Trusted data solves all known problems. That premise sounds simple. The reality enterprises are living with is anything but.
As AI investment accelerates, data clarity is degrading. Vendor counts in the data space have tripled in five years. Budgets have grown. The dashboards look better than ever. And yet the practitioners running these organizations — the people who actually live with the data — tell a different story in private.
What it costs, in human hours and institutional trust, to compensate for data that nobody fully believes. What happens when you layer AI on top of foundations that aren't ready. One former chief executive we interviewed said it directly:
The minute you put AI on top of bad data, it gets crap.
That's what we set out to document.
What we did differently
diginomica and Serendipitus undertook what we believe is the first bottom-up qualitative study of enterprise data health — not a vendor survey, not a benchmark report, but frank conversations with 18 senior practitioners under conditions of total anonymity.
No PR handlers. No vendor oversight. No approval process for quotes.
Rather than measuring stated confidence, we were measuring behavioral trust — the difference between saying you trust your data and what you'd actually hand to the CEO without checking first. As one marketing leader in financial services told us:
I was about to say 0% of the time. I'm actually going to adjust that to 1%.
What we found
The gap between how the market talks about data and how organizations actually live with it has never been wider. The problems aren't primarily technical. The tools exist. The platforms have been purchased. The governance frameworks have been written.
What's missing is harder to sell and harder to fix: shared vocabulary, aligned incentives, organizational structures that make data-sharing possible rather than political. As one commercial leader observed:
The data exists. Someone owns it and is accountable for it. None of it is joined up.
The report documents what practitioners told us — and what it means for the decisions that organizations need to make now, before AI amplifies every existing data problem at scale. If any of what you've just read sounds like the organization you work in, the full Enterprise Data Health Study is available to download below. It won't tell you to move faster. It will tell you what's actually in the way.
