Service
Service
Data Context & Semantics (Analytics)
Measurement teams trust enough to act on.
Reporting fails on definitions long before it fails on visualisation. We settle what each metric means, build models that reconcile, and design the small number of views that actually change a decision.
- Definitions settled first
- Numbers that reconcile
- Built for a decision
- Owned by your team
Advantage:
Tangible Outcomes
Agentforce · Data · Salesforce
When leadership stops trusting a number, the cause is almost never the chart. It is that two teams count the same thing differently and both are locally correct. No amount of redesign fixes that.
We start with the definitions and the model, get the numbers reconciling, and only then design the views. The test is not whether a dashboard is used — it is whether a decision changed because of it.
Scope
What we build
Metric definition and governance
An agreed, documented definition for each metric that matters, including the edge cases the disagreements always hide in.
- Metric catalogue with owners
- Edge case and exclusion rules
- Change process for definitions
- Single source for each number
Reporting models
Models built for the questions being asked, with grain and joins chosen so totals reconcile rather than approximately agree.
- Grain and conformance design
- Reconciliation to source of record
- Historical and slowly changing handling
- Performance at real data volume
Dashboards and reporting
A deliberately small set of views, each tied to a decision and an owner. Everything else is available but not promoted.
- Decision-first view design
- Salesforce reports, dashboards and Tableau
- Accessible, consistent visual treatment
- Named owner per view
Agent and AI measurement
Instrumentation for agents specifically: containment, escalation, action success, and credit consumption per outcome.
- Containment and escalation tracking
- Action success and failure taxonomy
- Credit burn against forecast
- Quality sampling and review
Enablement
Handing over the model, the definitions and the ability to extend them, so analytics stops depending on whoever built it.
- Model and definition documentation
- Analyst and admin enablement
- Self-service guardrails
- Review cadence for the catalogue
How we approach analytics
Step 1
Define
Agree what each metric means, who owns it, and which edge cases are in or out.
Step 2
Model
Build the reporting model and reconcile it to the source of record before any visual work.
Step 3
Design
Build the small number of views tied to real decisions, each with a named owner.
Step 4
Hand over
Transfer the catalogue, the model and the ability to extend both.
What good looks like
What trustworthy measurement changes
Numbers that agree
Reconciled models and settled definitions, so meetings stop relitigating the figures.
Fewer, better views
A small set of dashboards tied to decisions, each with an owner who acts on it.
Agents you can judge
Containment, escalation and action success measured, so agent performance is observable.
Cost per outcome
Credit consumption tracked against results rather than against a monthly invoice.
Definitions that survive
A catalogue with owners and a change process, so drift is a decision rather than an accident.
Self-service that holds
Guardrails and enablement so analysts extend the model without forking the truth.
Which number does your leadership team not trust?
That is usually the fastest place to start. Tell us the metric and we will show you where the definitions diverge.