Data & AI

Usable data, not just collected data

Many organisations have data everywhere but few truly data-driven decisions. We design platforms, pipelines and dashboards that make data readable, reliable and actionable.

A good data platform connects sources, quality, governance, security, metrics and final users. It is also the right foundation for AI, automation and ESG reporting without building on fragile data.

Single view shared indicators across teams

Data quality checks on consistency and completeness

Privacy by design governed access and data

Self-service dashboards teams can use

Expected outcomes

Faster decisions

Updated dashboards and metrics reduce meetings based on different versions of the same data.

Less manual work

Automated pipelines replace exports, copy-paste and reports rebuilt every week.

AI-ready data

Clean, catalogued and governed sources enable more reliable automation and models.

ESG and benefit measurement

Environmental, social and operational indicators are connected to processes and ownership.

What we build

Data assessment

Mapping of sources, quality, ownership, access, risks, tools and decision needs.

Data pipelines

ETL/ELT, normalisation, quality checks, scheduling, logging and error management.

Dashboards and KPIs

Operational, executive, ESG, finance, sales, operations and customer care reports.

Data governance

Rules on access, catalogue, metric definitions, retention, privacy and responsibilities.

How we build a data platform

01

Business questions

We start from the decisions to support, not from the visualisation tool.
02

Source map

We identify systems, databases, files, APIs, data quality and critical issues.
03

Model and pipelines

We design structure, transformations, checks and automatic updates.
04

Adoption

We train users and owners, measure usage and keep metrics consistent.

Frequent use cases

Executive dashboards

Shared KPIs on sales, margin, operations, support, production or sustainability.

Automated reporting

Removal of recurring manual reports and fragile consolidations.

Foundation for AI

Preparation of sources and knowledge bases for chatbots, agents, forecasting and automation.

Benefit and ESG metrics

Measurement of impact, accessibility, training, consumption, emissions and social initiatives.

FAQ

Do we need to replace every business system?

No. A data platform often starts by connecting existing systems and making data more coherent.

Can we start from a few dashboards?

Yes, if they are linked to important decisions and governed sources.

Do you handle privacy and access too?

Yes. Access control, data minimisation and traceability are part of the project.

Check lineage and freshness before you trust the KPI

A dashboard can look clean and still be misleading if the number is old, incomplete, or transformed in ways that are not visible to the people using it. Before a KPI enters an operational meeting, we recommend checking three things: where the data comes from, when it was last updated, and which transformations were applied before it reached the final report.

A practical mini-checklist:

Source — identify the system of record and the owner responsible for it.
Freshness — confirm the update timing matches the decision cadence.
Transformations — review filters, joins, mappings and normalisation steps that may change the original value.
Consistency — compare the KPI with the same metric in related reports.
Traceability — make sure the path from source to dashboard can be explained without guesswork.

This is especially useful when a metric supports finance, operations, ESG or executive reporting. If the KPI cannot be traced, it is harder to trust and easier to debate.

If you want to apply this check to a real dashboard, we can connect it to the service described in Data platforms and responsible analytics.

Want to turn fragmented data into decisions?

We start from sources, metrics and critical decisions to build a concrete data roadmap.

Start a data assessment