AI Is Bringing Data Science Closer to Its Goal

The ability to act on accurate data quickly has been the goal of data science from the beginning, and nothing seems to have brought that ability more firmly into our grasp than the advent of AI. Companies both large and small look at the capabilities of intelligent software components (AI agents) working with large language models (LLMs) and salivate over the opportunities presented.

Automate the Mundane, Accelerate the Valuable

At the most basic, these agents can automate mundane tasks while freeing human workers up to tackle the more advanced analytics that can drive profitability. As agents improve and begin working in cooperation, the possibilities expand. So better get them working as soon as possible — there’s no time to lose!

But Wait, Is Your Data Ready?

Ah, but there’s a snag, and sometimes in the rush to implement new AI technology, that snag can be hard to see before it’s too late: the old “garbage in, garbage out” theory.

I can be the fastest reader in the world, consuming multiple books in a day and broadening my knowledge as a result. But if those books are written in a language I don’t understand, are missing every third page, are an outdated edition, or are withheld from me entirely, my knowledge is incomplete, and any actions I take as a result is fraught with peril.

Incomplete or Inaccessible Data? That’s a Problem

The same can happen with AI agents. What if your on-prem data warehouse can’t be accessed efficiently, or the person scanning your physical documents isn’t labeling or summarizing with consistency? Maybe “gate-keepers” in your business aren’t making their data as cooperative or interactive as they could (or should) be? The most powerful AI agent in the world still won’t be giving you its money’s worth in a disjointed environment like that.

The (Not-So-New) Solution: Data Governance

The solution? Data governance. An old-school concept, for sure, but just as applicable in the AI world—maybe more so.

Quality data governance ensures you have your act together from a data perspective:

Stewards ensure their data is consistent and accessible, controlling both the rate and content of change, so every book is stable, up to date, and accessible to all the readers.
Standards allow your data—both electronic and physical—to be categorized in a coordinated manner. That way, everyone across the organization, including the AI agents, is speaking the same language and knows what’s in the book being read and how it may relate to the other books.
Controls over data exposure ensure nothing gets lost (or stolen) along the way, so your books remain your books no matter what shelf they’re on.

Helping You Get There

At Evolution Analytics, we have several architects with over 20 years of experience designing and implementing Data Governance solutions for clients both large and small. Those clients use our solutions to organize their data both for standard reporting and AI interactions, making both an even greater success.