Capabilities
Six data specialisms, from raw data to AI.
Each capability stands on its own, and many engagements start with one. Together they cover the full journey from scattered data to decisions, analytics and AI.
Data Management
Data that is governed, documented and fit to use.
Where it sits in the data journey
The business problem
Nobody is quite sure which data is correct, who owns it, or whether it can be trusted for a decision.
What we do
We set up the architecture, ownership and rules that keep data accurate, secure and understood, so every other use of it rests on something reliable.
Architecture
- Data architecture and modelling
- Data standards and naming conventions
Governance
- Data ownership and stewardship
- Access rules and handling of sensitive data
- Lineage: where data comes from and how it changes
Quality
- Data quality rules and monitoring
- Master data management
Typical scope
Data architectureGovernanceData qualityMaster dataLineage
Technologies: Across Microsoft Fabric, Databricks and AWS environments
Connects to
Combined only when the problem needs it. This capability is also engaged on its own.
Illustrative example
One revenue figure
Three departments report different revenue totals. A shared definition, a named owner and quality checks give everyone the same number.
Data Consolidation
Scattered data, brought into one coherent environment.
Where it sits in the data journey
The business problem
Data sits in ERPs, CRMs, spreadsheets, plant systems and cloud applications that were never designed to work together.
What we do
We build batch and real-time pipelines that bring data out of each system, reconcile it, and land it in one place in a consistent shape.
Ingestion
- Batch and real-time data pipelines (ETL / ELT)
- Connections to ERP, CRM, files and cloud applications
Reconciliation
- Matching and de-duplicating records across systems
- Consolidation into a single, query-ready model
Migration
- Moving on-premises data estates to the cloud
- Consolidating legacy databases and reporting stores
Typical scope
Data pipelinesETL / ELTIntegrationReconciliationMigration
Technologies: AWS · Databricks · Microsoft Fabric
Connects to
Combined only when the problem needs it. This capability is also engaged on its own.
Illustrative example
One customer view
Customer records held separately in the ERP, the CRM and the billing system are matched and combined into one view of each customer.
Data Platforms
The right home for data: data lake, lakehouse or Fabric Data Warehouse.
Where it sits in the data journey
The business problem
Data platforms that grew by accident: hard to scale, costly to run, and slow to answer new questions.
What we do
We design and build the platform that fits your data and your team, whether that is a data lake or lakehouse, a Microsoft Fabric Data Warehouse, or a combination, on the cloud you use.
Data lake & lakehouse
- Data lake and lakehouse architecture
- Raw, processed and archive data zones
Fabric Data Warehouse
- Microsoft Fabric Data Warehouse design and build
- Migration of existing warehouses and Power BI estates to Microsoft Fabric
Cloud data platforms
- Data platforms on AWS and Databricks
- Cloud infrastructure for data workloads
Data engineering
- Orchestration and scheduling
- Performance and cost tuning
Typical scope
Data lakeLakehouseFabric Data WarehouseCloud data platformsData engineering
Technologies: Microsoft Fabric · Fabric Data Warehouse · Databricks · AWS
Connects to
Combined only when the problem needs it. This capability is also engaged on its own.
Illustrative example
A warehouse that has outgrown its server
A reporting database that no longer keeps up moves into a Fabric Data Warehouse, and the existing Power BI reports are reconnected to it.
Semantic Layer & Analytics
One business meaning for every number.
Where it sits in the data journey
The business problem
The same term means different things in different systems and teams, so the same question gets different answers.
What we do
We build the semantic layer: shared business definitions and models that sit between the data and every report, dashboard and AI tool, so everyone works from the same meaning.
Semantic layer
- Business definitions and metrics
- Semantic models
- Consistent hierarchies, calculations and access rules
Analytics
- Power BI reports and dashboards
- Power BI and Microsoft Fabric architecture
- Self-service analytics on governed data
Decision support
- Forecasting and trend analysis
- Anomaly detection
Typical scope
Business definitionsSemantic modelsPower BIMicrosoft FabricAnalytics
Technologies: Power BI · Microsoft Fabric
Connects to
Combined only when the problem needs it. This capability is also engaged on its own.
Illustrative example
What counts as an active customer
'Active customer' is defined once, with its rule, and every report, dashboard and AI query uses that same definition.
AI on Data
AI that works on your data, not around it.
Where it sits in the data journey
The business problem
AI pilots that look impressive but give unreliable answers, because the data underneath them is scattered or inconsistent.
What we do
We build AI on top of a governed data foundation and semantic layer. People can ask questions of their data, get assisted analysis, and automate the decisions the data supports.
Conversational data
- Ask questions of your data in plain language
- Answers grounded in governed data and business definitions
Applied AI
- Machine learning for forecasting, classification and anomaly detection
- AI-assisted analysis and summarisation
- Understanding documents and unstructured text
Agents & automation
- AI agents where the task and the data justify them
- Automation triggered by trusted data
- Human review designed into automated steps
Assurance
- Evaluation for accuracy, cost and latency
- Deployment in your environment or through enterprise APIs
Typical scope
Conversational dataApplied AIMachine learningAI agentsData-driven automation
Technologies: Microsoft Fabric · Databricks · AWS
Connects to
Combined only when the problem needs it. This capability is also engaged on its own.
Illustrative example
A question in plain language
A sales manager asks which regions are behind plan this quarter. The answer comes from the governed sales model, using the agreed definition of 'plan'.
IoT & Real-Time Data
Machine and device data, connected to the systems that need it.
Where it sits in the data journey
The business problem
Machines and sites produce a constant stream of data that stays on the shop floor, disconnected from the rest of the business.
What we do
We collect data from devices, sensors and industrial systems, move it in real time, and bring it into the same data environment as the rest of the business, where it can be analysed and acted on.
Device & machine data
- Sensor and device data collection
- Integration with PLCs, historians and plant systems
Real-time
- Streaming ingestion, from the edge to the cloud
- Real-time monitoring and operational analytics
- Alerts when readings move out of range
Connected to the business
- Combining machine data with ERP and maintenance data
- History kept for analysis and AI
Typical scope
Device dataMachine dataReal-time ingestionReal-time analyticsAlerts
Technologies: AWS · Microsoft Fabric · Databricks
Connects to
Combined only when the problem needs it. This capability is also engaged on its own.
Illustrative example
A motor drifting out of range
Vibration readings from a motor stream in real time. When they drift out of range, maintenance is alerted with the machine's history attached.
Not sure where your data problem sits? Working that out is usually the first thing we do together.