The Data Understanding phase exists precisely to catch these problems before they become expensive. Here are five of the most common pitfalls, and what to do about each.

Here is a number that surprises a lot of people: only about 20% of enterprise data is structured. The other 80% — emails, PDFs, images, videos, call transcripts, social media posts, scanned contracts — is unstructured.

Most people have heard the term "big data" tossed around in meetings for years. Fewer people could actually tell you what makes data "big" in a way that matters for an AI project.

"So — what should we use AI for?"

It sounds like a simple question. It isn't. And the reason most organisations struggle to answer it isn't lack of ambition or budget. It's that "AI" has become a single word standing in for an enormous family of fundamentally different technologies — each with different requirements, different risks, and different definitions of success.

When a technology project is failing, speed matters—but clarity matters more.

When a technology project starts to fail, urgency takes over. Deadlines are missed. Costs rise. Pressure mounts. Leadership wants answers—and teams want direction.