Capability 03
Decisions your data can actually support
Most companies have the data and cannot answer the question. We build the architecture, the models and the dashboards that close that gap, then show whether the decisions got better.
How it runs
How a decision becomes a trusted dashboard on a governed data model
- Person decides
- Automated
- AI + human review
- Product database
- CRM and billing
- Support tool
- Governed data model
- Decision dashboards
- Reports that run on time
- Versioned definitions
Name the decision
Which decision do you keep making on incomplete information, and what would you need to see to make it well? That answer sets the model, the pipeline and the dashboard, and what we do not build.
A person makes this call. The system gives them the context.
Agree the definitions
We trace how each team calculates the metrics people argue about and write down the differences. The owners of those numbers agree one definition in a room; engineers do not pick a winner.
A person makes this call. The system gives them the context.
Connect and reconcile
Source mappings, transformations and data tests are built and reviewed by engineers. Each source is reconciled with documented lineage back to the rows it came from.
A person makes this call. The system gives them the context.
Automate reporting
The report a team assembled by hand runs on schedule. During the transition new numbers sit beside the old ones, so finance can see where they diverge and why before anything is switched off.
Rule-based and automatic, logged in the audit trail.
Predict where it pays
Churn scores and behavioral segments are added only where they would change what someone does. An analytics specialist reviews each model before anyone relies on it.
AI does the work, a named person reviews or signs off before it counts.
Judge on what happened
A retention score that flagged an account is checked against whether that account renewed. If a model is not changing anyone's behavior, we say so and take it out.
A person makes this call. The system gives them the context.
What you get
7 deliverables, all yours to keep
- Governed data model with documented lineage
- Written, versioned definition for each key metric
- Dashboards tied to named decisions and roles
- Automated reporting that replaces the manual pack
- Churn scores and behavioral segments where they pay
- Pipelines your own engineers can read and change
- A written record of what was cut and why
Sound familiar?
Why data and analytics fail to support decisions
Reporting takes a week and is stale when it lands, churn is found after the customer has left, and two teams bring different numbers to the same meeting. The data exists in the product database, CRM, billing and support tool. What is missing is one trusted definition per metric and a model built around a decision someone will act on.
We start from that decision, agree definitions with the people who own the numbers, then connect sources in the order the decision needs. A first trusted dashboard on a governed model typically lands in 3–5 weeks. Our longest analytics engagement, with a global AI SaaS company, ran 24 months because the questions kept getting better. We have also written about agreeing one definition per metric.
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Reporting takes a week and is stale when it lands
Someone assembles the pack by hand from several systems, and by the time it arrives the decision has already been made.
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Churn is discovered after the customer has gone
The signals were in product usage and support tickets, but nobody joined them in time to act.
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Two teams bring two numbers for the same metric
The meeting becomes about whose number is right rather than what to do.
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Nobody owns the definition of an active customer
The data sits in the product database, CRM, billing and support tool, and nobody trusts the join between them.
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Dashboards nobody would use keep coming back
Without a decision attached, every quarter brings requests for charts that inform nothing.
Services
What you can hire us for.
The work
Inside Data, Analytics & Insights.
What our data, analytics and insights work covers
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Analytics architecture
One model unifying product, CRM, billing and support data with documented lineage, so a number can be traced to the rows it came from.
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KPI governance
One definition per metric, with an owner, a written definition and the same calculation in every dashboard. The hard part is the organizational agreement.
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Predictive and retention modeling
Churn scores and behavioral segments that identify accounts drifting before they leave, with a review loop that judges the model on what happened next.
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Executive dashboards built around decisions
Each screen answers a named question for a named role. If nobody can say what decision a chart informs, it is removed.
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Reporting automation
The weekly pack a team assembles by hand becomes a scheduled job. This is usually where the first month of payback comes from.
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Product intelligence for SaaS
Conversion funnels, cohort behavior, feature adoption and the link between usage patterns and renewal.
How we work on data and analytics
- 01
Start from the decision, not the data
The decision determines the model, the pipeline and the dashboard. It also determines what we do not build, which is most of the backlog.
- 02
Definitions are agreed by the people who own the numbers
We document how each team calculates a disputed metric, then the owners agree one definition. We have written about how that meeting goes.
- 03
Prediction only where someone will act on it
A churn model nobody acts on is a cost. A score is added only where it would change what someone does on Monday.
- 04
Old and new numbers side by side
During the transition the new figures run beside the old ones, so divergences are explained before anything is switched off.
- 05
Something working every week
One-week sprints with a demo: a source connected and reconciled, a metric agreed and live, a report arriving on schedule.
Where analytics programs go wrong, and how we avoid it
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Engineers pick the winning metric definition
The owners of each number agree the definition in a room, and the result is written, versioned and enforced in every dashboard.
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A model keeps running because it is on the architecture diagram
Models are judged on scheduled checks against what happened next. If one is not changing behavior, we take it out.
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A definition changes and dashboards drift apart
Metric definitions are versioned, so every dashboard changes with them and the history shows when.
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The backlog fills with dashboards nobody uses
Each screen needs a named question and role. What was cut is written down with the reason, so it does not return every quarter.
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 works on it
- Data engineer
- Analytics specialist
- Product consultant
- Full stack developer
Engagement model
Agile Time & Material. Data work uncovers things, and the scope should be allowed to follow.
Good to know
The plan assumes access to source systems in week one and a date for your teams to agree definitions. Serious source quality problems are raised as soon as reconciliation finds them.
Proof
Engagements behind the numbers.
How a global AI SaaS company improved conversion rates by 18%
enhanced KPI visibility
FAQ
Data, Analytics & Insights: common questions.
Where does a data and analytics engagement start?
With a decision, not the data. We ask which choice you keep making on incomplete information and what you would need to see to make it well. Then we take the metrics people argue about most, trace how each team calculates them, and have the owners agree one definition before building the pipeline and dashboard around it.
What if our source data is poor quality?
Most is, somewhere. Reconciliation against each source shows where records are missing, duplicated or disagree, and we report that in the first weeks rather than hiding it inside a dashboard. Some issues are fixed in the pipeline with the rule written down. Others need a fix in the source system, and we tell you which, with the rows that prove it.
Do we need predictive models like churn scores?
Only where a score would change what someone does. Churn scores and behavioral segments can identify accounts drifting before they leave, but a model nobody acts on is a cost. Every model is judged on scheduled checks against what happened next, and if it is not changing behavior we say so and remove it.
How do you switch from our existing reports without losing trust?
The new numbers run beside the old ones during the transition, so your finance team can see where they diverge and why before anything is switched off. Definitions are documented and versioned, and lineage traces each dashboard figure back to the rows it came from.
Will our own engineers be able to maintain it?
Yes. The pipelines are yours, with transformations your engineers can read and change. Metric definitions are written down and versioned, and every change is reviewed by a named engineer. We also leave a written record of dashboards that were cut and the reason, so the backlog stays short after we hand over.