One language for all your data

Strategy commissioned an ROI study with UserEvidence to answer a simple question: what does it actually cost when teams can’t agree on the numbers? The results confirmed what most data leaders already suspect.

Ten or more analytics platforms run side by side. Analysts spend hours reconciling figures across systems. Business users stop trusting dashboards, not because the data is wrong, but because they’ve been burned too many times by conflicting answers.

“When someone had a report request, it would take days (or, more often, weeks) to fulfill it. Even then, you had to really know the data and have an advanced knowledge of Microsoft Access to understand the reports, which made them inaccessible to many end users.”

— Senior Manager of Reporting & Insights, omnichannel retail network owned by a Fortune 500 company

That delay isn’t just an operations problem. It’s capacity your team isn’t spending on analysis, forecasting, or the work that actually moves the business. The study puts a number on it: analysts at Strategy customers save an average of 38% of their time once definitions are standardized. It’s time that was previously spent reconciling numbers, not reading them. At a fully-loaded analyst salary of $100K, a 10-person data team is burning $380,000 a year on a problem that has a fix. 

The instinct when data is fragmented is to add more tools. But fragmentation isn’t a tool problem. It’s a definition problem. When every team builds its own version of “customer,” “revenue,” or “active user,” the stack grows and the confusion grows with it.

When organizations introduced a semantic layer,  a centralized layer that defines business concepts once and applies them everywhere,  redundant metrics and models declined by 44 percent.

Not a reduction in the data stack. A reduction in the cost of disagreement.

The trust impact was immediate. Before, respondents rated their ability to maintain consistent metrics and produce accurate reports at 5 out of 10. After, that number rose to 9 out of 10. Confidence in data doesn’t come from having more of it. It comes from everyone working from the same definitions.

“People don’t trust metrics unless it comes from Strategy. It’s become the golden record and that’s changed how quickly our leadership team can make decisions.”

— Senior Manager of Reporting & Insights, omnichannel retail network owned by a Fortune 500 company

This was already a reporting problem. Now it's an AI problem.

Organizations are deploying AI agents and automated workflows on top of the same fragmented data infrastructure. When your AI doesn’t know which definition of “active customer” to use, because three systems each define it differently, it doesn’t flag the ambiguity. It picks one. And you get a confident answer based on the wrong assumption.

Organizations that introduced governed metrics reported a 22 percent reduction in incorrect AI outputs and hallucinations.

That’s not a performance improvement. That’s a risk reduction. The question is what the inverse costs you. Every AI initiative you’ve already deployed is running on whatever definitions it found. If your revenue model and your churn model disagree on what “active customer” means, they’re not working from the same reality. They’re just confident in different wrong answers.

For some teams, the challenge wasn’t just inconsistency but scale. Legacy tools that worked fine for a smaller environment couldn’t survive modernization.

“We were originally using Cognos, but as we moved toward a next generation data warehouse project, it became too large for the platform to handle.”

— Senior Manager, telecommunications provider 

When the foundation is right, speed follows. Dashboards, models, and data products were delivered three weeks faster on average once a semantic layer was in place, because teams stopped spending half their time reconciling numbers and started spending it on the work itself.

If someone asked your data team “What’s our churn rate?” right now,  how many answers would they get? If the answer is more than one, you have a Definition Problem. And every AI agent you deploy on top of that infrastructure is inheriting it.

The fix isn’t a platform overhaul. It’s a definitions conversation. The organizations in this study didn’t start by replacing their stack. They started by agreeing on their five most contested metrics, the ones the CEO and CFO argue about most, and standardizing those first.

One financial services firm in the study calculated they recovered over $80 million annually. Not from a technology overhaul. From giving their people four hours back each day that had been spent manually consolidating data. 

Your board is probably asking about AI risk. The Definition Problem is one they should know about.

To see the full findings, including the implementation patterns that got organizations from fragmented to governed, explore the complete report.