It is the perspective that asks: is this data still teaching the model something true?
Data determines whether the organization keeps learning from the world or starts learning from itself — and whether data can still be trusted once it's in motion, not just on arrival.
Data is two jobs wearing one name: making sure what feeds the AI is something it can learn the right lessons from, and making sure what reaches people is something they can actually use — the Human-Direction System and the AI-Application System, data's version of the same split every perspective runs on.
For people: are the numbers you're deciding on actually correct, and are you seeing the whole situation? For the AI system: is what's feeding the model verified as correct, and does it have the full picture or the slice that was easiest to collect?
For people: does this data actually match the problem, or is it just what happened to be available? For the AI system: does training data reflect the real population it'll operate on, including rare cases, or only the common ones that were easy to collect?
For people: do two teams looking at the same term actually mean the same thing? For the AI system: does a given term mean the same thing everywhere it shows up in the pipeline? "Customer" and "churn" shifting meaning isn't inconsistent data — it's several datasets wearing the same name.
For people: can you get the data you need without filing a request and waiting a week? For the AI system: can it reach the data it needs at the moment a decision runs, or does it only show up in reporting after the fact?
For people: can someone trace a number back to where it originated, and would you know if the dataset changed last week? For the AI system: can you trace what trained this model — every input, which version, where it came from?
For people: do the people handling this data know what's sensitive and who's allowed to see it? For the AI system: is output actively monitored for bias and misuse of sensitive information, or was that only checked once, at launch?
For people: do decision-makers actually understand what a figure represents, or has each team quietly built its own translation? For the AI system: does the same input get treated the same way regardless of which part of the pipeline touches it?
For people: is there a real owner for each important dataset? For the AI system: is drift tracked closely enough to catch a bad pattern before it compounds, with an actual mechanism to roll back or retrain once one's found?
The Boston Housing dataset passed every check anyone ran against it for decades and was still teaching something false the entire time, because nobody was watching after the day it got approved. Once a system starts generating its own data — recommendations, summaries, decisions — the organization's record of "what happened" starts turning into a record of what the system did.
What the Data perspective looks like at each of the six SIMA360 Maturity Levels.
Data is present but not yet a managed decision asset. Teams can't reliably say what data would be needed to support a given decision. The work is narrow: identify the elements that matter most to the first decisions to improve.
Data exists but isn't a reliable decision asset — fragmented across systems, definitions varying by team, reconciliation happening informally. Shared definitions for the critical elements are what's needed, not a full data strategy.
Data supports specific use cases, but the improvements are context-bound — dependent on local assumptions that don't generalize. A data-driven decision that works in one area often degrades when tried elsewhere.
Definitions are standardized and governed across the organization — which creates a new blind spot. The system can't tell a quality failure from representational drift: data that passes every check while no longer representing reality.
Feedback loops are operational rather than theoretical. When a decision fails, the organization can trace whether the cause was completeness, timeliness, relevance, or drift, and correct it. The temptation is expanding faster than feedback can keep up.
Data strategy is tied directly to decision strategy — the organization anticipates where current data will fall short before a new decision gets introduced, rather than waiting for the failure.
Data rarely fails all at once. These are the signals the book points to — each one tracing back to a specific component.
Validation scores hold and the model looks fine on paper, but people are quietly working around it, overriding it, redoing the work themselves.
Interpretation and Application and Continuous Monitoring and Correction failing together — nobody built a way to act on what the workarounds are actually saying.
A growing share of what feeds the next version is the system's own prior output, not anything observed independently.
Recursive reinforcement inside the data pipeline — exactly what Traceability and Lineage and Continuous Monitoring and Correction are supposed to catch together.
The system performs well on common cases and poorly on rare, consequential ones — the rare cases don't move a dashboard average.
Relevance and Representativeness failing exactly where it matters most — the edge cases were never represented on purpose.
A workflow changes, customer behavior shifts, an upstream system updates — and the meaning of the incoming data moves before anyone catches it.
Consistency and Definition and Accessibility and Timeliness failing together — the term still looks the same, but what it actually measures has quietly changed.
Past decisions get fed back into training as if they were objectively correct, now repeated by a machine at scale and with total confidence.
Traceability and Lineage and Accuracy and Completeness both skipped — nobody traced where the label came from, and nobody checked whether it was actually right.
Measures your current Data maturity level across Accuracy and Completeness, Relevance and Representativeness, Consistency and Definition, Traceability and Lineage, Security and Ethics, and Continuous Monitoring and Correction.
Structures the improvement cycle for data capability — from separating a structural quality failure out from representational drift to building the feedback loop that catches recursive reinforcement.
Provides shared-definition templates, representativeness and lineage-tracing checklists, security-and-ethics review guides, and drift-monitoring playbooks.
Builds practitioner skills in data management for AI — including data literacy for non-technical stakeholders, data stewardship practices, and AI data pipeline operations.
SIMA-Probe measures your Data maturity level and identifies the gaps most likely to undermine your AI investments.