You did the right thing. You invested in a data warehouse. You hired consultants or bought a platform. You sat through the demos, approved the budget, and waited for the promised land of "data-driven decisions."
Six months later, your finance team is still exporting to Excel. Your dashboards exist but nobody opens them. The data team is drowning in ad-hoc requests. And leadership is still arguing about whose numbers are right.
What went wrong?
The Symptoms of a Broken Data Warehouse
Before diagnosing the cause, let's confirm the symptoms. A data warehouse that isn't working looks like:
Nobody uses it. The dashboards are there. The reports exist. But when someone needs an answer, they export from the ERP and build a spreadsheet. The warehouse is a monument to good intentions, not a tool people actually use.
The data is stale. "As of yesterday" isn't good enough when you need to make a decision today. If the warehouse only refreshes overnight—or worse, weekly—it's always one step behind reality.
The numbers don't match. The warehouse says revenue is $2.3M. The ERP says $2.1M. Finance has a third number. Instead of one source of truth, you now have another source of confusion.
Every question requires a ticket. Users can't get their own answers. Every new report needs the data team. The backlog grows. People give up and go back to spreadsheets.
It's too slow. Queries take minutes. Dashboards time out. Users learn that "checking the data" means making coffee while they wait.
If you're nodding along, your data warehouse has failed. But that doesn't mean the concept is wrong—it means the implementation is.
Why Data Warehouses Fail
After seeing dozens of warehouse implementations—successful and failed—patterns emerge. Here's what actually goes wrong.
1. Built by technologists, not for the business
The most common failure: the warehouse was designed around technical best practices, not business questions.
The data team built a beautiful star schema. They normalised everything correctly. The ETL pipelines are elegant. But when the CFO asks "which customers are most profitable after returns and rebates?" the answer is "we'd need to build that."
A warehouse that can't answer the questions your business actually asks is an expensive database, not a business tool.
The fix: Start with questions, not data. What does leadership need to know? What decisions are being made on gut feel that should be made on data? Build for those first.
2. No transformation layer (or a broken one)
Raw data from your ERP isn't useful. It needs transformation: business logic applied, calculations performed, dimensions created.
Many warehouses skip this. They dump raw ERP tables into the warehouse and call it done. Now analysts have to understand the ERP's internal schema—which tables to join, which fields to use, which records to exclude—every time they write a query.
Or worse, each analyst applies their own logic. One excludes cancelled orders; another doesn't. One calculates margin before freight; another includes it. The numbers diverge.
The fix: Build a proper transformation layer. Define business logic once, in code, with version control. "Revenue" means the same thing in every report because it's calculated the same way everywhere.
3. Stale data that nobody trusts
"The warehouse updates overnight" sounds reasonable until someone checks a dashboard at 3pm and sees yesterday's numbers.
Stale data destroys trust. Once users learn the warehouse is behind, they stop trusting it entirely. They go back to the ERP—even when the warehouse data is actually current—because they can't be sure.
The fix: Refresh frequency should match decision frequency. Strategic dashboards can update daily. Operational dashboards need hourly or real-time. Know the difference and build accordingly.
4. No ownership after launch
The consultants built it, handed over documentation, and left. The internal team that was supposed to own it has other priorities. Nobody's maintaining the pipelines, updating the logic, or fixing things when they break.
Data warehouses aren't projects—they're products. They need ongoing attention, iteration, and ownership.
The fix: Assign clear ownership. Someone's job is to keep the warehouse healthy, respond to issues, and evolve it as the business changes. If nobody owns it, it will rot.
5. Too much, too fast
Ambitious implementations try to boil the ocean. Every data source, every possible metric, every conceivable dashboard—all in the first phase.
These projects drag on for months. Scope creeps. By the time it launches, requirements have changed. Users have lost interest. The thing that finally ships doesn't match what anyone actually needs.
The fix: Start small. One data source, a few key metrics, for one team. Prove value, then expand. Iteration beats big-bang every time.
6. Wrong tool for the job
Sometimes the technology itself is the problem. A warehouse designed for batch analytics can't support real-time dashboards. A cloud platform might be overkill for a mid-sized business. An on-premise solution creates maintenance burden you can't sustain.
Or the BI tool on top is too complex for business users, so only the data team can build anything. This is a common reason why self-service BI never works in practice.
The fix: Match technology to actual requirements. Don't buy enterprise tools for mid-market problems. Don't cheap out on infrastructure when performance matters. Understanding the difference between data warehouses and data lakes helps you pick the right tool for your situation.
The Deeper Problem: No Data Strategy
Most failed warehouses share a root cause: there was no data strategy. There was a project—"implement a data warehouse"—but no clarity on why, for whom, or how it would actually be used.
A data strategy answers:
- What decisions should be data-driven that currently aren't?
- Who needs data access, and what kind? (Self-service vs. curated reports)
- What's the cost of bad data? (Wrong decisions, wasted time, missed opportunities)
- Who owns data quality and governance?
- How will we know if this is working?
Without these answers, you're building infrastructure without purpose. The warehouse becomes a solution looking for a problem.
How to Fix a Failing Data Warehouse
If your warehouse is underperforming, you have options. The right one depends on how broken things are.
If the foundation is sound but adoption is low:
The data is there, it's reasonably accurate, but nobody uses it.
Focus on quick wins. Find one team with a painful reporting problem. Build them something that saves hours of manual work. Make heroes out of early adopters. Momentum beats mandates.
Simplify access. If the BI tool is too complex, put a simpler layer on top. Pre-built dashboards that answer common questions. Guided analytics that don't require SQL.
Train and support. Users won't adopt what they don't understand. Invest in training—not "how to use the tool" but "how to answer your questions."
If the data quality is the problem:
The warehouse exists but the numbers are wrong or inconsistent.
Audit the transformation layer. Where does business logic live? Is it consistent? Are there definitions documented? Often the fix is standardising calculations that have drifted.
Reconcile to source. Pick a metric—revenue, order count, whatever matters most. Trace it from the warehouse back to the ERP. Find where it diverges. Fix that.
Fix one metric at a time. Don't try to fix everything. Get one number right, build trust, then expand.
If the architecture is wrong:
The warehouse can't do what you need—too slow, wrong granularity, missing sources.
This is harder. You may need to rebuild significant pieces. But you don't have to start from scratch.
Prioritise the highest-value fixes. What's the one thing that would make the biggest difference? Maybe it's adding real-time sync for one critical source. Maybe it's restructuring one fact table. Fix that first.
Plan for migration. If a full rebuild is needed, do it incrementally. Run old and new in parallel. Migrate users team by team. Don't do a big-bang cutover.
If it's unsalvageable:
Sometimes it's cheaper to start over than to fix what's broken.
This isn't failure—it's learning. You now know what questions matter, what data sources are critical, and what went wrong last time. The second implementation will be better.
Document what you learned. Why did it fail? What would you do differently? Make sure the next attempt doesn't repeat the same mistakes.
What a Working Data Warehouse Actually Looks Like
When it works, it's transformative:
People use it. Not because they're told to—because it's faster than the alternative. Checking the dashboard is easier than exporting to Excel.
The numbers are trusted. When someone cites a metric, nobody asks "where did you get that?" Everyone knows the answer came from the same source.
Self-service is real. Business users can answer their own questions. The data team focuses on hard problems, not ad-hoc report requests.
Decisions improve. Leadership stops debating numbers and starts debating strategy. Problems get caught earlier. Opportunities get spotted that were previously invisible.
It evolves. As the business changes, the warehouse changes with it. New questions get answered. New sources get added. It's a living system, not a static monument.
This isn't a fantasy. It's what happens when the implementation is done right—or when a broken implementation gets fixed. When data works, you stop paying the hidden cost of bad data that drains most organisations.
Questions to Ask About Your Warehouse
If you're not sure whether your warehouse is working:
- When was the last time someone made a decision based on data from the warehouse?
- How many people used the warehouse in the last week? The last month?
- If you asked for a number, would the warehouse, ERP, and finance team all agree?
- Can a business user answer a simple question without involving the data team?
- When something breaks, how long until someone notices? How long until it's fixed?
Honest answers tell you whether you have a data warehouse or just a data warehouse project that never quite landed.
The Path Forward
A failing data warehouse isn't a reason to give up on data. It's a reason to do data properly.
That might mean fixing what you have. It might mean rebuilding with lessons learned. It might mean engaging proper data infrastructure expertise to get it right this time.
What it doesn't mean is going back to spreadsheets and gut feel. The businesses that win are the ones that figure this out.
Data warehouse underperforming? Book a call with our team. We'll assess what you have, identify what's broken, and show you what "working" actually looks like.
