Offer 05 · Data & Intelligence
Dashboards your leadership team can act on, built on one source of truth
One source of truth, documented metric definitions, and pipelines that live in your own cloud: the base a dashboard or forecast needs before anyone can trust it.
MRR
$184,392
+6.2% MoM
Pipeline
$2.1M
+3 deals
Churn
1.8%
-0.3pt
CAC
$212
flat
Revenue, trailing 8 weeks
synced hourlySources: Stripe · Salesforce · warehouse — illustrative figures
What you get
Five things every business intelligence build includes
- 01
One source of truth
Every number on a dashboard traces back to one documented data model, not three exports that quietly drift apart by Friday.
- 02
One definition per metric
MRR, churn, and CAC mean the same thing in finance, product, and sales. Each definition is written down, reviewed, and signed off before anything is built on top of it.
- 03
Dashboards non-analysts can read
Built around the five questions leadership actually asks in a weekly review, with drill-downs that stop before they overwhelm.
- 04
Pipelines in your own cloud
Snowflake, BigQuery, or Postgres, whichever fits the shape of your data. The warehouse and the pipelines sit in your accounts from day one.
- 05
Predictive work only once the base is clean
Forecasting and scoring ship after reporting is trustworthy, not before. A forecast built on bad plumbing is a confident guess.
One number, not three
Every source agrees before a dashboard gets built
- Billing, CRM, and the warehouse read the same figure, every time.
- A documented definition sits behind every metric on the dashboard.
- Drill-downs stop at the segment that explains the number, not ten tables deeper.
Metric: monthly recurring revenue
3 of 3 sources agree- Stripe (billing)$184,392
- Salesforce (CRM)$184,392
- Warehouse (dbt model)$184,392
Drill down · by segment
| Enterprise | $96,110 | +4.1% |
| Mid-market | $61,820 | +8.4% |
| SMB | $26,462 | -1.2% |
Illustrative figures, one documented definition per metric
How we work
Four phases, each signed off before the next starts
- 01
Audit
Where the numbers come from today, where they disagree, and what leadership actually needs to see every week.
- 02
Model
A canonical data model with one definition per metric, documented and signed off before any dashboard is built.
- 03
Build
Pipelines, warehouse, semantic layer, and the dashboards on top. Tested against your real questions, not a sample set.
- 04
Operate
A monthly rhythm of definition changes, new questions, and the governance that keeps the whole thing trustworthy.
Questions buyers ask
Before you scope a project
- Do we need a data team already in place?
- No. We often build the first version of the data function alongside you. Where you already have analysts, we build the plumbing they need and leave the analysis to them.
- Which data warehouse do you use?
- Whichever fits the shape of your data and your cloud. Snowflake, BigQuery, and Postgres each have the right project. We pick per build and document why.
- How long until we have a working dashboard?
- A first dashboard on real data ships in 4 to 6 weeks once the canonical data model is signed off. The model itself usually takes 2 to 3 weeks, depending on how many source systems feed it.
- When should we add predictive analytics?
- After reporting is trustworthy. A forecast built on bad plumbing is a confident guess. We will say no to predictive work on a shaky base and fix the base first.