Databases Reference
In-Depth Information
IT owners
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Consists of IT project managers assigned to lead the implementation and support for a specific
business unit.
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Responsible for leading the IT teams to work on the initiative, the project delivery, issue
resolution, and conflict management, and work with the council to solve any issue that can impact
a wider audience.
IT teams
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Consists of members of IT teams assigned to work with a particular business team for
implementing the technology layers and supporting the program.
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Responsible for implementing the program and data governance technologies and frameworks in
the assigned projects, report to the council on issues and setbacks, and work with the council on
resolution strategies.
Data governance council
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Consists of business and IT stakeholders from each unit in the enterprise. The members are SMEs
who own the data for that business unit and are responsible for making the appropriate decisions
for the integration of the data into the enterprise architecture while maintaining their specific
requirements within the same framework.
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Responsible for:
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Data definition
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Data-quality rules
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Metadata
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Data access policy
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Encryption requirements
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Obfuscation requirements
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Master data management policies
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Issue and conflict resolution
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Data retention policies
Governance has been a major focus area for large and midsize enterprises for implementing a suc-
cessful business transformation initiative, which includes data and information management as a subcom-
ponent. Properly executed governance for both data and program aspects have benefitted enterprises by
providing confidence to the teams executing business decisions based on the information available in the
enterprise. These initiatives have increased ROI and decreased risk when implemented with the right rigor.
Technology
Implementing the program from a concept to reality within data governance falls in the technology lay-
ers. There are several different technologies that are used to implement the different aspects of govern-
ance. These include tools and technologies used in Data acquisition, Data cleansing, Data transformation
and Database code such as stored procedures, programming modules coded as application programming
interface (API), Semantic technologies and Metadata libraries.
Data quality
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Is implemented as a part of the data movement and transformation processes.
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Is developed as a combination of business rules developed in ETL/ELT programs and third-party
data enrichment processes.
