Most BI projects fail before the first useful dashboard. Three reasons from real projects, including a Bangladeshi e-commerce company that spent six months on a dashboard nobody used.
When leaders decide they want dashboards, in Power BI, Tableau or a free tool like Apache Superset, the first move is almost always to hire a BI developer. In my experience, that is exactly where most BI projects start to fail.
A dashboard only has value when business leaders trust the numbers and act on them. That trust needs reliable data pipelines, consistent processing, tested BI logic and agreed KPI definitions. A BI developer alone rarely gets the time or support to build all of that.
Reason 1: the BI developer is thrown into the ocean
A new BI developer is expected to analyse the business, find and understand every data source, clean the data and build the pipelines, which is data engineering work, before building a single dashboard. A solo developer can spend five to six months on this before dashboard development even starts.
That usually ends one of two ways. Leaders see months with no visible output, decide BI is a waste of time and close the project or let the developer go. Or the developer, under pressure, builds dashboards on static data and produces reports on demand. That is ad-hoc reporting, not BI.
A real example
A well-known Bangladeshi e-commerce company, in business for well over a decade, wanted BI but had not budgeted for it. The project went to a front-end developer and a data analyst. The business processes already ran on Excel.
After six months, they launched a free web-based dashboard with live graphs and charts on a fixed set of KPIs. Some of the KPIs were useful. Some were not.
Underneath, the data problem was never solved. The sources were never digitised or automated, so the dashboard depended on other teams emailing updated Excel files on a regular basis.
When the dashboard rolled out, several business leaders found the colourful KPIs useless. Some of them had real potential, but resistance to change and to working from data meant the reports were dismissed and the business went back to manual Excel. The project was closed before a live Power BI version was ever built.
The lasting damage is the lesson the company took from it: its leaders now believe BI is useless everywhere. The dashboard was never the real failure. The missing data foundation and the missing buy-in were.
Reason 2: more KPIs instead of the right ones
Even when the data side is handled, BI developers and leaders often spend more time adding KPIs and metrics than solving the actual business need. The dashboard grows, and later nobody looks at it.
One business-critical number that leaders check and act on is worth more than a hundred fancy pie charts.
Reason 3: leaders who do not act on the numbers
A capable data or BI team can build a useful, accurate dashboard and it can still be useless. If business leaders are not interested in using data, and nobody takes action based on what the dashboard shows, the project delivers nothing. In the example above, this was the final reason the project died.
What a BI project needs before the first chart
- Data engineering treated as its own workstream, not handed to the BI developer as a side task.
- Automated, reliable data pipelines, so dashboards never depend on someone emailing a spreadsheet.
- Agreed KPI definitions and BI logic that everyone signs off on before anything is visualised.
- A short list of business-critical numbers, rather than every metric that can be calculated.
- Leaders who commit to reviewing those numbers and acting on them.
How we approach BI at EXCO
We separate the data foundation from the dashboard work. Pipelines, data quality and KPI definitions come first, so the numbers can be trusted when the first dashboard goes live. We work across Power BI, Tableau, Apache Superset and other industry-standard tools, because the tool matters far less than the data underneath it.
That governed data foundation is also what makes AI possible later. Companies that build their data backbone properly for BI today are the ones ready for AI tomorrow.