One number everyone in the business trusts.

Because those dashboards each tell a partial story from their own system's point of view, and when the numbers disagree, which they usually do, someone has to manually reconcile them before a decision gets made. This consulting practice builds a semantic layer across CRM, ERP and commerce systems so there's one number the whole business can trust, instead of three dashboards that each claim to be right.

It's a layer that defines what things mean consistently across systems, so “a customer,” “a sale” or “active user” is calculated the same way everywhere, instead of your CRM counting customers one way and your commerce platform counting them another. Without it, two accurate dashboards can show two different numbers for the same question.

Usually that happens because governance was missing, not because dashboards are inherently unreliable: no clear ownership of definitions, no refresh schedule, no access control, so numbers drift and nobody's sure which version is current. Step three is explicitly governance and rollout: defined ownership, refresh schedules and access control, so trust doesn't erode the second time either.

Both. Attribution and reporting are tied back to revenue specifically, not just channel-level activity, so marketing spend can be evaluated against what it actually generated in pipeline and closed deals, alongside the broader executive dashboards covering the rest of the business.

Power BI most often, connected to the underlying data layer, though the tool is chosen based on what your team already knows and what needs to integrate with it. The harder and more valuable part of the work is the data modelling underneath, not which visualisation tool sits on top.

It depends on how reliable your existing data already is. If CRM, ERP and commerce data are already integrated and reasonably clean, we can build the semantic layer and dashboards directly. If the data audit in step one finds the underlying systems don't agree with each other, that gets fixed first. A dashboard built on unreliable data just makes bad numbers look official.