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An MDM consolidates important domain data into unique or linked instances (e.g. a “golden” record) and then uses that unique record as a reference point for aggregating associated data, purging duplicates, standardizing data across various applications, and creating rules to continuously resolve, merge, or disassociate records.
Do we know the business outcomes tied to data risk management? These questions drive classification. Once you have dataclassification then you can talk about whether you need to tokenize and why, or anonymize and why, or encrypt and why, etc.” Data Collaboration Data discovery has increasingly become a team sport.
So how does data intelligence support governance? Examples of governance features that leverage data intelligence include: A business glossary, with automated dataclassification, to align teams on key terms. Data lineage tracking and impact analysis reports to show transformation over time. Data lineage features.
This means that it is best used for elaborating dataclassifications in conjunction with other efficient algorithms. For instance, when used with decision trees, it learns to outline the hardest-to-classify data instances over time. But the results should be worth it.
Global policies such as data dictionaries ( business glossaries ), dataclassification tags, and additional information with metadata forms can be created by the governance team to ensure standardization and consistency within the organization.
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