Data analytics consulting that starts from the decision
Pipelines, a governed data model and dashboards built around the choices your leadership team actually makes. Scoped by the question, not by how many sources you have.
- Python
- SQL and dbt
- PostgreSQL
- MongoDB
- AWS
- Power BI
- React
- Node.js
- Product database
- CRM and billing
- Support tool
- 01 Map decisionsranked by cost of a wrong call
- 02 Agree definitionsone written formula per KPI
- 03 Build tested pipelinesreconciled against finance numbers
- 04 Run in parallelold and new numbers side by side
- Role dashboards
- Scheduled reports
- Churn risk scores
What you get
8 deliverables, all yours to keep
- Decision inventory by role
- Written KPI definitions with owners
- Ingestion pipelines from core systems
- Tested warehouse layer with lineage
- KPI dashboards built per role
- Scheduled reporting to replace the weekly pack
- Predictive scores where they change action
- Runbooks and pipeline handover
Sound familiar?
When data analytics consulting is the right call
Most organizations that come to us for data analytics consulting do not have a data shortage. They have a product database, a CRM, a billing system and a support tool, and still cannot answer the question the board asked last quarter. We work backward from the decision: agree what each metric means, build pipelines your engineers can read, then put dashboards in front of the people who make the calls.
For a global AI SaaS company this meant unifying product, CRM and billing data into one analytics platform with retention modeling over a 24 month engagement. We also wrote about how the definition meeting usually goes, including the part where half the disagreement turns out to be timing rather than formula.
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The weekly pack takes most of Monday to assemble
Two people spend the morning building a report that is out of date by Wednesday.
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Sales, finance and product each show a different figure
Meetings about what to do turn into arguments about whose number for active customers is right.
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Churn surfaces only when the renewal fails
Months of quietly falling usage go unseen until the account is already lost.
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The BI tool was bought, then abandoned
Dashboards were built, but usage dropped after the first month because nobody trusted the joins.
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A warehouse is proposed with no decision attached
Budget goes toward infrastructure before anyone can say which choice it would improve.
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Engineers keep pipelines alive but own nothing else
Nobody has time to own the definitions or the reporting built on top of the data.
The work
Inside a Data Engineering & Analytics engagement.
Data engineering services and the reporting built on them
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Ingestion from operational systems
Pipelines from product, CRM, billing and support systems, connected in the order the first decision needs them.
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One agreed shape per business entity
A modeled layer where customer, order, partner and subscription each have one shape, with tests so a silent schema change in a source fails loudly.
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Lineage for every figure
Documented lineage, so any number on a dashboard can be traced back to the rows it came from.
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KPI dashboard development per role
Role dashboards, scheduled reports, and alerting where a threshold matters more than a chart.
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Predictions that change behavior
A churn or drift score for accounts where it would change what someone does on Monday, reviewed against what actually happened next.
How we approach business intelligence consulting
- 01
A decision inventory, not a source inventory
In the first week leadership names the choices they make on incomplete information. That list sets which sources get connected first and removes many requested dashboards before anyone builds them.
- 02
Definitions come before pipelines
We trace how each team calculates the three most disputed metrics and write down every difference. The owners agree one definition in a working session. Engineers do not pick the winner.
- 03
Batch unless someone acts within minutes
Most executive reporting is fine refreshed hourly or daily. Streaming adds cost and operational burden, so it is used only where a person acts within minutes.
- 04
Start from the replica and the BI tool you have
A read replica plus a modeled layer is often enough for the first year. We publish into the BI tool your finance team already uses unless it cannot express a definition you need.
Where analytics projects go wrong
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A dashboard nobody opens three months after launch
Every screen gets a named role, a named question and a scheduled review of whether it changed a decision. Views that changed nothing are removed.
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Finance loses trust when a new revenue figure differs from the ledger
During transition, new and old numbers run side by side and every gap is explained before anything is switched off.
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A pipeline only the vendor can maintain
Transformations are written in plain SQL and Python, kept in your repository, and handed over with runbooks written by the people who built them.
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Prediction for its own sake
A churn model nobody acts on is a cost. If a model is not changing behavior after a review cycle, we recommend switching it off.
How it runs
From first call to production.
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Decision mapping
About a week with the people who make the calls. We list the decisions made on incomplete information and rank them by cost of getting them wrong.
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Definition work
The three metrics people argue about most are traced through every team's calculation, and the owners agree one definition in writing.
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Pipeline and model
Sources are connected in the order the first decision needs them, reconciled against the numbers finance already reports, and tested.
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Dashboards in parallel
New reports run beside the old ones until the differences are explained. Nothing is switched off while a number is still disputed.
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Review and extend
Each dashboard is checked against whether it changed a decision. Unused views are removed and the next decision on the list is taken up.
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, because data work uncovers things the scope should be allowed to follow. A Dedicated Team suits organizations that want a standing analytics function after the first release.
Good to know
Runs as Agile Time & Material, since source quality and definition disputes cannot be scoped ahead. Your side provides read access to source systems in week one, a finance contact for reconciliation, and the metric owners for the definition session.
Proof
Where we have built this.
How a global AI SaaS company improved conversion rates by 18%
enhanced KPI visibility
How a global fashion retailer increased customer engagement by 34%
increased customer engagement
How an FMCG brand increased market share by 37% through digital commerce
increase in market share
FAQ
Data Engineering & Analytics: common questions.
What does a data analytics consulting engagement actually deliver?
A working reporting system, not a strategy deck. That means pipelines from your source systems, a modeled data layer with documented lineage, written definitions for the metrics that matter, and dashboards each tied to a named decision. The first usable output normally arrives within the first three weeks, usually an automated report that replaces something a team currently assembles by hand.
What is the difference between data engineering services and business intelligence consulting?
Data engineering moves and models the data: ingestion, transformation, testing and the warehouse layer. Business intelligence consulting sits on top of it and decides what gets reported, to whom, and how the numbers are defined. Most failed dashboard projects bought only the second. The charts looked right, but nobody could trace a figure back to its source, so nobody trusted it.
Do we need a new data warehouse before we can build KPI dashboards?
Usually not. Many organizations already have a PostgreSQL replica or a cloud warehouse that is underused. We start with what exists and add a modeled layer on top. A new warehouse is worth it when query load is hurting the production database or when sources cannot be joined reliably, and we will say so after the first week or two rather than assume it.
Can you work with Power BI or the BI tool we already pay for?
Yes. The tool matters less than the model underneath it. If your finance team already lives in Power BI, we build the governed layer and publish into it. We only recommend a different tool when the current one cannot express a definition you need, or when licensing across the audience you want to reach would be out of proportion.
How long before analytics work shows a return?
The earliest payback usually comes from reporting automation, typically in the first three to five weeks, because team hours spent assembling packs stop immediately. That assumes read access to source systems in week one and a definition meeting the metric owners can attend early. Better decisions take longer to show up and should be checked, not assumed. We schedule a review of each dashboard against the decision it was built for, and remove the ones that did not change anything.
Related services
- AI DevelopmentLLM applications, generative AI features and document agents built for production: structured outputs, pinned models, regression sets and a person on every consequential decision.
- Legacy Application ModernizationLegacy application modernization in reversible slices. The old system stays live while each function is rebuilt, shadow tested and cut over on its own.
- Cloud MigrationCloud migration services across AWS, Azure and GCP. Rehost where it is safe, re-platform where it pays, with cost controls in place from day one.
Further reading
