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A Center for CIO Leadership Summary
CIO Context
It’s been said that IT is the engine for growth and business innovation in the 21st century, and data is the gasoline that fuels it. And while data is undeniably one of the greatest assets an organization has, it is increasingly difficult to manage and control. From structured to unstructured data—including customer and employee data, metadata, trade secrets, e-mail, video, and audio—organizations must find a way to govern data in alignment with business requirements without obstructing the free flow of information and innovation.
For many organizations today, data is spread across multiple, complex silos that are isolated from each other. There are scores of redundant copies of data, and the business processes that use the data are just as redundant and tangled. There is little cross-organizational collaboration, with few defined governance and stewardship structures, roles, and responsibilities. For reasons such as these, data governance has emerged as a strategic priority for companies of all sizes. This paper describes two models that can be used as a roadmap for developing effective governance.
Overview of the Capability Maturity Model
Developed by the Software Engineering Institute (SEI) in 1984, the Capability Maturity Model (CMM) is a methodology used to develop and refine an organization’s software development process. It has five levels.
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Maturity Level 1: Processes are likely to be ad hoc, and the environment is not stable. Success reflects the competence of individuals within the organization rather than the use of proven processes. Projects frequently exceed their budgets and schedules.
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Maturity Level 2: Successes are repeatable, but the processes may not repeat for all the projects in the organization. Basic project management helps track costs and schedules, while process discipline helps ensure that existing practices are retained.
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Maturity Level 3: The organization’s set of standard processes are used to establish consistency across the organization. The standards, process descriptions, and procedures for a project are tailored from the organization’s stet of standard processes.
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Maturity Level 4: Organizations set quantitative quality goals for both process and maintenance. Selected subprocesses significantly contribute to overall process performance and are controlled using statistical and other quantitative techniques.
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Maturity Level 5: Quantitative process improvement objectives for the organization are firmly established and continually revised to reflect changing business objectives, and used as criteria in managing process improvement.
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At what maturity level is your organization? What are the steps necessary to move your organization to the next higher level?
Elements of Data Governance
The Data Governance Council Maturity Model measures data governance competencies of organizations based on the 11 crucial domains of data governance maturity. These domains can be grouped into four areas:
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Outcomes: Data risk management and compliance; value creation
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Enablers: Organizational structures and awareness; policy; stewardship
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Core disciplines: Data quality management; information life-cycle management; information security and privacy
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Supporting disciplines: Data architecture; classification and metadata; and audit information logging and reporting
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Each domain has five levels of maturity:
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Level 1: Initial
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Level 2: Managing
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Level 3: Defined
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Level 4: Quantitatively managed
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Level 5: Optimizing
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How would you rate your organization’s maturity for each of the 11 crucial domains of data governance? Why?
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