For CEOs
Data for CEOs: the no-jargon guide
Everything a CEO needs to know about data to make better decisions, without the technical complexity: what to ask for, what to measure and how to get results.
Read articleKey takeaways
- Starting from technology instead of the use case is the most expensive mistake.
- Oversizing infrastructure inflates cost from day one.
- Ignoring governance leads to wrong decisions and legal risk.
- Not measuring ROI leaves the project undefended.
- The pattern: technology before business.
Many data projects fail not for lack of technology, but for the wrong approach. These are the seven most expensive mistakes we see again and again, and how to avoid them.
The seven mistakes
- Starting from technology: "we need a data lake" is starting from the end.
- Oversizing infrastructure: buying capacity "just in case".
- Ignoring governance: numbers nobody trusts, legal risk.
- Underestimating maintenance: it is never "finished".
- Not measuring ROI: no way to defend or prioritise.
- Doing everything at once: "big bang" projects accumulate risk.
- Building everything in-house without needing to.
The pattern behind almost every mistake
If you look at the seven, they share a root: putting technology ahead of the business. When the start is "which tool do we set up" instead of "which decision do we want to improve", the project grows in complexity and cost without getting closer to value.
Technology first
Complexitygrows
Reverse it
Businessquestion first
Result
ScopedMeasured
How to rescue a project going wrong
- Return to the use case: a concrete decision to improve.
- Scope it down: deliver a small, useful first result.
- Measure: tie it to a business metric.
- Reduce your own load: outsource the non-differentiating operation.
The biggest mistake is not choosing the wrong technology, but starting from it instead of the business.
In summary
Data platforms fail from approach, not technology: starting from the tool, oversizing, ignoring governance, underestimating maintenance, not measuring ROI, doing everything at once and building in-house unnecessarily. The fix is to reverse the order — business question first — and deliver scoped, measured results.
Sources & further reading
Frequently asked questions
What is the most frequent mistake?
Starting from technology instead of a concrete, measurable business use case.
How do I reduce project risk?
By starting scoped, measuring ROI from the start and relying on a provider that already has platform and team ready.
Is it better to build or outsource?
For most, outsourcing with a managed service reduces cost, time and risk. Building in-house only pays off when data is the core of the product.
Why is oversizing a problem?
It locks up budget in capacity "just in case" and inflates cost from day one, before any value is delivered.
How do I rescue a failing project?
Return to a concrete use case, scope it down to a small useful result, measure its business impact, and outsource the non-differentiating operation.
What links all the mistakes?
Putting technology ahead of the business. Reversing that order — business question first — prevents most of them.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.