Make data trustworthy for the decision before asking it to power AI
Data readiness is specific to a decision and use, not a property a platform can declare globally. MTera connects business meaning, ownership, provenance, quality controls and engineering to the decisions the data must support, then defines a practical route from current gaps to dependable analytical and AI use.
Reports disagree because key terms are interpreted differently.
Quality issues are measured but not prioritised by business consequence.
Ownership exists on paper but decision rights remain unclear.
Data used for a model cannot be traced to origin and transformation.
Teams cannot explain whether historical data fits the intended use.
AI work begins before access, retention and human oversight are designed.
02
Why partial fixes fail
Centralising data does not create common meaning, and a catalogue does not create accountability. Generic quality scores can also hide the defects that matter most for a particular decision.
03
Business consequence
Leaders act on inconsistent evidence, teams spend time reconciling results and AI introduces decisions that are difficult to explain, monitor or own.
04 · Target state
Critical data has clear meaning, permitted use, accountable owners, traceable lineage and controls tied to decision risk; model and human responsibilities are designed before production use.
05
Approach
01Begin with the decision, user and consequence of error.
02Map critical data elements, sources, transformations and owners.
03Define fit-for-use quality and evidence requirements.
04Assess process, integration and operating-model gaps.
05Prioritise changes around use-case value and risk.
06
Key decisions
01What data is material to this decision and why?
02Who may define, change, access and approve its use?
03Which quality failures require prevention, detection or human review?
04What evidence must be retained for explanation and challenge?
