Data intelligence
& analytics.

Most analytics problems are definition problems

When two dashboards disagree about revenue, the cause is almost never a broken pipeline. It is that two teams defined revenue differently — one net of refunds, one gross; one on order date, one on fulfilment — and both are correct within their own logic. No amount of tooling resolves this, because it is a governance question.

So the first deliverable on most data projects is not a pipeline. It is an agreed set of metric definitions, written down, with an owner for each. Which timestamp counts. What happens to cancellations, partial refunds, and test accounts. Which currency conversion and at which rate. This work is unpopular and it is the difference between a warehouse people trust and one they quietly work around with spreadsheets.

Once definitions exist, they belong in one place — a semantic layer or a modelled transformation layer — so every dashboard, report, and model reads the same logic. Metrics reimplemented in each BI tool will diverge; this is not a discipline problem but an inevitability.

Building pipelines that survive

We build in layers, because it makes failures diagnosable. Raw ingested data is landed unmodified and retained, so a transformation bug is recoverable without re-extracting from source systems. A cleaned and conformed layer handles typing, deduplication, and entity resolution — reconciling the fact that the same customer appears with three spellings across four systems, which is usually the hardest genuine work in the project. A modelled layer holds business definitions. Presentation views serve specific consumers.

Data quality is enforced as tests in the pipeline rather than discovered by a user. Row count and freshness assertions, uniqueness and referential checks, distribution bounds on important fields. A pipeline that fails loudly is far more valuable than one that silently propagates a schema change into a dashboard that executives are reading.

We also make lineage visible, because when a number looks wrong the first question is always where it came from. Being able to trace a figure on a dashboard back through transformations to a source record turns a two-day investigation into a five-minute one.

From reporting to decisions

A dashboard describes what happened. It is genuinely valuable and it is where most data investment stops, which is why so many organisations have extensive reporting and no measurable change in decisions.

The step that produces return is attaching analytics to a decision that recurs. Demand forecasts that feed purchasing rather than sitting in a report. Anomaly alerts on revenue and data quality that route to someone who can act — designed with the base rate in mind, because an alert that fires ten times per real issue gets ignored, as covered under anomaly detection. Segmentation that changes how customers are treated rather than describing them, which means validating that segments actually respond differently rather than trusting a clustering score, per unsupervised learning.

This is also where the data foundation pays off twice. The modelled, tested, well-defined layer that makes reporting trustworthy is the same foundation that makes machine learning possible — and its absence is the most common reason ML projects stall long before any modelling begins.