Data Management. Data Governance. Master Data Management (MDM). There have been many names over the years.
A Data Management program starts with great fanfare and sometimes grandiose statements. A group of people focus on discovery and documenting data in the organization.
Then the program usually stagnates. A small team is expected to maintain the blueprints. Changes may not be communicated to that team. Worse, the organization may not have access to the information such that there is limited value from the program.
Think about a workable approach that provides ongoing value across the organization.
First, confirm the desired outcomes (ie., begin with the end in mind). Examples:
- Identification of logical entities and attributes, including a common vocabulary across the organization.
- Classification of sensitive data such as PCI, PHI, or PII.
- Identification of where data resides (at rest).
- Identification of where data is used (in transit).
- Identification of the lifecycle of data (lineage).
- Identification of whether data is encrypted at rest or in transit.
- Cross-team maintenance of this information.
- Cross-team access to this information.
Second, implement a sustainable approach:
- Identification of logical entities and attributes. Start with data that has the highest value to the business. Identify perhaps five to 10 entities (e.g. Customer, Financial Transaction, Order, Product) and the key attributes.
- Allow others to associate these entities and attributes to applications, integrations (e.g. API's), or repositories. This includes identifying the use of sensitive data.
- Review, enhance, and grow the information. Add more things to build out the Where Used of the organization.
In Step #1 don't boil the ocean by attempting to blueprint hundreds or thousands of physical entities. It's a common mistake and provides limited value. If needed, have the organization selectively blueprint physical entities after important logical entities are blueprinted, reviewed, and in use.
Step #2 has significant value since it allows people to directly contribute their knowledge. This includes ongoing changes in the IT landscape.
Finally, celebrate how the information will be used everyday across the organization:
- Business analysis or detailed design. What data is involved?
- Compliance. SOX 404 and documenting lineage in the USA? HIPAA in the USA? APPs in Australia? PIPEDA in Canada? GDPR in the EU?
- Diagnosing a problem. Where is data changed? Lineage?
- Security. Risk assessments. Tracking sensitive data and unencrypted risks.
- Staffing. Onboarding of new resources.
- Standards. Vocabulary.
- Transformation. Digital? Microservices? They all need data.
Is it time for your IT organization to have a pragmatic and sustainable approach to data management?
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