Two departments, two numbers, same question

Because unused dashboards are nearly always a symptom rather than the problem. Either the numbers disagree with another report and people stopped trusting them, or the dashboard shows a general picture instead of the specific thing someone is accountable for. We start from the decisions being made, agree the definitions behind them so the figures reconcile, and track which reports get opened. Anything still unopened after a month gets retired — a smaller set of trusted reports beats a large catalogue nobody believes.

You can report directly for a while, and many companies should. It stops working when analytical queries start affecting application performance, when a question needs data from two systems at once, or when you need history that the production database overwrites. That is the point at which a warehouse pays for itself. A managed warehouse for a mid-sized company is a modest monthly cost, and considerably cheaper than the analyst time currently spent joining exports in spreadsheets.

First useful reports typically land four to six weeks in, covering one domain — sales, finance or operations — end to end. A complete platform across several source systems, with quality testing, governance and enablement, is more like three to six months. We deliberately sequence it domain by domain so something is in use early, rather than building the whole warehouse before anyone sees a number.

Data quality, almost every time. Source systems have optional fields that turned out to be mandatory in practice, customer records duplicated across regions, historical data that changed meaning when a process was updated three years ago. Discovering this is unavoidable, so we look for it during the assessment rather than during a board presentation. The second most common cause is a source system changing shape without warning, which is why we put contracts and freshness tests in place so a load fails loudly instead of publishing incomplete figures.

Yes, provided there is a semantic layer underneath it. Natural-language querying works well when the model knows what active customer means, which table holds it, and how the tables join — because that turns a vague question into a constrained query against agreed definitions. Point the same tool at a raw warehouse and it will generate confident SQL against the wrong table and return a number no one can check. The interface is the easy part; the definitions are the work.

You do. It runs in your cloud account, the transformation code sits in your repository, the dashboards are in your BI tool, and there is nothing proprietary of ours in the middle. We build with your analysts throughout and run handover sessions on the pipelines, the tests and the release process. Most clients keep us on a light retainer for new sources and periodic review, which is a choice rather than a dependency.