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AIMA
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Operational Management
M-QI-A: Stream A
M-QI-B: Stream B
Maturity Level 1
Maturity Level 2
Maturity Level 3
M-QI-A-1
M-QI-A-1: Are there informal or ad hoc processes to ensure basic data quality?
Siloed Data:
Data fragmented, unstructured, lacking standardized definitions.
Poor Quality:
High duplicates, missing values, and noise.
No Validation:
Absence of accuracy or relevance validation rules.
0
1
2
3
Description
Siloed Data:
Data fragmented, unstructured, lacking standardized definitions.
Poor Quality:
High duplicates, missing values, and noise.
No Validation:
Absence of accuracy or relevance validation rules.
M-QI-A-2
M-QI-A-2: Are initial integrity checks occasionally performed on data?
Initial Cleansing:
Basic data profiling and cleansing processes implemented.
Early Standards:
Initial completeness and consistency rules applied.
Metadata Tracking:
Early stages of data cataloging and metadata management.
0
1
2
3
Description
Initial Cleansing:
Basic data profiling and cleansing processes implemented.
Early Standards:
Initial completeness and consistency rules applied.
Metadata Tracking:
Early stages of data cataloging and metadata management.
M-QI-A-3
M-QI-A-3: Are formalized data quality procedures defined and regularly executed?
Standardized Metrics:
Defined metrics for accuracy, completeness, consistency, and timeliness systematically tracked.
Active Quality Management:
Continuous data quality checks, real-time scoring, LLM-specific data filters (e.g., toxicity, hallucination-prone data).
Curated Data:
Regular curation based on model feedback and bias tracking.
0
1
2
3
Description
Standardized Metrics:
Defined metrics for accuracy, completeness, consistency, and timeliness systematically tracked.
Active Quality Management:
Continuous data quality checks, real-time scoring, LLM-specific data filters (e.g., toxicity, hallucination-prone data).
Curated Data:
Regular curation based on model feedback and bias tracking.