ANALYTICS & DECISION INTELLIGENCE
Give every decision the same trusted version of the business.
We connect governed data, shared metric definitions and fit-for-purpose analytics so teams can explain a number, trust it and act on it, without rebuilding the logic in every report.
Reporting → Trusted decisions
Does this sound familiar?
If several of these are already true, this is the right conversation. If none of them are, it probably is not.
- Executives receive different answers to the same business question.
- Analysts spend more time reconciling metrics than interpreting them.
- Critical KPI logic lives inside individual dashboards or spreadsheets.
- Self-service has produced more reports, but not more consistency.
- Nobody can easily trace a metric back to its source and its definition.
- Analytics and AI teams inherit the same semantic ambiguity.
What it costs you
- The same measure is defined differently in different tools
- Meetings begin by reconciling dashboards instead of deciding
- Decisions are delayed, and confidence in the numbers drops
- Teams duplicate analytics effort to produce a version they trust
- AI and decision automation inherit the ambiguity and scale it
The problem is almost never the BI tool. It is that a definition was never agreed, so every tool encodes a slightly different one and the disagreement surfaces in the meeting rather than in the design.
What materially changes
| Today | With PaWa |
|---|---|
| Dashboard-specific KPI logic | Governed shared metric definitions |
| Multiple versions of truth | Named business definitions with named owners |
| Manual reconciliation | A consistent semantic and metric layer |
| Opaque calculations | Navigable lineage from metric to source |
| Report proliferation | Purpose-built decision products |
| Analyst dependency | Governed self-service with clear boundaries |
Today
Dashboard-specific KPI logic
With PaWa
Governed shared metric definitions
Today
Multiple versions of truth
With PaWa
Named business definitions with named owners
Today
Manual reconciliation
With PaWa
A consistent semantic and metric layer
Today
Opaque calculations
With PaWa
Navigable lineage from metric to source
Today
Report proliferation
With PaWa
Purpose-built decision products
Today
Analyst dependency
With PaWa
Governed self-service with clear boundaries
How we solve it
Mechanisms, not adjectives. Each of these is something we build, document and hand over.
Metric inventory and rationalisation
Find every place a measure is defined, and how many of those definitions actually differ. The overlap is usually smaller and more contested than anyone expects.
Business glossary and ownership
Definitions tied to analytics, with one accountable owner per term rather than a committee. Shared ownership of a definition is precisely how two teams end up with two numbers.
Semantic and metrics-layer architecture
The agreed definition encoded once, so every tool inherits it instead of reimplementing it. When the definition changes it changes in one place and every consumer follows.
Data product and curated model design
Curated models built on governed sources and master data, so a per-customer figure is not silently counting one customer three times.
Lineage from source to reported measure
The derivation path for a published number becomes navigable rather than reconstructed, which is what makes a figure explainable under challenge.
Quality rules on critical measures
Data quality rules attached to the measures that matter, defined under the Governance & MDM practice and enforced where the metric is produced.
Report rationalisation and governed self-service
Retiring what nobody uses, certifying what people depend on, and giving analysts a clear path to build without letting definitions drift again.
Decision workflow design
What action a metric should enable, by whom, and when. A dashboard with no decision attached is a maintenance liability rather than an asset.
Reference architecture
Governed sources and master data — customer, product and reference domains from the Governance & MDM practice — feed curated data products. A semantic and metrics layer sits above them, defining each measure exactly once, and BI tools, embedded analytics and data apps consume that layer rather than reimplementing the logic. That is what stops two tools from disagreeing. Those products attach to specific decision workflows, so a report has a named owner and a cadence. Definitions, ownership, quality, lineage, access and observability span the whole path, so anyone looking at a number can find out what it means and who is accountable for it.
1Governed sources & master data
- Curated source data
- Mastered entities
- Reference domains
2Curated data products
- Modelled datasets
- Conformed dimensions
- Certified tables
3Semantic / metrics layer
- Metric definitions
- Business logic
- Certified measures
4BI, embedded analytics & data apps
- Dashboards
- Reports
- Embedded analytics
- Data apps
5Decision workflows
- Owners and cadence
- Operational reviews
- Alerts and thresholds
Across the whole flow
Governance: definitions and ownership · Data quality on critical measures · Lineage from metric to source · Access control · Observability and freshness status
What you receive
Written artifacts you keep and can act on with any firm, including without us.
- Metric and KPI inventory, with the conflicts and ownership gaps named
- Canonical metric definitions and the semantic model that encodes them
- Target analytics architecture
- Priority dashboards and data products redesigned around the decisions they support
- Lineage and quality controls for the critical measures
- Report rationalisation backlog: what is retired, what is kept, and why
- Self-service governance rules and enablement material for your analysts
- A route for a new metric to become official rather than proliferate
How an engagement runs
- 1
Discover
Find where the same measure is defined more than once, and which decisions actually depend on it.
- 2
Design
Facilitate agreement on definitions and ownership. Where two areas genuinely need different measures, we name them differently rather than pretending one is wrong.
- 3
Deliver
Build the semantic layer and rebuild the priority reporting on it, in parallel with what exists so the numbers can be compared before anything is switched off.
- 4
Enable
Hand over the definition change process and the certification path, so the next metric does not start another divergence.
Relevant experience
Each item below is labelled with what kind of evidence it is. Nothing here claims a client outcome we cannot support.
Making regulatory figures explainable
A Canadian financial institution producing regulatory numbers on time but unable to demonstrate how a given figure had been derived. Each reported measure was traced through its transformations back to source, and the owning business definition captured with a named owner. Quality rules were attached to the definitions themselves, so a breach named a person rather than a table. Where two business areas disagreed on a definition, the disagreement surfaced during the work rather than during a review — which is the cheaper of the two moments to find it.
Delivered by our principal in a previous role, before PaWa Data Solutions.
Representative pattern: two teams, two numbers
Dashboards exist and are not trusted, because finance and operations calculate the same measure differently and neither definition is written down. The work surfaces the disagreement rather than averaging it, then fixes the definition once in a semantic layer and rebuilds the reporting on top.
What the engagement leaves behind: agreed metric definitions with owners, a semantic layer that enforces them, and a rationalisation list with reasoning.
Representative engagement — a realistic pattern used to explain our approach, not a client result.
Technology experience
Semantic and metrics layers
- dbt Semantic Layer
- Cube
- LookML
- Power BI models
BI and visualisation
- Power BI
- Tableau
- Looker
- Metabase
Governance and catalog
- Collibra
- Microsoft Purview
- Alation
- OpenMetadata
Platform
- Snowflake
- Databricks
- BigQuery
- Azure Synapse
Platforms and tools we have worked with directly. This is experience, not a partnership claim: PaWa Data Solutions holds no reseller agreement or partner status with any vendor listed here, which is what keeps the recommendation neutral.
Papa S. Nguer
VP of Technical Sales & Engineering, PaWa Data Solutions
Analytics work is led by our principal, whose background is governance, lineage and mastered data for Tier 1 financial institutions. That matters here because most analytics trust problems are definition and entity problems that have surfaced in a dashboard.
Full profileQuestions buyers actually ask
Do we need to change BI tools?
Usually not. Two tools disagreeing is a symptom of two definitions, not of the wrong tool. Once the definition lives in one place most tools consume it happily, and a migration undertaken to fix trust generally reproduces the problem in a new interface.
Who decides the definition when two teams disagree?
You do, and the work is making that decision possible rather than making it for you. Sometimes the honest answer is that both measures are legitimate and need different names — averaging them into one is the failure mode we most want to avoid.
How does this depend on governance and MDM?
Heavily. A metric built on unresolved entities will be wrong in a way no dashboard can reveal: if one customer appears three times, every per-customer figure is wrong and looks fine. We will say plainly when the analytics problem is actually an entity problem.
Will you delete our dashboards?
We will produce the list and the reasoning; retiring them is your call. Rationalisation is politically harder than it is technically, so we would rather hand you a defensible case than a fait accompli.
What about self-service? We do not want a bottleneck.
Nor do we. The aim is analysts building freely on certified datasets with agreed definitions, plus a clear route for a new metric to become official. The bottleneck people fear usually comes from having no such route, not from having governance.
How long before anything changes?
The Analytics Health Check runs in weeks and is deliberately narrow: where definitions diverge, which decisions depend on them, and what it would take to fix the top few. A full semantic layer is longer work and should be justified by that review rather than assumed.
Can you work alongside our BI team?
Yes, and it is usually the right shape. Your BI team knows the reporting landscape and the politics; we bring the definition and governance discipline. Both are needed, and neither is sufficient.
Analytics Health Check
A focused review of metric consistency, semantic architecture, report sprawl, lineage, data quality and decision usability. You finish with the conflicts named, the owners identified, and a sequence for fixing the ones that matter.
Scope and commercial terms are agreed in writing before the review starts.