We build the measurement architecture, attribution models, and reporting infrastructure that turn the numbers your business generates into the decisions your leadership needs to make.
Most mid-market businesses have reporting. Very few have data they trust. The dashboards exist but reconcile differently depending on who built them. Attribution credits channels that didn't earn it. CAC is calculated three ways and nobody agrees on the number. Leadership makes decisions on data they've learned to distrust.
We build the infrastructure layer beneath the reports. The pipelines, definitions, and attribution models that make the numbers consistent. Then we build the reporting layer on top, for the people who actually make decisions with it, not for the people who build dashboards.
This is the right pillar if:
Diagnose, build, then operate. Phases are gated, not dated. We earn the next one before moving.
Data quality audit: what the business actually has, what it's collecting, what it's missing, and where the numbers in the current reports are wrong. Most businesses are surprised by the last part.
Analytics instrumentation, attribution modeling, and the data pipelines that make reporting consistent and trustworthy across every system in the stack.
BI dashboards, executive reporting, and the operating rhythm to use them. Data that lives in a portal nobody opens is not an asset. We build for adoption.
For the marketing team that can't answer 'which channel drove that deal.' Multi-touch attribution, channel tracking, and the dashboard to run on.
One source of truth for leadership. Automated weekly and monthly reporting that gives the board and exec team the metrics they actually need to run the business.
Data strategy through governance. The full measurement architecture for a business that wants to make decisions from data, not from whoever built the last spreadsheet.
One pillar, one URL. Systems are sections on this page, not a separate Solutions catalogue. The scope is set against the diagnostic, so you buy the system the business needs, not a fixed package off a shelf.
Pillar-specific questions. The Services page covers how the firm engages overall.
With the audit. In our experience, businesses with 'a lot of data' typically have fragmented data: numbers that don't reconcile between systems, attribution that overcounts or undercounts, and definitions that vary by team. The assessment identifies where the data is trustworthy and where it isn't, so we build on a clean foundation rather than a fast one.
We work in your tools: Looker, Tableau, Power BI, or whatever your team uses. If you don't have a BI tool yet, we help you select the right one for your scale and build the initial reporting layer in it. We don't create dependency on proprietary dashboards.
The signal is usually a disagreement: marketing reports lead volume, sales reports pipeline health, and leadership is trying to reconcile two numbers that don't match. Or CAC is high and nobody agrees on what's driving it. When the same business event looks different in three different reports, the attribution is wrong.
A clean baseline, data audit, pipeline connections, and a core dashboard set, typically takes eight to twelve weeks. The complexity driver is usually the number of systems that need to connect, not the dashboard design. Attribution modeling for a multi-channel business takes longer and runs alongside implementation.
The data audit surfaces which reports are telling the truth and which aren't. Most leadership teams are surprised which is which.
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