Your organization has a data strategy. Maybe it was developed last year, maybe three years ago. You invested in tools, hired people, and built dashboards. But something isn't clicking.
The reports exist but nobody looks at them. The data warehouse is built but questions still take weeks to answer. Leadership talks about being "data-driven" but makes decisions the same way they always have. Here's how to diagnose what's going wrong.
1. Nobody Uses the Dashboards You Built
You invested in a BI platform, built 30 dashboards, and usage is flat. Executives glance at them in meetings but don't drill into the data. Business users still export to Excel because the dashboards don't answer their actual questions.
The problem: The dashboards were built for the data team, not for the business. They show what's easy to measure, not what people need to decide.
The fix: Go back to the business users. Ask them what decisions they make weekly and what data they need to make them. Rebuild from that starting point.
2. The Same Questions Keep Getting Asked
If your CEO asks "what's our churn rate?" and three people give three different answers, you don't have a data problem — you have a definitions problem. When metrics aren't standardized, every report becomes an argument about methodology.
The problem: You skipped governance. There's no single source of truth for key business metrics.
The fix: Define your core 15-20 business metrics. Agree on the calculation methodology, the data source, and who owns each one. Then build a single layer of truth that everyone references.
3. Your Data Team Is Drowning in Ad-Hoc Requests
If your data team spends 80% of its time on one-off requests — "can you pull this list?" or "what did Q3 look like by region?" — they're not building strategic capability. They're running a service desk.
The problem: The organization doesn't have self-service analytics. Every question requires a data team member to write a query.
The fix: Invest in self-service tooling that lets business users answer routine questions themselves. This frees the data team for higher-value work — automation, predictive models, infrastructure improvements.
4. You Can't Trust Your Numbers
When people qualify reports with "this might not be exactly right" or "depending on how you count it," trust is broken. Once trust is broken, people stop using data to make decisions — and your entire investment is wasted.
The problem: Data quality issues at the source. Duplicate records, missing values, inconsistent formats, stale data. The warehouse is only as good as what goes into it.
The fix: Audit your data quality at the source system level. Implement validation rules, deduplication, and monitoring. This isn't glamorous work, but it's the foundation everything else depends on.
5. Projects Take Longer Than They Should
If building a new dashboard takes six weeks instead of two, or integrating a new data source takes a quarter, your architecture is the bottleneck. Technical debt accumulates silently until everything takes 3x longer than it should.
The problem: The data infrastructure wasn't designed for the current scale or complexity. Pipelines are fragile, documentation is sparse, and changing one thing breaks another.
The fix: Targeted infrastructure improvements — not a complete rebuild, but focused work on the bottlenecks. Automate what's manual, document what's tribal knowledge, and refactor what's fragile.
6. Business Leaders Don't Know What You're Working On
If the VP of Sales can't tell you what the data team delivered last quarter, you have an alignment problem. The data team is working — they're just working on things the business doesn't see, use, or value.
The problem: No shared roadmap between the data team and the business. Priorities are set based on technical interest rather than business impact.
The fix: Build a shared backlog with business stakeholders. Prioritize by business value, not technical complexity. Report on outcomes monthly: what was delivered, what business problem it solved, and what's coming next.
What to Do About It
If you're seeing three or more of these signs, your data strategy needs a reset — not necessarily a replacement, but an honest reassessment of what's working and what isn't.
Start with a data audit: evaluate your current tools, processes, and outputs. Talk to the people who were supposed to benefit from the data investment and find out why they're not.
The goal isn't to start over. It's to realign the work with the business problems it was supposed to solve. Sometimes that means rebuilding dashboards. Sometimes it means fixing data quality. Sometimes it means rethinking the team structure entirely.
A structured data audit is often the fastest way to identify what's broken. Here's how our audit process works — we assess your data infrastructure, processes, and outputs, then deliver a prioritized action plan.