BI tools: buy, build, or somewhere in between

The buy-versus-build decision for BI tooling isn't binary in practice — most companies end up somewhere in between, and knowing where to draw that line saves real time and money.

What "buy" actually gets you, and where it falls short

Off-the-shelf BI tools (Looker, Tableau, Power BI) genuinely excel at standard business reporting — sales dashboards, financial reporting, operational metrics that follow common patterns these tools were built for. They fall short when your reporting needs something genuinely custom to your business logic that doesn't map cleanly onto the tool's standard visualization and modeling patterns, or when you need reporting embedded directly into a customer-facing product rather than an internal dashboard.

What "build" actually requires, honestly

A fully custom BI layer — typically a combination of a data warehouse, a transformation layer (often dbt), and custom visualization — gives you complete flexibility but requires ongoing engineering investment that a buy-first tool doesn't. We're honest with clients that full custom builds are usually only justified when the reporting need is core to the product itself (customer-facing analytics as a feature) or when standard tools genuinely can't model your business logic, not as a default preference for control.

The "in between" that actually fits most companies

The pattern we build most often: a solid data warehouse and transformation layer as the foundation (this part is usually worth building or properly configuring regardless of your visualization choice), paired with an off-the-shelf BI tool on top for the actual dashboards and exploration. This gets you clean, well-modeled data — the hard part — while avoiding custom visualization engineering for problems Looker or Tableau already solve well. The warehouse and transformation layer is also portable if you later switch visualization tools, whereas the reverse isn't true.

A concrete example

A logistics client came to us wanting a fully custom-built dashboard platform, estimating it as a multi-month project. Our assessment found their actual reporting needs — fleet utilization, delivery time tracking, cost-per-route analysis — mapped well onto standard BI patterns; what was actually missing was clean, well-modeled underlying data, since their metrics were being calculated inconsistently across three different spreadsheets maintained by different people. We built a proper data warehouse with a dbt transformation layer establishing single, tested definitions for each metric, then implemented Looker on top. Total timeline was about 6 weeks rather than the originally estimated several months, and — more importantly — the three previously inconsistent metric definitions became one trusted, tested calculation that multiple dashboards could reference reliably.

The decision framework we actually use

We ask: is this reporting need something a standard BI tool's visualization model can represent (most operational and financial reporting genuinely is), or is the underlying data problem the actual bottleneck (very often true)? If it's the latter — which we find more often than clients expect — the right investment is in the data layer, not custom dashboard engineering, and a standard tool on top will serve you well once the data underneath it is trustworthy.

How Ndakum approaches it

Our Data Engineering & AI work usually starts by assessing whether your bottleneck is the data layer or the visualization layer — they require very different investments, and we won't recommend a costly custom build when the real fix is cleaner underlying data.