Trusted Data & Analytics Foundation
Data engagement
Make the same business question produce the same answer.
Product and marketing define conversion differently. Customer, account, store, and product identities vary by system; local metrics and drifting events undermine experiments. Analysts reconstruct definitions, while AI consumes data whose meaning and provenance are unclear.
Bounded scope · visible work · no forced upsell
The promised outcome
Make decisions on data your teams can trust.
Canonical business entities, shared metrics and event/data contracts, accountable ownership, lineage and quality rules, one reference semantic or analytics path, and prioritized adoption steps.
Data landscape and decision map
Inventory sources and the business questions where disagreement, event drift, or low confidence changes a decision.
Canonical entity model
Align customer, account, store, product, and other agreed business entities across systems.
Shared taxonomy and semantic model
Define a common vocabulary, taxonomy, ontology where useful, and semantic-layer principles that preserve meaning.
Metric and KPI catalog
Document calculation, grain, inclusion rules, owners, and experiment/conversion definitions for agreed measures.
The problem this solves
Product and marketing define conversion differently. Customer, account, store, and product identities vary by system; local metrics and drifting events undermine experiments. Analysts reconstruct definitions, while AI consumes data whose meaning and provenance are unclear. Best for a product, data, or engineering leader whose analytics, experiments, commercial reporting, or AI depend on shared definitions across teams.
Build shared meaning beneath every analytical decision.
A reusable foundation supports retail, product, marketing, experimentation, commercial reporting, and AI consumption.
Data landscape and decision map
Inventory sources and the business questions where disagreement, event drift, or low confidence changes a decision.
Canonical entity model
Align customer, account, store, product, and other agreed business entities across systems.
Shared taxonomy and semantic model
Define a common vocabulary, taxonomy, ontology where useful, and semantic-layer principles that preserve meaning.
Metric and KPI catalog
Document calculation, grain, inclusion rules, owners, and experiment/conversion definitions for agreed measures.
Event and data contract standards
Set schema, identity, versioning, quality, and compatibility expectations for product and commercial analytics.
Ownership and governance model
Connect stewardship, approvals, change controls, and quality checks to the actual producer and consumer workflows.
Provenance and lineage requirements
Make source, transformation, attribution, and confidence expectations inspectable for reporting and AI.
Reference semantic or analytics implementation
Implement or prototype one agreed path that reconciles an important metric or analytical question.
Adoption and migration plan
Sequence catalog adoption, producer changes, validation, accountable owners, and rollout gates.
How this creates leverage
Connect analytics to shared contracts and ownership.
Critical decisions establish the vocabulary; reference entities, metrics, contracts, and lineage make that vocabulary executable.
- 01Diagnose the decisions most affected by conflicting definitions and weak data confidence.
- 02Design canonical entities, metrics, contracts, provenance, and workflow-level governance.
- 03Prove one semantic or analytics path against an agreed business question.
- 04Transfer the catalog, ownership rules, validation expectations, and migration sequence.
Relevant work
A bounded engagement with visible work and an honest stopping point.
You should know what is being decided, what evidence supports it, and where the engagement ends.
Explore the thinking before we talk
Use these practical articles and lead magnets to evaluate the approach against your own situation.
Data by workload
Choose data boundaries and storage patterns against actual workload needs.
Read the field guideUsage attribution
Connect consumption to product, customer, cost, and outcome.
Read the field guideProduct principles factsheet
Give teams shared standards for owned, measurable data products.
Read the field guideBefore you engage
The practical questions leaders ask first
Clear answers about fit, scope, timing, and what happens before any commitment.
Who is this engagement designed for?
Best for a product, data, or engineering leader whose analytics, experiments, commercial reporting, or AI depend on shared definitions across teams.
What changes by the end?
Canonical business entities, shared metrics and event/data contracts, accountable ownership, lineage and quality rules, one reference semantic or analytics path, and prioritized adoption steps.
How long does the engagement take?
Trusted Data & Analytics Foundation is planned around Scoped engagement. Final timing reflects access, evidence, and the number of teams involved.
What is intentionally not included?
Scope covers agreed entities, decisions, contracts, and one reference path. A new warehouse, full historical backfill, every dashboard, and enterprise-wide data remediation require separate approval.
What happens before I approve anything?
Bring one business question that produces conflicting answers and the teams that own its data. We will define the reference path, required access, and adoption boundary together.
Do you implement anything or only advise?
One representative workflow or integration is implemented or prototyped within the agreed scope. Access, acceptance criteria, and the boundary of a wider rollout are written down first.
Will we be required to use Agenty software?
No. We work with your existing environment where feasible. You retain the reusable artifacts and continuation plan; a follow-on purchase or Agenty software license is optional.
Do we need a new warehouse?
Usually the first move is shared meaning, ownership, and contracts in your current stack. Technology changes are recommended only when an agreed workload or proof path requires them.
Can this cover experiments and product analytics together?
Yes. We align agreed event and metric definitions across product, marketing, retail or commercial reporting, experimentation, and AI consumption; the scope names which decisions and entities are included.
Ready to get this done? Start here.
Bring one business question that produces conflicting answers and the teams that own its data. We will define the reference path, required access, and adoption boundary together.