The problem usually isn’t a lack of data — it’s a lack of data anyone trusts. CAC figures conflict between the finance model and the marketing dashboard. Pipeline velocity means something different to the CRO than it does to RevOps. Channel ROI is disputed in every QBR because attribution is broken or undefined. In that environment, decisions get made on gut feel, and spend grows in directions that are hard to defend or reverse. AI needs data — it’s its fuel — and without a clean, agreed analytics layer, AI initiatives stall at the “we should try this” stage.
We build the reporting infrastructure the executive team can actually use: KPI alignment workshops to get one agreed definition of each metric, then dashboard builds wired to CRM reality rather than vanity analytics. Attribution views tie channel spend back through to closed revenue, not just last-touch clicks. Automated reporting replaces the weekly spreadsheet pull. We hand over the dashboard, the KPI dictionary, and the reporting cadence — not a Looker instance only we know how to maintain.
The design work starts before any dashboard is built. A KPI dictionary session surfaces every metric that appears in QBR decks or exec readouts and forces a decision on each one: what it measures exactly, which system is the source of truth, how it is calculated, and who owns it. Conflicts between systems — where the CRM says one number and the ad platform says another — are resolved by mapping the attribution model, not by averaging the two figures. Only once that dictionary exists do we connect data sources and build the views. Dashboards built without this step answer the wrong question with confidence, which is worse than no dashboard.
Access requirements include read permissions across your CRM, ad platforms, and any data warehouse or BI tool in use. During the build phase, we map data flows and document what fields from each source feed which metric. If PII is present in pipeline records — contact names inside deal data, for instance — we scope the dashboard queries to exclude it at the field level, so the reporting layer does not become a PII exposure surface. The KPI dictionary and data-source mapping document become part of the handover package and serve as the audit record for how each number is produced.
There are cases where a custom analytics build is not warranted. If your CRM vendor’s native reporting covers your KPI set and your team is small enough that one person holds the attribution model in their head, the maintenance cost of a custom layer is hard to justify. HubSpot’s native reporting is adequate for a large number of sub-50-person B2B teams; Salesforce’s built-in dashboards cover more ground than most implementations use. We will say so if the audit reveals this. Vendor-independence means recommending the simpler path when it genuinely fits.
Operational ownership after handover is straightforward but needs to be assigned explicitly. The KPI dictionary owner — usually a RevOps lead or a finance analyst — is responsible for updating metric definitions when the business model changes. The dashboard owner monitors data freshness alerts and escalates when source connections break. We set up automated freshness checks that fire an alert if a data source stops updating; silent staleness is the most common failure mode, and it tends to surface at the worst possible moment — mid-QBR. A quarterly review of the attribution model is worth scheduling at handover, because channel mix shifts and the attribution logic needs to reflect it.