Ongoing support should not make you dependent on the partner; it should do the opposite. A proper data function is built to leave your own team more capable over time, with documentation they can read, models they can extend and decisions explained rather than hidden. The aim is that you could take it in house if you wanted to.
For a deeper look, see our guide to Where a data partner adds value alongside a team that already knows what it's doing.
Ask what designation they actually hold, not which logo is on the slide. A Microsoft Solutions Partner has had to prove skilling, delivery and customer success. Then ask to speak to the engineers who would do your work, rather than the ones who turn up to win it.
For the full picture, see Where a data partner adds value alongside a team that already knows what it's doing.
Yes. Fabric and Power BI bill on consumption, so a design that looks cheap to build can be expensive to run. A good partner talks to you about capacity, refresh patterns and what your estate will cost to operate once it is busy. If nobody in the pitch mentions running cost, that is the bill you find later.
There is more detail in Azure data engineering: what to hand a consultancy, what to keep, and what it should cost.
The Analytics Acceleration Programme moves through establishing the foundations, building what the business needs, and a continuity phase that keeps everything healthy and evolving. It comes in tiers, so a small team and a large estate can both have a version that fits, and the honest-broker rule applies throughout: if the data cannot support a conclusion, we say so.
We go into this further in Analytics Is A Garden, Not A Project.
The single most useful question to ask a prospective partner is who understands the solution when the project ends. If the honest answer is only the partner, you have rented a capability rather than bought one, and you will be back next year paying to keep the lights on. The partners worth having build so your own team can run and extend the work.
We go into this further in Where a data partner adds value alongside a team that already knows what it's doing.
An ongoing data function beats a one-off project for three reasons. Continuity of knowledge, because the person who fixes your refresh in month nine is the one who built it. Small and early beats big and late, because a standing function catches drift while it is a ten-minute fix. And the economics are better, because keeping an asset healthy costs less than the build, drift and rebuild cycle.
Related reading: Azure data engineering: what to hand a consultancy, what to keep, and what it should cost.
Still have questions?
Can’t find what you’re looking for?
The first conversation is exploratory and carries no obligation. We’ll give you an honest answer to any question you have.
Book a free audit