Build · Service
Analytics and BI
that turn data into a clearer view of what is happening.
We design reporting, dashboards and analytical models around the questions teams actually need to answer — connecting trusted data to operational, commercial and management decisions.
When reporting stops being enough
More dashboards do not necessarily create more understanding.
Analytics becomes difficult when measures are inconsistent, reports are built around available data rather than business questions, and teams spend more time debating numbers than acting on them.
- 01Different reports tell different versions of the businessThe same measure varies across teams because definitions, filters or source data are inconsistent.
- 02Reporting explains activity but not what mattersDashboards contain many metrics without enough connection to business outcomes or decisions.
- 03Analysts spend too much time rebuilding recurring reportsRepeated data preparation and manual reporting leave less time for useful analysis.
- 04Leaders still depend on spreadsheets for important decisionsBI tools exist, but trusted operational and management reporting still happens outside them.
The analytics decisions
Before building another dashboard, decide what somebody should be able to understand or decide from it.
- 01What business question are we trying to answer?Which operational, commercial or management decision should become clearer?
- 02Which measures actually matter?What outcomes, drivers and leading indicators deserve regular attention?
- 03What does each metric mean?How should measures be defined so teams interpret them consistently across reports?
- 04Which dimensions explain the result?What customer, product, location, channel, time or operational context needs to sit behind the headline number?
- 05Who needs which level of detail?What should executives, managers, analysts and operational teams each be able to see, explore or drill into?
- 06What action should the insight support?What changes when the metric moves, and who is responsible for responding?
What Analytics & BI can include
Analytics can support leadership visibility, operational control and deeper investigation from the same trusted data foundation.
- 01Executive dashboardsFocused views of business performance, priorities and important exceptions for leadership teams.
- 02Operational reportingDay-to-day visibility into workflows, service levels, volumes, capacity and operational performance.
- 03Sales & commercial analyticsPipeline, revenue, conversion, customer and market performance across commercial teams.
- 04Product analyticsUsage, adoption, engagement, journey and feature behaviour across digital products.
- 05Financial & management reportingStructured reporting around performance, planning and management measures.
- 06Self-service analyticsCurated datasets and semantic models that allow teams to investigate questions without recreating business logic.
Related analytics & data areas
Analytics depends on reliable data, connected systems and clear measurement foundations. These areas commonly shape the quality and usefulness of the BI environment.
Engagement scope
What we actually do.
- 01Analytics discovery & decision mappingIdentify audiences, business questions, recurring decisions and the current reporting gaps that matter most.
- 02KPI & measurement definitionDefine measures, business rules, dimensions and ownership so reports are built around shared meaning.
- 03Analytical & semantic modellingCreate reusable models that organise source data into business-friendly entities, measures and relationships.
- 04Dashboard & reporting developmentDesign and build reporting experiences around the decisions, users and level of exploration required.
- 05Data validation & reconciliationVerify that reported measures trace back to trusted sources and behave consistently across views.
- 06Adoption & analytics governanceEstablish ownership, documentation, access, review and improvement practices so reports remain trusted and useful as the business changes.
Engagement options
Ways we can build with you.
Not sure which starting point fits? We will help you choose.
- Focused dashboard or reporting solutionCreate a defined analytics experience around one business area or decision set.Useful whenThe organisation knows which information it needs but current reporting is fragmented or manual.
- BI foundation & reporting programmeDefine shared measures, analytical models and reporting across several related teams or functions.Useful whenMultiple dashboards and spreadsheets need one more coherent reporting environment.
- Embedded analytics capabilityAdd analytics engineering and BI expertise alongside existing data or business teams.Useful whenThe organisation already has data foundations but needs sustained modelling, reporting and decision-support capability.
What you leave with
A clearer, shared view of the business — and the measures behind it.
The exact outputs depend on the reporting environment, but the engagement should leave teams with trusted definitions, useful analytical experiences and a stronger path from information to action.
- 01Defined business measuresClear definitions for the KPIs and metrics that matter most.
- 02Reusable analytical modelsStructured entities, dimensions and measures that reduce repeated reporting logic.
- 03Decision-focused dashboardsViews designed around the questions and actions important to each audience.
- 04Trusted reporting logicMeasures validated against source systems and reconciled across reports.
- 05Controlled access and ownershipClear responsibility for data, definitions, reports and who can use them.
- 06A foundation for deeper analysisAn analytics environment ready to support self-service, experimentation, forecasting and more advanced analytical use cases over time.
Our approach
Start with the decision. Build backwards into the data and the dashboard.
- 01Understand the questionClarify which decision, audience and business context the analytics needs to support.
- 02Define the measuresAgree on the outcomes, drivers, definitions and dimensions behind the analysis.
- 03Build the analytical modelOrganise trusted data into reusable structures that reflect how the business thinks.
- 04Design the experienceCreate dashboards and reports that surface what matters without overwhelming users with available data.
- 05Observe use and improveUnderstand which reports are trusted, used and acted upon, then evolve the analytics environment accordingly.
From reporting to better decisions
A dashboard is a milestone. The organisation still has to trust it, use it and act on it.
Analytics & BI can move directly into the capabilities required to strengthen the data foundations, automate information flows and connect insight back into products and operations.
FAQ
Common questions about Analytics & BI.
Business intelligence is the use of data models, reporting and dashboards to help organisations understand performance, monitor operations and support decisions.
Data engineering creates the pipelines and data foundations that make information reliable and reusable. BI uses those foundations to create measures, reports and analytical experiences for business users.
We can work with appropriate established BI platforms depending on the organisation’s data environment, user needs, licensing and existing technology choices rather than forcing one tool.
Yes. Executive dashboards are usually designed around a focused set of outcomes, drivers and exceptions rather than trying to reproduce every available metric.
Often, yes. We first understand why the spreadsheet exists, which transformations and decisions it supports, and whether those should move into reusable analytical models and BI reporting.
We define metrics explicitly, identify their source data and logic, assign ownership and reuse the same analytical definitions across reporting wherever practical.
Yes. The level of exploration can be designed around each audience, from high-level executive reporting through to detailed analytical investigation.
Yes. Self-service works best when teams are given governed datasets, reusable metrics and clear business definitions rather than unrestricted access to raw operational data.
Yes. This usually relies on Data Engineering or System Integration to bring the relevant information together reliably before analytical modelling.
Yes, where the decision genuinely benefits from real-time or near-real-time information. Many management use cases do not require that additional complexity.
Yes. AI can support natural-language querying, summarisation, anomaly investigation and decision support when it operates over trusted, well-defined data.
Yes. We can continue with data engineering, BI development, cloud operations and ongoing reporting improvements as business requirements evolve.
