Tactical Marketing
Dashboard & Reporting · Data & Reporting Integrity

Marketing dashboards that hold up because the data underneath them does.

A dashboard is a presentation layer on top of decisions about what to count.

The dashboard is the visible artifact. The work is the decisions about what counts as a conversion, which lifecycle moves are real, and how multi-system records get reconciled into a single number.

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Where dashboards usually break

Most marketing dashboards we audit are reporting numbers that look right but don't reconcile against any other source. The HubSpot dashboard says one MQL count, Salesforce says another, the ad platform attributes a different number to the same campaign, and the conversation in the QBR becomes about reconciling them rather than acting on them.

80%
of analysts' time spent finding and cleaning data, not analyzing it
Forbes/CrowdFlower, 2016
23×
more likely to acquire customers at data-driven organizations
McKinsey Global Institute

The fix is upstream. Reconcile the underlying data first. Establish single sources of truth per metric. Build the dashboard against those sources, not against whatever the platform's default surface presents. That upstream work is where the real effort lives: analysts spend roughly 80% of their time finding, cleaning, and preparing data rather than analyzing it (Forbes/CrowdFlower, 2016), and the payoff for getting it right is large — data-driven organizations are 23x more likely to acquire customers than their peers (McKinsey Global Institute).

We do this in HubSpot, Salesforce, Looker, Tableau, Power BI, Domo, Sigma, and increasingly the warehouse-native BI patterns where the warehouse is the source of truth and the dashboard tool is a thin layer on top.

The dashboard build sequence

Run in this order; skipping ahead produces dashboards nobody trusts.

  1. 1
    Question definition
    What decisions does this dashboard inform? Build for the decision, not for the data that happens to be available.
  2. 2
    Source of truth selection
    For every metric on the dashboard, name the source. If two sources disagree, pick one and document why.
  3. 3
    Data layer reconciliation
    Fix the upstream sync, segmentation, or attribution issues that caused the discrepancy. Don't paper over it in the report layer.
  4. 4
    Metric definition
    Written, in the documentation, with the calculation logic and the edge cases. New team members shouldn't have to derive what the metric means.
  5. 5
    Build
    Dashboard built against the agreed sources, with the metric definitions surfaced inline.
  6. 6
    Cadence and ownership
    Who owns the report. Who watches the data behind it. What happens when something looks off.

Matching services

See the same work from the platform and delivery angle.

These service pages cover scope, approach, and what an engagement actually delivers.

Business Intelligence →Act-On Data Studio →

Frequently asked questions

QShould our marketing dashboards live in HubSpot/Salesforce or in a BI tool?+
BI tool when you need to combine multiple sources or when the audience is broader than the platform users. Native when the audience is the team operating the platform and the data already lives there.
QLooker, Tableau, or Power BI?+
Looker for warehouse-native organizations on Snowflake/BigQuery. Tableau for organizations with strong analyst teams. Power BI for Microsoft-native shops. None is wrong; the fit is contextual.
QWhat about HubSpot's native reporting?+
Strong for marketing-only views. Limits show up when you need to combine HubSpot with non-HubSpot data; that's when you graduate to a BI tool.
QDo you do executive dashboards or operational dashboards?+
Both. The two have different audiences and different cadences. Most engagements include both.
QCan you replace our agency reporting?+
Yes. Agency reporting is usually decoupled from the operational platform; we build the version that lives where the team makes decisions.
QHow long does a dashboard project take?+
Single dashboards run two to four weeks. A reporting platform rebuild runs six to twelve weeks.
QDo you do dbt and warehouse modeling?+
Yes when the warehouse is the source of truth. The dashboard is downstream of the model; the model is where the trust is built.
QWhat about real-time dashboards?+
Possible. Usually unnecessary: most marketing decisions don't move at the cadence that justifies real-time, and the engineering cost is non-trivial.

Related services

Funnel Analytics & Attribution
A measurement model the executive team will actually believe.
RevOps Consulting
Aligning marketing, sales, and CS so revenue stops leaking between teams.
Marketing Operations Consulting
The senior layer that owns process, governance, and operational standards.
CRM Data Cleanup
Restoring the data layer reporting and segmentation depend on.
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