3 Ways to Confirm Your Data Catalog Is Really Powered by a Knowledge Graph
Posted: Tue Feb 11, 2025 5:45 am
Why your data catalog needs to be powered by a knowledge graph
I’ve said it before, and I’ll say it again — your organization needs a data catalog.
Modern companies need data catalogs to empower your workers to get more information from your data investments, gain better data insights as a whole, and make smart decisions more quickly.
I’ve also been adamant that any data catalog you adopt needs to be powered by a knowledge graph. And I mean a real knowledge graph — we’ll get to what real means in a moment.
A knowledge graph consists of nodes and edges representing real-world objects and the relationships between them. The nodes (bubbles) in the knowledge graph can represent tables, columns, dashboards, reports, business terms, users, domains… anything that exists within the data ecosystem of your organization. The edges (lines) represent their relationships, and how they’re associated, related, derived from, and so on.
Without a knowledge graph powering your data catalog, you can’t properly integrate knowledge and data across your organization. And beyond that, without a knowledge graph, your indonesia whatsapp number data data environment and sources may eventually outgrow your catalog’s capabilities. Data catalogs powered by traditional relational technology and not by a knowledge graph are rigid and inflexible, meaning it can take months for them to support new types of data sources. And that’s not time any modern, data-driven business has to spare.
By building your data catalog on a knowledge graph model, you make it easy to quickly extend your catalog alongside your growing data ecosystem. That’s why data-driven leaders like Airbnb, Lyft, and LinkedIn have built their catalogs on a knowledge graph. And the industry has taken note.
Suddenly, everything’s a “knowledge graph”
The adoption of knowledge graph models by these industry behemoths has made plenty of noise in the world of data. Perhaps that’s why, all of a sudden, it seems every data catalog is powered by a knowledge graph, some seemingly magically switching from traditional relational technology overnight!
But just because you’re calling your model a “knowledge graph” doesn’t make it so. When you look beyond the marketing hype, you’ll see true knowledge graphs possess three distinct characteristics:
I’ve said it before, and I’ll say it again — your organization needs a data catalog.
Modern companies need data catalogs to empower your workers to get more information from your data investments, gain better data insights as a whole, and make smart decisions more quickly.
I’ve also been adamant that any data catalog you adopt needs to be powered by a knowledge graph. And I mean a real knowledge graph — we’ll get to what real means in a moment.
A knowledge graph consists of nodes and edges representing real-world objects and the relationships between them. The nodes (bubbles) in the knowledge graph can represent tables, columns, dashboards, reports, business terms, users, domains… anything that exists within the data ecosystem of your organization. The edges (lines) represent their relationships, and how they’re associated, related, derived from, and so on.
Without a knowledge graph powering your data catalog, you can’t properly integrate knowledge and data across your organization. And beyond that, without a knowledge graph, your indonesia whatsapp number data data environment and sources may eventually outgrow your catalog’s capabilities. Data catalogs powered by traditional relational technology and not by a knowledge graph are rigid and inflexible, meaning it can take months for them to support new types of data sources. And that’s not time any modern, data-driven business has to spare.
By building your data catalog on a knowledge graph model, you make it easy to quickly extend your catalog alongside your growing data ecosystem. That’s why data-driven leaders like Airbnb, Lyft, and LinkedIn have built their catalogs on a knowledge graph. And the industry has taken note.
Suddenly, everything’s a “knowledge graph”
The adoption of knowledge graph models by these industry behemoths has made plenty of noise in the world of data. Perhaps that’s why, all of a sudden, it seems every data catalog is powered by a knowledge graph, some seemingly magically switching from traditional relational technology overnight!
But just because you’re calling your model a “knowledge graph” doesn’t make it so. When you look beyond the marketing hype, you’ll see true knowledge graphs possess three distinct characteristics: