Understanding and taking control of your data ecosystem [Q&A]

Data comes in many forms, it may be structured, it may be unstructured, it could be sensitive or purely statistical. Whatever it looks like you can only benefit from it if you know what you have, where it can be found and how to access it.

We spoke to Brett Hurt, CEO and co-founder of data.world to discuss how enterprises can understand their data and derive the maximum value from it.

BN: What is the importance of AI data governance?

BH: AI data governance reduces data risk and drives more valuable AI outcomes. It accomplishes this by cataloging AI use cases and validating data reliability.

AI data governance also helps teams discover and protect sensitive data, so they can stay compliant and avoid fines. Through AI, teams enforce data access policies that give them greater control over who has access to specific data.

Essentially, by ensuring that the data used in AI applications is reliable and properly managed, it ultimately leads to more trust in AI systems.

BN: Knowledge graph is the foundation of your company's data catalog. Can you explain what this is and how it benefits your customers?

BH: A knowledge graph is a sophisticated representation of an organization's entire data ecosystem. It connects and models metadata, processes, policies, people, and systems in a way that enables extensive automation.

As the foundation of the data catalog, this architecture offers significant benefits to customers. It provides a more interconnected view of the organization's data landscape: think rolling camera versus still snapshot. The knowledge graph structure enhances the potential for AI applications by providing rich context and relationships within the data. Ultimately, it’s a tool that allows customers to gain deeper insights and make better use of their data assets.

BN: Can you explain how context engines create a strategic advantage in today's competitive landscape?

BH: Context engines bridge the gap between generic AI models and an organization's specific needs. They make it possible to basically 'Google' your own data. By providing relevant, organization-specific context, these engines make AI more tailored and effective for individual businesses. Think about the competitive edge you'd have if you could ask your databases things like, "How much ROI did X product deliver over the past five years, and how does it compare to the ROI for Y product?"

BN: How is data.world different from your competitors?

BH: We set ourselves apart from the competition because of our unique approach to data management and AI-readiness. We're the only data catalog platform built on a knowledge graph architecture.

We're also a team of actual scientists and innovators. We have 76 patents and counting. We've always been focused on AI-ready data that is accurate, explainable, and governed -- way before it was cool.

Our platform includes data cataloging, governance, and DataOps, all powered by the knowledge graph. We can unify the data sprawl better than anyone.

We leverage AI, automation, integrations, and partnerships to help organizations maximize the utility of their data.

BN: Data sprawl has been a long-standing issue for today's enterprises. How does data.world address this issue?

BH: Organizations are definitely drowning in data, there's no doubt about that. Data sprawl chokes productivity and obscures valuable insights.

Rather than attempting to centralize all data in one place, data.world provides a unified data catalog platform that can integrate various data silos and tools. Data can stay where it is -- that's fine! We understand that organizing data is a huge job and it's often not under a single purview.

That's why our platform does the work independently. We connect models and disparate data sources to create a cohesive view of the enterprise's data landscape. Our catalog acts as a single pane of glass, offering visibility into data assets scattered across various systems, databases, and cloud platforms.
We understand that when it comes to managing data sprawl, it's not just about knowing where data is. It's about understanding what it means, where it came from, and how it's been used. Tracking data movement and transformations across systems helps in understanding data flow and impacts.

And like any modern data catalog, data.world is built to handle massive volumes of data across diverse sources, so we grow alongside our customers' data ecosystems.

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