
Explaining AI/ML automation: How do you explain the decision of an AI system? What model was used? What features were used? What datasets were used? Who are the stewards of those datasets?
Powerful Today, and Prepared for Tomorrow
The bottom line? A knowledge-graph-powered data catalog supports your known use cases of today and your unknown use cases of tomorrow, protecting your investment for the future, whatever it brings.
Learn more about how data.world’s knowledge-graph powered data catalog can benefit your enterprise business.