How it works
From data to decisions.
Every engagement follows the same structured path, sized to the complexity of your business and the state of your data.
Engagement model
Six stages
Each stage produces a defined output, so you always know what has been delivered and what comes next.
- 01
Discover
Understand the business, objectives, systems, challenges and decisions that matter.
- 02
Audit
Assess existing data sources, reporting processes, data quality and infrastructure.
- 03
Connect
Bring relevant data sources together and create a reliable analytical foundation.
- 04
Analyze
Build data models, KPIs, dashboards, analytical models and forecasts.
- 05
Advise
Translate findings into clear business recommendations.
- 06
Improve
Monitor results, automate reporting and continuously improve the analytical system.
Output — Discover
A written summary of objectives, systems and key decisions.
Output — Audit
A data health assessment with prioritised findings.
Output — Connect
A consolidated, documented analytical data foundation.
Output — Analyze
Working dashboards, models and defined KPIs.
Output — Advise
A recommendation set tied to specific business actions.
Output — Improve
Automated reporting and an agreed monitoring cadence.
The pipeline
What happens to your data along the way
Raw operational records become a governed foundation, then measurement, then interpretation, then a decision. Nothing is skipped.
- 01
Data Sources
Excel, CRM, ERP, accounting, e-commerce, marketing, databases
- 02
Data Foundation
Cleaned, consolidated, modeled and documented
- 03
Analytics
KPIs, dashboards, analysis and forecasts
- 04
Intelligence
Patterns, drivers and early signals
- 05
Decisions
Clear recommendations and measurable actions
Worked example
A decision, end to end
A simplified illustration of the method applied to a common commercial problem.
“Our sales are growing, but profitability is declining.”
Illustrative exampleInputs
- Sales
- Customers
- Products
- Marketing
- Costs
Margin decomposition by product line, customer cohort analysis and acquisition cost tracking over twelve periods.
Finding
Growth is being driven by low-margin products and rising acquisition costs.
Recommendation
Shift acquisition toward higher-value customer segments and optimize the product mix.
Data becomes valuable when it changes a decision.
FAQ
Common questions
- Do we need a data warehouse before we start?
- No. Most engagements begin with the systems you already use — spreadsheets, a CRM, an accounting tool. We assess what exists first and only recommend new infrastructure when the analysis genuinely requires it.
- How long before we see something useful?
- The Data Health Check produces a written assessment and recommendations from a defined scope of sources. Ongoing engagements typically deliver a first working dashboard within the initial reporting cycle, depending on data access and quality.
- Which tools do you build in?
- We select tooling per engagement. That may mean Power BI, Tableau or Looker Studio for reporting, with SQL, Python or Java services behind it. We do not force a single stack onto every client.
- Who owns the dashboards and models you build?
- You do. Deliverables, documentation and data models belong to your business.
- Can you work with messy or incomplete data?
- Yes — that is usually the starting point. Data cleaning, consolidation and quality assessment are part of the Data Management work that precedes analysis.
- Is pricing fixed?
- Final pricing depends on data volume, integrations, complexity and reporting requirements. Custom projects are quoted after an initial assessment.
Start with a clear view of your data.
A short consultation is enough to identify where your reporting breaks down and what a reliable analytical foundation would look like for your business.