Analytics & Decision Intelligence
KPI architecture, predictive analytics and executive reporting
SaaS companies tracking retention and conversion, and enterprises where the same KPI has three different values depending on who ran the report.
How it works
How raw data becomes a governed KPI and then a decision
- Automated
- Person decides
- AI + human review
- Product and CRM data
- Billing and support
- Operations systems
- Governed metric catalog
- Ranked churn-risk list
- Executive dashboards
Connect sources
Scheduled or streaming pipelines load each source into one warehouse model. Every table carries lineage: where it came from, when it was loaded and what transformed it.
Rule-based and automatic, logged in the audit trail.
Agree definitions
Each metric gets one definition, in plain language and in code, with a named owner. Agreeing it is a people decision. Definitions are versioned, so a change shows when and why it happened.
A person makes this call. The system gives them the context.
Govern and compute
Any dashboard showing a metric calls the governed definition and cannot compute its own. Alerts fire when a governed metric moves outside its band.
Rule-based and automatic, logged in the audit trail.
Predict and segment
Retention scoring flags accounts drifting toward churn from behavioral signals, and segmentation groups users by behavior. Customer success gets a ranked list and decides who to act on.
AI does the work, a named person reviews or signs off before it counts.
Report automatically
The weekly pack a team used to assemble by hand is generated from governed metrics and distributed on schedule. Executive dashboards are organized by decision and role.
Rule-based and automatic, logged in the audit trail.
Decide
The layer that matters. People act on the retention list, weigh the alert and make the call. The platform supplies the governed number, not the decision.
A person makes this call. The system gives them the context.
Who it is for
When the same KPI has three values depending on who ran the report
Analytics & Decision Intelligence is the analytics layer we build when a company has plenty of data and no reliable answers. It unifies sources, gives every metric one governed definition with a named owner, adds prediction where it earns its place, and automates the reporting that was consuming a team. Getting to one definition per metric is more organizational than technical.
Because it is built top-down from a named decision, a first trusted dashboard on a governed model is live in 3–5 weeks. A global AI SaaS company ran this pattern over 24 months, combining behavioral analysis, predictive retention and automated reporting, and platforms of this type improve KPI visibility by up to 25%.
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The same KPI has three different values
Each team computes its own version, so meetings argue about whose number is right instead of what to do about it.
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BI charts sit on top of ungoverned data
The tool draws excellent charts of whatever it is pointed at, including three versions of the same metric, because it does not own the model.
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The data platform stops at the model
The warehouse is built, but the decision layer is left to whoever builds the dashboard, and nobody owns what a metric means.
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A team spends its week assembling the report
The weekly pack is put together by hand from several systems, which consumes the people who should be reading it.
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Retention and conversion are tracked after the fact
Without behavioral scoring, accounts drifting toward churn are not flagged, and customer success has no ranked list to work from.
The platform
What Analytics & Decision Intelligence gives you.
Modules in production, ready to configure
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Unified analytics architecture
Product, CRM, billing, support and operations data in one warehouse model, with lineage on every table.
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KPI governance framework
One definition per metric, in plain language and in code, with a named owner and version history.
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Predictive retention and churn models
Behavioral signals flag accounts drifting toward churn, delivered as a ranked list rather than a probability.
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Behavioral segmentation
Users grouped by what they do in the product rather than by demographics.
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Conversion and cohort analysis
Conversion and retention by cohort, computed from the governed metric definitions.
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Executive dashboards
Organized by decision and by role, not by data source. Power BI or custom dashboards.
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Automated report generation
Scheduled packs generated from governed metrics and distributed without anyone assembling them.
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Semantic search over metric documentation
Retrieval with pgvector lets an analyst ask which definition applies and get the governed answer.
What is different about this analytics and decision intelligence layer
- 01
Built top-down from the decision
The model contains what the decisions need and not much else, which makes it smaller, faster to build and easier to trust than a warehouse that tried to model everything.
- 02
Definitions are enforced, not just documented
Dashboards must call the governed definition. When a definition changes, every dashboard changes together, and the history shows when and why.
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Models only against a named decision
Prediction is added where it earns its place. The customer success team gets a ranked list of accounts, not a probability.
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Ask which definition applies
Semantic retrieval over the metric documentation, using pgvector, returns the governed answer rather than a colleague's recollection.
Architecture
What Analytics & Decision Intelligence runs on.
The reference architecture we deploy. The crimson application services are where we configure for you; identity, workflow, storage and observability are already built and in production.
Architecture of the Analytics and Decision Intelligence. Users: Product managers, Executives, Data team, Customer success. Edge and access: API gateway (rate limits, keys), Identity and SSO (Cognito, SAML), CDN and WAF (CloudFront). Application services: Ingestion (Python, scheduled), Metric layer (governed definitions), Prediction (models), Ask your data (LLM + retrieval). Data: Warehouse (PostgreSQL), Semantic retrieval (pgvector), Query cache (ElastiCache Redis). Sources: Product events (SDK), CRM (API), Billing (webhooks), Support desk (API). Delivery: Executive dashboards (role-based), Scheduled reports (email), Risk alerts (to CS). Storage and delivery: Data lake (S3), Read replica (reporting load), Static delivery (CloudFront). Observability and security: Pipeline monitoring (CloudWatch), Data quality checks (per load), Secrets and keys (KMS).
Team and timeline
3–5 weeks
to a first trusted dashboard on a governed model
Indicative. Actual duration depends on requirements and complexity, and can change.
Who sets it up
- Data architect
- Data engineers
- Analytics engineer
- Data scientist
- Delivery lead
Good to know
The first model is small because it is built from one decision. Agreeing each definition with its owner and getting access to source systems usually set the pace.
Proof
Where this platform has run.
How a global AI SaaS company improved conversion rates by 18%
enhanced KPI visibility
FAQ
Analytics & Decision Intelligence: common questions.
Why not just buy a business intelligence tool?
Keep it if you have one. BI tools draw excellent charts on top of whatever data you point them at, including three different versions of the same metric, because they do not own the model. This layer sits underneath: it unifies sources, holds one governed definition per metric and feeds your dashboards, whether those are Power BI or custom built.
Who agrees the metric definitions?
Your people. Each metric gets one definition, written in plain language and in code, with a named owner. We draft definitions and show where current reports disagree, but deciding what an active customer means is a business decision. Getting there is more organizational than technical. Once agreed, the definition is versioned, and every dashboard that shows the metric changes with it.
Do we need predictive models from the start?
Usually not. Models are added only against a named decision, once the metrics they depend on are governed. Retention scoring is a common first model: it flags accounts drifting toward churn from behavioral signals and gives customer success a ranked list. A model built on disputed metrics produces disputed predictions, so governance comes first.
What should we prepare before the work starts?
Three things. The decision you want the first dashboard to support, named plainly. The people who own the metrics that decision depends on, with time in their calendars to agree definitions. And read access to the source systems those metrics come from. With those in place, the first weeks go on building rather than waiting.