datagovernance

Stop Making Copies: 3 Information Governance Principles for Reliable Data – E136

Clean dashboards and reliable data don’t happen by accident. In this episode of What Counts, Lee Karas and Maura Dunn pick up from the shift from records management to information governance and move into the three action principles that actually get you there: stop making copies, focus on data creation points, and touch once, use many times. They trace how society lost its mindfulness about creating data from Sumerian clay tablets to the printing press to the Federal Records Act and walk through a painfully familiar example of how a single shared link quietly multiplies into four copies. Along the way, they unpack the reliability paradox at the heart of information governance: the more copies you make, the less you can trust your data. It’s a candid, practical conversation about why these simple-sounding principles are so hard to follow, and why the tools we use every day keep reinforcing our worst habits.

New here? Start with the previous episode: Records Management vs. Information Governance.

Key Takeaways

The more copies you make, the less reliable your data becomes. People hoard copies because they don’t trust they’ll find the original later, which only makes the underlying data less trustworthy. It’s a paradox, but it holds.

We’ve lost our mindfulness about creating data. When information was expensive and hard to produce, more thought went into it. Now anyone can create and disseminate data in seconds, so almost none goes into it.

Three principles work together: stop making copies, focus on data creation points, and touch once / use many times. They’re simple to say, clear to understand, and genuinely hard to do.

Data creation deserves rules. Who’s allowed to create a new contract, location, or counterparty record? Where does it live? Who can change it, and how? Answering these up front prevents downstream chaos.

Acquisitions are data creation events. When data comes in through an acquisition, you need a crosswalk from old identifiers to new ones or you end up asking, “Where’s all the data for Armadillo Ranch?”

Bad data has real costs: failed audits, unanswerable litigation and e-discovery requests, duplicate survey spend, and revenue you can’t collect because you can’t prove your rights.

Your tools may be the problem. Email and collaboration platforms often reinforce copy-making habits or force workarounds, because the value of doing it right isn’t obvious in the moment.

Your Data Is Messy – Can AI Actually Handle It? – E131

Episode 131 – Data governance and AI readiness go hand in hand — and most organizations aren’t as ready as they think. In this episode, Maura and Lee take a hard look at what an AI-powered customer support escalation workflow actually requires to function. Spoiler: it’s not just a smart model. It’s clean, connected, versioned data — customer records, contract terms, and executed agreement instances all properly linked. Using a real-world cable company scenario, they unpack how disconnected systems, outdated identifiers, and missing metadata cause AI to hallucinate answers instead of finding them. The episode closes with an introduction to data objects and metadata mapping — and a preview of the counterparty reconciliation work that has to happen before AI can deliver on its promises.

Episode length: 00:19:21

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