When two people pull the same metric from the same dashboard and get different numbers, the data model is broken. When every analysis requires an engineer to write a query from scratch, the team is undersized for the volume of decisions being made. When KPI definitions shift between board packs, leadership is navigating by feel.
We build the foundational layer that makes reliable, self-serve reporting possible: canonical data models, agreed metric definitions, transformation pipelines with testing and CI/CD, and the documentation that lets analysts work with confidence.
Note: we don't do dashboarding as a standalone service. BI tools and dashboards are the visible deliverable of this engagement — the underlying work is what makes them worth having.
What we deliver
Select a service area to see how we approach it.
Build a shared data layer your whole organisation can rely on.
- 01
Align with stakeholders on the entities and metrics that matter most to the business.
- 02
Audit raw source data for grain, completeness, and known quality issues.
- 03
Design the dimensional model with clear layer separation: raw, staging, and final.
- 04
Implement in dbt with schema tests, freshness checks, and inline documentation.
- 05
Validate outputs against known business numbers before handing over to the analytics team.
Ensure every team measures success the same way, always.
- 01
Run stakeholder workshops to surface all competing definitions of each key metric.
- 02
Identify the root cause of each discrepancy: model grain, filter logic, or missing joins.
- 03
Agree on canonical definitions with sign-off from finance, product, and growth.
- 04
Encode definitions in the transformation layer and expose them via the semantic layer.
- 05
Document the governance process that keeps definitions stable as the business evolves.
Ship data changes confidently with automated testing and monitoring.
- 01
Audit existing pipelines for failure modes: missing tests, no alerting, undocumented dependencies.
- 02
Implement dbt schema tests and custom data quality checks at each model layer.
- 03
Configure freshness monitors and anomaly alerts to catch silent failures.
- 04
Build the CI/CD pipeline so all transformation changes are tested before merging.
- 05
Document runbooks for the most common failure modes so on-call engineers can resolve them.
Give leadership the numbers they need, when they need them.
- 01
Agree on the metrics that belong in leadership reporting — and the ones that don't.
- 02
Verify that each metric has a canonical, agreed-upon definition in the transformation layer.
- 03
Design the reporting structure: cadence, format, and level of detail for each audience.
- 04
Build the dashboards and automated exports on top of the canonical models.
- 05
Document data lineage so any number can be traced back to source and explained to a stakeholder.
North star metrics that everyone in the company agrees on. Analysts spending time on analysis, not data wrangling. A reporting foundation that scales as the company grows.
Founders and Heads of Data/Analytics at companies where reporting is slow, inconsistent, or distrusted across teams.
We publish in-depth playbooks on data engineering best practices at handbook.bottomlinedata.co. Detailed guides related to this practice area will be linked here.
Start a conversation.
Every engagement begins with a focused discussion of your current data environment and priorities. To schedule an initial consultation, reach out directly.
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