Truly trusted data is the gift that keeps on giving, for organizations of all sizes. From startup to enterprise, if you can trust your data you can do, well, pretty much anything. At our recent “Blueprints for Generative AI” event, expert speakers delivered a wealth of insights into leveraging AI to improve data quality and governance. Here's a summary of the critical takeaways that can help you harness the power of AI to cultivate trust in your data assets. Missed it? Watch the replay on-demand now!
Through AI, governance automation like never before
Industries have always tried to automate to save time. When it comes to data governance, AI means the opportunity for automation skyrockets, and productivity can skyrocket along with it. Large Language Models can answer enterprise questions in a way that’s never been seen before.
Juan Sequeda, head of data.world’s AI Lab, noted: “It's been an eight year journey for us. Beginning with the foundational development of our knowledge graph architecture, we built a data catalog application designed to crowdsource and automate the creation of enterprise knowledge graphs. From there we developed automations to streamline governance tasks enabling the automation of workflow and governance activities. And today we culminate this evolutionI integrating generative AI into the knowledge graphs our customers build, setting a new standard for data interaction.”
The importance of “eating your vegetables”
Understanding how data travels across systems, transforms, and reaches its final state is crucial for ensuring trust and quality. It may not be the most glamorous work, but it sets an important AI-ready foundation.
According to Sequeda, “Eating your vegetables can be fun. Doing all that catalog governance and stewardship work is the foundation to be able to go build all these AI applications and AI agents. The work that you're putting inside of your catalog is really the foundation for all of that.”
AI isn’t just about the end result, where you’re unlocking a supercharged data strategy through flashy bells, whistles, and robots. If your team is in the process of “eating its vegetables”, you’re probably on the right track for setting up a foundation of AI-ready data.
Metadata management helps keep the business on track
Metadata is at the heart of data governance, and AI significantly improves metadata management by providing automatic classification and annotation.
AI's ability to tag, categorize, and classify data at scale unlocks the power to quickly find and use trusted data. These capabilities are essential in rapidly growing data environments where new datasets are frequently ingested.
Cris Hadijez, Sr. Dir of Data Governance, Norwegian Cruise Line Holdings, has been working to adapt AI in metadata management with data.world’s AI Context Engine. He told us: “[AI-readiness] requires us to do a really effective job in metadata management. At this point in time, we really need to understand the lineage, because when you're migrating from Platform A to platform B, the business still wants the same reports. They still want the same data. But you’ve got to figure out all the mapping. There's a huge value in being able to say, ‘Here’s apples. And then here's apples.’ It's not, ‘Here's apples. And then there's oranges.’ They're ultimately looking for the same thing, even if you migrated to a new platform.”
Having a “digital conversation” with your data
Imagine having a seamless conversation with your data, where insights flow effortlessly like words in a chat, and they’re all centered around your specific data.
A data catalog can essentially open up the black box of AI, delivering the explainability you need on questions and structured data. According to Jason Guarracino, Sr. Technical Product Manager at data.world, “it essentially throws [the black box of AI] entirely away.”
To effectively enact AI-ready data, you’ll need to bring the data to life by connecting relationships, uncovering patterns, and revealing hidden insights. Essentially, your goal state is to be able to “have a digital conversation” with your data.
Trust in data is everyone’s business
Another emerging theme of the event was that creating a culture of trust around data requires more than just tools and technologies. It demands collaboration between technical and non-technical stakeholders. By embedding data governance into the culture and utilizing AI to streamline and automate processes, businesses can confidently rely on their data for decision-making.
Building trust in your data with AI is not just about implementing new technologies but about establishing holistic frameworks for AI data governance. With AI-powered quality control, lineage tracking, and anomaly detection, you can ensure your data remains accurate, consistent, and reliable.
To dive deeper into cutting-edge AI strategy and hear from the experts directly, be sure to watch the Generative AI event on-demand.
