The Maturity Model for Data Governance program at an enterprise.

1. Data Governance Maturity Levels
Author: Sowmya Kandregula

2. Data Governance:
Data governance ensures that the data is relevant and that identified changes to the
status quo, like internal processes and system changes and market disruptions, are
judiciously reviewed to ensure that they do not negatively impact the relevance,
timeliness or quality of the data.
Business benefits of data governance?
Governance ties all the information assets and technology components together and
is responsible for achieving the synergies needed for operational excellence; this
typically includes data/information governance framework, charter, policy, process,
controls standards and the architecture to support enterprise-wide data governance.
Data Governance Capabilities
How can your organization comply with data-privacy laws and still perform effective
data analytics, including analysis of historical data? Data Governance module makes it
possible to meet enterprise priorities while remaining compliant with ever more
stringent data-privacy laws.

3. Maturity Levels of Data Governance
The foundation of this multi-level structure is the Data Management Maturity Model
(DMM), which is a branch of Capability Maturity Model (CMM), a registered service
mark of Carnegie Mellon University.
Aspects of data governance were blended into this model to come up with specific
characteristics of each level.
Level Characteristics
1 Initial • Processes and policies mostly undocumented, driven in ad-hoc,
uncontrolled and reactive manners by users or events;
• Lacks strict rules and procedures on most aspects of data management;
• Few, if any, attempts to catalog what already exists;
• Data may exist in multiple files and databases, may be used in multiple
known and unknown formats; may be stored across multiple systems by
different names and using different data types;
• Quality of data depends on skill level of technical personnel;
• Organization may take on monumental tasks with little knowledge of their
impact.

4. Level Characteristics
2 Repeatable • Some policies and processes exist and possibly repeatable, with consistent
results;
• Process discipline is unlikely to be rigorous and is unlikely to be sustained
during time of stress;
• Data governance function exists but not institutionalized; relies on a
central person or group to understand issues and propose measures;
• Differences between business and technical aspects of data are
understood to certain extent but quality of data still largely depends on
skill level of technical personnel;
• Lacks effort to capture and document business meaning of data; little or
no differentiation exists between logical and physical data design;
• May begin to institute data governance practices focused on a specific
type of data used for business unit reporting.
3 Defined • Defined and documented policies and processes exist and subject to some
degree of improvement over time;
• Have created an enterprise wide data governance function and
established it as a core component of data lifecycle;
• Data assets and related IT assets fully cataloged and centrally maintained;
• Typically understand business meanings of data for most (if not all) data
sets;
• Enforce and test to ensure that data quality requirements are defined and
met;
• Use tools to record and maintain data governance documentation and
proactively monitors data governance performance.

5. Level Characteristics
4 Managed • Enterprise wide visibility to IT and data end-user on all data assets (what
data, where, how to get access etc.);
• Executive level buy-in to support ‘data is a corporate asset’ maxim;
• A managed metadata solution is established to catalog and maintain
metadata for corporate data structure;
• Data governance function is involved to certain extent all development
efforts to assist in the cataloging of metadata and reduction of redundant
data elements;
• No change is ever introduced into a production data store without prior
scrutiny by data governance function and documented within metadata
repository;
• Begin to conduct data audits to gauge production data quality.
5 Optimized • Use practices involved in Level 1 to 4 to continually improve data
availability, usability, integrity, security and database performances.