Buyer's Guide
Evaluating data quality platforms for humans & AI
17 capabilities data leaders need and how to make the case to leadership
Enterprise data now has two kinds of consumers, humans and machines, and both have outgrown traditional data quality tooling. Pre-determined pipelines and manually written rules can't validate all data all the time.
Whether you're replacing a legacy platform, consolidating observability tools, or building a program from scratch, this buyer's guide gives you the full evaluation framework and the language to defend the investment.
What's in the guide:
- A scoring rubric for all 17 capabilities, so you can score every vendor demo the same way and compare answers side by side
- The four capabilities that actually matter once an agent is acting on your data, and why the other 13 aren't enough on their own
- The exact questions to ask on a vendor call, grouped by automation, security, catalog integration, and AI readiness
- A build vs. buy breakdown of what an internal rules engine actually costs to maintain by month three
- A way to put a dollar figure on what bad data is costing you now, tied to metrics leadership already tracks