A data readiness assessment
Map sources, owners, quality gaps and access requirements against an agreed reporting or AI use case.
03 / DATA & ANALYTICSINDIA + ASEAN
Make your data useful. For decisions and AI.
Connect fragmented data, build reliable pipelines and give teams information they can trust. Create the foundation for better decisions and useful AI.
Discuss your data prioritiesFROM SOURCES TO TRUSTED DATA
THE STARTING POINT
Start with a decision. Build the data behind it.
We work backwards from the reports, decisions and AI use cases your teams need. We assess the sources, quality and access constraints, then design a data platform and pipelines sized to that work.
Useful data often sits across business applications, databases and spreadsheets. Conflicting definitions, manual preparation and unreliable refreshes make it hard to trust. We connect the sources and make quality, ownership and access explicit.
WHAT WE DELIVER
A clear scope.
Something you can use.
A practical data foundation
Design and implement scoped ingestion, transformation and storage using a lake, warehouse or existing platform where appropriate.
Analytics people can use
Agree business definitions, build useful data models and dashboards, and validate reports with the people making decisions.
Quality and operational ownership
Put validation, access controls, pipeline monitoring and runbooks in place, with a named owner for ongoing operations.
WHERE IT APPLIES
Data platforms and engineering
Connect operational systems through repeatable pipelines, with quality checks and traceable transformations.
BI and analytics modernization
Assess existing reports, preserve essential definitions and modernize dashboards around a clear business question.
Data readiness for AI
Prepare approved data for retrieval, model evaluation and AI workflows. For sovereign AI, map where data, embeddings and backups may be stored and processed, who can access them and how long they are kept.
SECURITY AND SAFETY
Security by design.
Safety throughout.
Protect systems and data. Evaluate how AI behaves and the consequences of its actions. Agree controls and evidence for the risks in each engagement.
Protect data by design
Agree data ownership, permitted uses, access and retention. Apply these rules to source data, embeddings, logs and backups, including where each may be stored and processed.
Make quality traceable
Validate transformations, data quality and lineage. Check retrieval and reporting permissions so downstream users only see data they are allowed to access.
Operate with care
Monitor access and pipeline failures, test recovery and define owners for incidents and data changes. Evaluate downstream AI risks when data is used for AI.
DEFINE SUCCESS FIRST
Measure the work.
Then the difference.
We agree a baseline, target, measurement method and accountable reviewer, alongside security and safety acceptance criteria. These are measures to consider, not promised results.
Verifiability in practice
Trace reported figures to their sources and transformations. Keep versioned definitions, data quality checks and reconciliations so the customer can reproduce the calculation and investigate discrepancies.
- Data completeness and accuracy
- Pipeline reliability and data freshness
- Time spent preparing reports
- Adoption of trusted reports and datasets
GOOD QUESTIONS
Before we begin.
Can we engage you for data work without an AI project?
Yes. Data engineering, platform implementation and analytics are standalone offerings. We scope the work around the decision or operational need you want to improve.
Do we need to replace our existing data platform?
Not necessarily. We assess what already works and where the gaps are. A focused pipeline, quality improvement or reporting change may be enough.
Can you help with both analytics and AI readiness?
Yes. They often share foundations, but need different validation. We agree the data sources, business definitions, access controls and quality checks for each use case.
YOUR NEXT STEP