Reports disagree
Teams calculate the same indicator differently or rely on extracts produced at different times.
05 · Floatger service
Data analytics & business intelligence
We connect, model, and present business data through practical analytics and reporting experiences, with attention to definitions, ownership, quality, and the decisions each view supports.
The context
When this service becomes valuable.
Turn scattered operational data into clearer, trusted decisions. The right starting point is a shared understanding of the problem—not a predetermined feature list.
Data is available but not useful
Dashboards contain activity without connecting it to the decisions and actions people need to take.
Ownership is unclear
No one is accountable for source quality, definitions, access, refresh behaviour, or investigating anomalies.
What the work may include
Connected capabilities, shaped around the engagement.
The exact mix is agreed after understanding your current situation, priorities, and constraints.
01
Analytics discovery
Define audiences, decisions, questions, measures, source systems, and the operational action each view should enable.
02
Data integration and modelling
Connect approved sources and develop understandable models with documented transformations and definitions.
03
Dashboard and reporting design
Create clear views, filters, comparisons, and detail paths suited to each audience and usage rhythm.
04
Quality and governance foundations
Establish practical validation, lineage, access, refresh, ownership, and issue-handling expectations.
Potential outputs
Tangible progress your team can use.
Outputs depend on the scope and stage of the engagement. They are agreed before delivery begins and refined as the work becomes clearer.
- 01A measurement framework and agreed business definitions
- 02Connected and documented analytics-ready data models
- 03Dashboards or reports designed around priority decisions
- 04Data quality, access, refresh, and ownership guidance
Planning the engagement
Make the inputs, evidence, and boundaries clear.
Useful delivery starts with the right context and an agreed way to evaluate progress—not an assumption that every possible concern belongs in scope.
What we need to understand
- Priority decisions, audiences, questions, and reporting cadence
- Source-system access, data definitions, sample outputs, and known quality issues
- Privacy, retention, regional, and role-based access requirements
- Existing data platforms, reporting tools, owners, and operating constraints
How progress can be evaluated
- Stakeholders use agreed definitions for priority measures
- Reports reconcile to documented sources and refresh as intended
- Users can move from a signal to the context needed for an informed action
Important scope boundary
Analytics can improve visibility but cannot correct weak source processes on its own. Data accuracy, historical completeness, third-party platform limits, forecasting assumptions, and formal governance work are assessed and scoped explicitly.
Delivery path
From context to a practical next stage.
Each stage creates enough clarity for the decisions that follow, while keeping the process proportionate to the work.
Question
Define the decisions, audiences, measures, and action context.
Map
Trace sources, definitions, access, quality, and ownership.
Build
Develop models, transformations, dashboards, and validation.
Adopt
Document, release, observe usage, and refine the reporting experience.
Engagement shape
Start at the point that matches the challenge.
Analytics assessment
Map decisions, reports, data sources, definitions, gaps, and the most useful starting point.
Dashboard and data delivery
Create the required models, pipelines, views, validation, and documentation as one connected scope.
Analytics evolution
Extend measures, sources, and decision support as business needs and data maturity develop.
Service questions
Useful things to clarify.
Can you work with our existing reporting tools?+
Yes. We first review their capabilities, licences, data connections, governance model, and current adoption before deciding whether to improve or extend them.
Do you clean all of our historical data?+
Data profiling identifies the issues relevant to the agreed use case. Remediation, backfilling, deduplication, and source-process changes are scoped according to impact and feasibility.
Can you create real-time dashboards?+
Where the decision genuinely requires it and the source systems can support it. Refresh frequency should reflect user need, cost, reliability, and the way source data becomes available.
How do you prevent different metric definitions?+
We document calculation rules, sources, exclusions, timing, ownership, and changes for priority measures, then implement those definitions consistently in the data model.
Can analytics support forecasting?+
Potentially, once the question, historical evidence, uncertainty, validation approach, and decision context are suitable. Forecasts should communicate their assumptions and limitations.
Start a conversation
Let's discuss data analytics & bi.
Share the context, current state, and what you need to move forward. We will help identify a sensible starting point.