Business intelligence and dashboard development services.

Usually yes, and that is where we prefer to start. A review covers grain, relationships, calculated columns, duplicated measures, storage mode and refresh behaviour, and produces a written list of what to fix in place and what genuinely needs rebuilding. A rebuild is something we have to justify, not a starting assumption.

It is the layer between your warehouse and your dashboards that holds tables, relationships and agreed measures. When it exists, every report calculates revenue the same way. When it does not, each dashboard re-implements the logic and the numbers drift apart. It also lets analysts build their own views without writing joins.

Almost always upstream of the dashboard. The usual causes are different filters, a different grain, different date logic or a metric defined twice. We trace each figure back to its source, agree one definition per metric with a named owner, and move the calculation into the model so a report is no longer the place the logic lives.

Whichever one your team will maintain. Power BI suits organisations already in Microsoft 365 that need a governed semantic layer; Tableau suits heavy visual exploration; Looker Studio is reasonable for lighter reporting over Google Cloud data; Streamlit fits when the output is really an interactive Python tool. We would rather improve the tool you have than sell you a migration.

Not always, but usually. A semantic model can read an operational database for a while and sometimes that is the right call. Once reports need history, several sources, or a grain the source system does not store, a curated warehouse layer costs less than repeatedly patching reports. We can tell you which situation you are in after looking at the sources.

The fix is to reduce what the report has to read rather than importing less data as a workaround. That means partitioning, incremental refresh, aggregation tables, pushing transformation back into the warehouse and removing columns nothing uses. On past work this has reduced query scan sizes by 60 to 75% and moved refreshes from hours to minutes.

We quote after a scoping call. A useful first project is one dashboard rebuilt on an agreed grain and reconciled against the figures people already trust, and further dashboards and a wider semantic layer are scoped once that first one is agreed. MV.tech works remotely from Ahmedabad, India, with working hours planned around your timezone and a lean model that avoids large-consultancy overhead.