DIG determines whether a decision is allowed — not what the answer is.
DIG is the core engine of
DecisionUniversa.
Before a decision is evaluated, DIG determines:
What evidence is admissible
What data is missing
What conclusions cannot be supported
Evaluation Outcomes
Every evaluation in DIG results in a clear determination of whether evidence can be used to support a claim, model, or decision.
Admissible
The available evidence is sufficient, relevant, and properly structured to support the question or decision being evaluated.
What it means:
Data meets required standards
Evidence is directly applicable
No critical gaps or conflicts detected
Implication:
The dataset can be used confidently for analysis, modeling, or decision-making.
Inadmissible
The evidence fails to meet one or more critical criteria, making it unsuitable.
What it means:
Missing or incomplete data
Lack of comparability
Compliance issues
Implication:
The dataset should not be used until deficiencies are resolved.
Conditionally Usable
The evidence can be used under specific constraints with controlled assumptions.
What it means:
Partial data availability
Limited comparability
Known uncertainty factors
Implication:
The dataset may be used with caution, provided limitations are acknowledged.
Evaluation Outputs
Beyond determining admissibility, DIG clarifies what the evidence can support, what is missing, and where uncertainty remains.
What the Data Can Support
Defines the specific claims, analyses, or decisions that the available evidence can reliably support.
Includes:
Valid use cases and applications
Supported variables or relationships
Scope of reliable interpretation
Outcome:
Clarifies how the dataset can be used with confidence within defined boundaries.
What Additional Data Is Required
Identifies gaps in the evidence and specifies what additional data is needed to strengthen or complete the evaluation.
Includes:
Missing variables or dimensions
Required data improvements
Additional sources or validation
inputs
Outcome:
Provides a clear path to improve dataset usability and achieve admissibility.
What Uncertainty Remains
Highlights areas where uncertainty persists, even after evaluation, due to limitations in data or context.
Includes:
Known limitations and assumptions
Confidence boundaries
Potential risks in interpretation
Outcome:
Ensures transparency and helps users account for risk in decision-making.
Evidence Gaps
When evidence is insufficient, DIG does not guess.
It specifies:
What additional data is required
What level of confidence is possible
Where uncertainty remains
System Role
DIG functions as an admissibility layer between data and conclusions.
DataUniversa
Within DataUniversa, DIG governs whether datasets can legitimately support particular claims, predictions, or analyses.
Decision Universa
Within Decision Universa, DIG governs how evidence can be used when evaluating choices.
This ensures that conclusions drawn anywhere within the system remain aligned with the strength and structure of the available evidence.
(Decision Intelligence Guardrails)
(data layer)
(decision layer)
Decision Admissibility
DIG determines whether available evidence makes a particular analysis:
Admissible
Supported by sufficient evidence
Weakly admissible
Possible but uncertain
Non-admissible
Unsupported by available evidence
When evidence is insufficient, DIG does not simply reject the analysis. Instead, it identifies the minimum additional data required to make the analysis admissible, including:
Missing data types
Required sample sizes
Comparability conditions
Necessary time horizons
This allows users to understand both the limits of the current analysis and the information required to strengthen it.
Controlled Approximation
When users choose to proceed without sufficient data:
Flags the limitation
Bounds the interpretation
Records the assumption
Why this matters to buyers
Prevents invalid model trainingPrevents false conclusionsReduces legal exposure