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Data and intelligence
What we do
We build the data layer the business decides on. We integrate data from the systems that generate it, govern it with single agreed definitions, and deliver it as information that can be used both in the operation and in the boardroom.
We do not deliver isolated reports. We design platforms where data has a traceable origin, an owner, measured quality and a refresh cycle the operation can sustain.
How we approach it
We start from decisions, not from tables. We identify what is decided, how often, and with what tolerable margin of error, and from there we define metrics, granularity and latency. Anything that does not support a decision stays out of the model.
Data governance is designed from day one: agreed definitions, explicit lineage, automated quality controls and assigned ownership. Without that, any dashboard ends up being argued over instead of used.
Value we create
One version of the data, which operations and management work from without manual reconciliation or disputed figures. Decisions are made on verifiable information, as current as each case requires.
The cumulative effect is less time spent producing the number and more spent acting on it, with the ability to anticipate deviations rather than explain them afterwards.
Enterprise data platforms
- What it is
- Design and construction of the infrastructure that ingests, integrates, governs and serves business data, from the systems that originate it to its analytical and operational consumers.
- How we understand it
- A data platform is production infrastructure, not an analytics project. It is designed to the same standards of availability, change control and observability as any critical system.
- What it solves
- Data scattered across systems, manual loads, contradictory definitions, and an analytics layer that breaks every time a source changes.
- Value it creates
- Integrated, traceable data with measured quality, available as often as the operation needs and sustainable without recurring manual effort.
Business Intelligence and executive analytics
- What it is
- Modelling and delivery of the information management and area leads use to run the business: agreed indicators, comparable over time and drawn from a single source.
- How we understand it
- BI is not a visualisation layer. It is a prior agreement on what is measured and how it is calculated; without that agreement, every area arrives at the meeting with its own figure.
- What it solves
- Reports that do not reconcile, metrics whose definition changes depending on who builds them, and decisions made on stale information.
- Value it creates
- A stable, auditable indicator framework that keeps the management conversation on facts and shortens the gap between the business event and the decision.
Advanced analytics and predictive models
- What it is
- Development of statistical and machine learning models that anticipate behaviour —demand, risk, churn, failure— and are embedded in the process that has to act on the prediction.
- How we understand it
- A model is only worth building if someone changes a decision because of it. We design backwards from the resulting action, including the threshold, the owner and the cost of being wrong in each direction.
- What it solves
- Models that never leave the analysis notebook, predictions with no operational owner, and lab accuracy that does not hold up against real data.
- Value it creates
- Anticipation built into the operation, with model performance monitored in production and an explicit rule for when to retrain or retire it.
Operational and strategic dashboards
- What it is
- Design of the dashboards each level of the organisation uses to follow its operation: the shift and the line operationally, the result and the deviation at management level.
- How we understand it
- A dashboard is designed for a specific management routine —a meeting, a shift, a close— not to show everything available. What is not reviewed on a cycle does not belong there.
- What it solves
- Crowded panels nobody opens, indicators with no threshold or owner, and too much distance between spotting a deviation and correcting it.
- Value it creates
- Focused monitoring on a defined cadence, actionable alerts, and a clear chain from the indicator to the cause to the person who can act.