Data analytics solutions development

Most companies that come to us already have reports. What they do not have is a reliable answer to the question a manager is actually asking on a Tuesday afternoon: what changed, why, and what should we do about it. That gap is the real product of an analytics engagement – not charts, but answers arriving where decisions are made, at the moment they are made.

We frame every engagement on the analytics ladder. Each rung – descriptive, diagnostic, predictive, prescriptive – answers a more valuable question than the last, and each demands more of the data underneath it. The expensive mistake is jumping rungs: commissioning forecasts while the historical record is still inconsistent, or buying a prescriptive engine when nobody trusts last month's numbers. Diagnosing which rung your decisions actually need, and which rung your data can currently support, is the first thing we do.

That diagnosis keeps budgets honest. Sometimes the highest-return project is unglamorous descriptive work that finally makes one number mean the same thing in every meeting.

The hard part of analytics development is rarely the analytics. It is the years of operational data sitting in CRMs, ERPs, spreadsheets and third-party tools, each with its own identifiers, time zones and ideas about what a customer is. Until that estate is mapped and reconciled, every chart built on top of it is an argument waiting to happen. The foundation problems we meet most often:

• The same metric computed three different ways in three departments
• Source systems that silently change schemas and break downstream reports
• History too thin or too dirty to support any predictive work
• Nobody accountable when a number on a dashboard turns out wrong

We treat these as engineering problems with engineering answers: validated ingestion pipelines that fail loudly rather than load quietly, a semantic layer that pins each definition in one place, and explicit data ownership written down per source. This work is invisible in a demo and decisive in month six, when the business has started making real commitments on what the platform says.

There is no single analytics stack, and a vendor who proposes one before seeing your data is selling a preference. The decisions that matter are sequencing decisions: batch or streaming ingestion, warehouse or lakehouse storage, an off-the-shelf BI front end or visualisation built into your own product. We make each call against three constraints – the volume and shape of your data, the skills of the team who will run the platform after us, and the budget rung you are buying.

Machine learning enters the picture at the predictive rung, and only when the foundations can feed it: models trained on inconsistent history produce confident nonsense. For route optimisation work we have used deep learning over visit patterns; for many clients a well-specified statistical forecast does the job at a fraction of the complexity. Choosing the simplest method that answers the question is a discipline, not a compromise.

We also bias towards managed services over self-hosted infrastructure where data volumes allow, because the total cost of an analytics platform is dominated by the people who keep it running, not the licences.

Where the model layer itself is the product rather than one rung of the ladder, that work belongs with our AI and ML development services.

The top rung of the ladder is not a report at all. Prescriptive analytics earns its keep when the recommendation arrives inside the workflow where the decision happens – a planner's schedule, a manager's approval queue, a rep's morning route – already shaped into an action someone can take or decline. That is decision support, and it changes how a system has to be engineered: latency, explanation and trust in the output start to matter as much as accuracy.

We built exactly this for a major pharmaceutical distributor: a system that learns from visit patterns and GPS data to predict the best routes for medical sales representatives, turning historical field data into each day's working plan. The proof of concept stood in three weeks, the solution was delivered in six, and the client – in Julia Dvornikova's words on this page – saw measurable gains in field efficiency.

Engagements at this rung include a feedback loop: the system records which recommendations were followed and what happened next, so the analytics improve on real outcomes rather than assumptions.

The full story is in our case study on optimising medical sales representative routes with AI.

Analytics work prices on three drivers: how many source systems must be connected and cleaned, which rung of the ladder you are buying, and how far the outputs reach into your operations. A descriptive platform over two clean systems is a different engagement from prescriptive decision support across a field organisation. We scope against those drivers in a short discovery, then staff a compact team – management and analytics, developers, UI/UX and testing – sized to the rung rather than a fixed bench.

Launch is the midpoint, not the end. Pipelines run unattended only because someone built monitoring, alerting and a recovery path for every failure mode; dashboards stay trusted only because data quality checks run on every load. We hand over with runbooks and training so your team operates the platform, and we stay for support or the next rung only when that earns its place.

Cost control is also an architecture concern: we design storage and compute so the monthly bill scales with use, and document where the levers are.

For telemetry analytics embedded in daily field operations, see our GPS monitoring solution for sales agents.