Defined scope
One dataset, report or decision with a defined business use. It can be a current data-quality problem, with no AI project planned.
Service
Data Quality & AI Readiness
A practical assessment for unreliable reports, inconsistent master data or an intended analytics or AI/ML use case. I connect one workflow or decision to the available records, definitions, ownership and quality. The result is useful even when no AI project is planned.
Call when Reports or master data cannot be trusted, or the data needed for an analytics or AI/ML use case is unclear.
What I do I trace data definitions, creation and use, test representative records, identify quality and ownership gaps and prioritise the corrections that matter to the chosen workflow.
You receive Evidence-backed quality findings, agreed data definitions, a prioritised correction plan and, where relevant, readiness and evaluation criteria for an AI/ML pilot.
Your work package
What we agree before starting
How the result is accepted
The data owner can reproduce key quality findings on the reviewed sample and agree definitions, cleanup priorities and decision criteria.
Production and ongoing support
This assessment delivers findings and an action plan. Bulk data correction, report implementation and an AI/ML pilot are separate work packages.
Typical starting points
Concrete issues this work can handle
The service is useful when the issue is specific enough to inspect in real systems, reports, data samples or daily workflows.
Conflicting reports or inconsistent master data without an AI project
Data cleanup that needs prioritisation by workflow and decision value
Documents or messages that may support extraction, classification or routing
Historical records that may support forecasting or anomaly detection
Scope and deliverables
Scope and tangible results
The scope stays close to the systems, data and workflows that affect day-to-day work. The deliverables are designed to support a clear next decision.
Scope
Operational decisions, workflows and measurable value
Structured data, documents and representative historical records
Data meaning, quality, ownership and access
Evaluation criteria, risk and human guardrails
Deliverables
Data-quality findings linked to the reviewed sample
Agreed definitions and ownership gaps
Prioritised correction actions for the chosen report or workflow
Where relevant, AI/ML readiness, evaluation needs and go/no-go criteria
How it works
How the engagement works
A defined sequence keeps the work focused. Representative examples and access to the right owners make the review faster and more reliable.
Steps
Choose the report, dataset, workflow or decision
Inspect representative records and how they are created and used
Validate definitions, quality gaps, ownership and consequences with the data owner
Prioritise corrections or define a justified next step for analytics or AI/ML
Fit and boundaries
Good fit and clear boundaries
A focused scope makes the work easier to evaluate, deliver and hand over.
Good fit
- Data or reporting owners dealing with inconsistent records and disputed figures
- Teams deciding which master-data corrections matter first
- Organisations assessing whether data can support an analytics or AI/ML use case
Not included
- Not a generic AI strategy workshop
- Not a promise that poor data can be bypassed with AI
- Not a vendor-led technology selection exercise
- Not cleanup work without a defined operational purpose
Related services
Useful next steps and related services
Operational problems can cross system, data, interface, workflow and AI/ML boundaries. These services are often relevant together, but each can also stand alone.
Applied AI/ML Pilot A specific AI/ML use case needs to be proven against real data and daily work. Interface Engineering Sprint An existing flow needs repeated corrections, or a new connection needs clear rules and reliable processing. Work Apps & Workflow Automation Manual office workflows slow work down, or a defined operational process needs a lightweight tool without replacing the core system.
Does this service fit your case?
Bring the current workflow or problem, a few representative examples and the business context. The first step is to decide what is worth changing, testing or leaving alone.