Artificial Intelligence and Data
Turning the data operational systems already produce into something a team can act on, with the engineering underneath it that makes the answers trustworthy.
In brief
Data engineering, reporting and applied analytics built on operational data from connected systems.
The business challenge
Most organisations have more data than insight. Readings sit in separate systems, in different formats, with no agreed definition of what a figure means — so two reports answer the same question differently.
Analytics built on that foundation produce confident answers that are wrong, which is worse than no answer at all because decisions get made on them.
Solution overview
We work on the layer beneath the dashboard: collecting operational data reliably, reconciling it across sources, and giving figures a single agreed definition before anything is visualised.
Applied analytics and machine learning are added where they answer a question the organisation actually has. We do not introduce a model where a well-defined report would do.
Capabilities
Data engineering
Collecting, cleaning and reconciling operational data across the systems that produce it, so figures are comparable.
Reporting and dashboards
Operational views built around the decisions each team makes, rather than showing everything available.
Applied analytics
Analysis of operational patterns — utilisation, exceptions, trends — to support planning.
Applied machine learning
Prediction and classification where there is enough reliable history to support it, with the limits of the model stated.
Business benefits
One agreed set of figures
Reports draw on a single reconciled source, so teams stop debating whose number is correct.
Decisions on current data
Operational questions are answered from what the systems are reporting now rather than from a monthly compilation.
Visible assumptions
Definitions and calculations are documented, so a figure can be checked rather than trusted blindly.
Foundations that support later analysis
Getting the data layer right first means analytics can be added without rebuilding.
How we deliver it
Data assessment
We map what data exists, where it lives, how reliable it is, and which questions the organisation needs answered.
Model the data
Definitions, reconciliation rules and structure are agreed before anything is built on top.
Build and integrate
Pipelines, storage and reporting are built and connected to the source systems.
Review and extend
Reports are reviewed against real decisions, then refined as needs become clearer.
Technology architecture
A data platform is only as good as the layer below the visualisation. The structure below separates collection from meaning, so a change in one source does not invalidate every report.
Source layer
Operational systems, devices and platforms that produce the raw data.
Ingestion and storage
Reliable collection and retention, with the raw record preserved.
Modelling layer
Reconciliation, definitions and derived figures — the single agreed meaning.
Presentation layer
Dashboards, reports and interfaces built on the modelled data, not on raw sources.
Relevant industries
- Utilities
- Telecommunications
- Transportation
- Government
- Manufacturing
Related platforms
- thethings.io
Partner attribution
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Related projects
- Operational reportingImage to be supplied
- Data consolidationImage to be supplied
Project references for this solution are being prepared and will appear here once approved.
Discuss this solution with our team
Tell us about your environment and constraints, and we will arrange a conversation with the right engineers.