Data engineering & business analytics

Build a reliable data foundation

We map data sources, formats, owners, and permitted uses, then design the processes that collect, transform, and organise information. Work can cover database development and maintenance, data pipelines, quality checks, and preparation of labelled datasets for machine learning.

Good analysis needs consistent definitions. We agree what each metric means, how it is calculated, and how missing or conflicting records are handled. This helps users understand the limits of a report before relying on it.

Make analysis useful for decisions

Business intelligence and dashboards turn agreed measures into a clearer view of operations. Predictive analysis can support planning where sufficient relevant data exists, with evaluation against a baseline and explicit uncertainty.

We document provenance, access, refresh schedules, and ownership. A handover and monitoring plan helps the organisation maintain data quality as source systems and business processes change.

Start with the decision and its data

We define the operational question, the people who will act on the result and the current source of truth. Data discovery identifies source owners, access restrictions, refresh frequency and quality issues. The work can bring together business records, operational telemetry and approved external data, with explicit definitions for important measures. A shared definition matters when different departments use the same label for different calculations.

Build a reliable analytical pipeline

Data preparation includes validation, reconciliation, transformation and documented treatment of missing or inconsistent values. We preserve lineage from the report back to its sources and define who can change the logic. Dashboards, anomaly analysis or predictive models are selected according to the decision. An unusual pattern is presented for investigation with its context; it is not automatically proof of fraud, failure or causation.

Evaluate outputs and support operational use

Analytical results are tested against representative historical data and reviewed with subject-matter experts. Predictive work includes an appropriate baseline and checks for leakage between training and evaluation. We agree how users should respond to a signal, how performance will be monitored and when the method needs review. The handover covers metric definitions, access, refresh responsibilities and the limitations that decision-makers need to understand.

Make business metrics consistent across teams

Two reports can disagree because they use different definitions, cut-off times or source records. We work with the business owner to define the metric, identify its authoritative inputs and explain known exclusions. A data dictionary and reconciliation examples make that definition usable for finance, operations and management. A dashboard should expose the agreed calculation rather than conceal disagreement behind a polished chart.

Connect ERP, CRM and spreadsheet data

A practical starting point may be a recurring report assembled manually from several systems. We map how records relate, identify duplicate identifiers and define checks for missing or late data. The pipeline can retain source references and a record of transformations so a reviewer can trace an unexpected result. Access is designed around who needs the resulting dataset and its underlying detail.

Evaluate a forecast against a business decision

A forecast should be tested against a meaningful baseline and a defined use, such as planning stock or allocating capacity. We agree the evaluation period, error measure and cost of different mistakes. Performance on familiar historical data alone does not establish usefulness after deployment. Monitoring should show when the model or its inputs need review and when a person should override a recommendation.

Common questions