Why the reporting habits that worked at $5M in revenue break down at $20M — and what to build instead.
Why it matters
The gap between having data and being able to act on it is usually a pipeline and governance problem, not an analytics problem. Fix the plumbing and the insights follow.
What this looks like in practice
- Data prepared properly before any model gets trained on it
- Clear ownership and access controls so the audit isn't a scramble
- Production ML that ships, not a notebook that never leaves someone's laptop
- Pipelines built to survive schema changes without breaking
Where teams get stuck
Teams often invest in a shiny BI tool before fixing the pipeline feeding it. Better dashboards on top of unreliable data just make bad numbers look more official.
How Ndakum approaches it
This is the kind of problem our Data Engineering & AI work is built around. We start by mapping how the work actually happens today, design a solution scoped to your systems and data, and stay through rollout so it's your team's tool from day one — not ours.
Curious whether this fits your business?
A short conversation will tell us both. No pressure, no obligation.
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