The Four Shapes of Messy Data: Why AI Projects Stall Before the Model Ever Runs
About this session
Every stalled AI project starts with the same word: messy. But messy is a complaint, not a diagnosis. Drawing on 40 years inside enterprise data environments, Tim Brown breaks down the four recognizable shapes that kill AI projects: Duplicated, Undefined, Unowned, and Unreconciled data. For each shape you get the sign that you are looking at it, the specific damage it does to a model built on top of it, and a fix you can start the same week, often in nothing more than a spreadsheet. You will also see why AI makes these problems worse, not better: a model run on bad data still returns an answer, fast, confident, and wrong in ways that are harder to catch than an obvious failure. You leave with a four-word diagnostic vocabulary for your next scope meeting and one sequencing rule that protects your timeline and your credibility: data first, then analytics, then AI.
Speaker
Key takeaways
- A diagnostic vocabulary that replaces "messy": every project-killing data problem is Duplicated, Undefined, Unowned, or Unreconciled.
- For each shape, the early-warning sign you're looking at it and a practical first fix you can start the same week, often in nothing more than a spreadsheet.
- The sequencing rule that protects your timeline and credibility: data first, then analytics, then AI, because a model run on bad data returns fast, confident, wrong answers.