How We Turned a 3-Week Data Queue Into 30 Minutes
146 people across the business waited 3 weeks for a small data team to answer simple operational questions. Thousands of orphaned dashboards. Nobody trusted any of them.
[Redacted | Global Enterprise]
6,100/yr
Productivity Unlocked
£440K
Cost Saved
Tech Stack
Before & After
Data Request Turnaround
Team Access
The Gap
Over the years, thousands of dashboards had been created by various employees, many of whom had since left the company. The result was a graveyard of reports nobody trusted.
146 people across Marketing, Supply Chain, and Regional Management needed answers to simple operational questions. But they couldn’t get them without asking a small, overwhelmed data engineering team. A routine data request took up to three weeks. The data scientists were spending their expensive time on basic reporting instead of the strategic work they were hired for.
The data science team was drowning in the wrong work. They’d been hired to build predictive models and surface strategic insights. Instead, they were spending 60–70% of their time on data extraction, dashboard maintenance, and fielding questions that should have been self-service. It was an expensive bottleneck. Not because the team was underperforming, but because there was no other way to get answers.
The Build
The solution wasn’t more dashboards. It was eliminating the need for them.
We consolidated their fragmented data sources into a unified Microsoft Fabric workspace. Then we built a self-service query tool using Fabric Data Agents: a secure, internal chatbot that understood the company’s specific manufacturing terminology.
We spent most of our time on the boring bit. Documenting and mapping the existing data so the AI could actually understand what it was looking at. Anyone could ask a question in plain English and get an answer in minutes. No SQL. No tickets. No waiting.
The documentation phase was the most critical, and the most overlooked. Before any AI could answer questions accurately, we needed to map the entire data estate: what existed, what it meant, where it lived, and how it related to other tables. We spent three weeks on this before writing a line of AI code. That investment is why the system gives accurate answers instead of confident hallucinations.
The Result
We removed the human bottleneck entirely.
Speed: Data answers went from 3 weeks to under 30 minutes.
Access: 146 team members, from Marketing to Supply Chain, could now query the system in plain English. The data-literate ones still built their own charts. Everyone else just asked questions.
Capacity: 6,100+ hours per year freed across the organisation, roughly £440K in saved capacity. The data science team went back to doing actual data science.
Consistency: One workspace, one source of truth. The “which version is right?” problem disappeared overnight.
Strategic Impact: With the data team freed from reporting duties, they moved to predictive modelling, demand forecasting, and supply chain optimisation. The hours saved were one thing. The bigger win was giving them back the work they were actually hired for.
The Bottom Line
A 3-week waiting list became a 30-minute self-service query. 146 people who used to file tickets can now ask the data a question directly. The data team got their time back. The business got answers.
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