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Data is a vital asset for enterprises of all kinds. Smart businesses are seeking new strategies to manage their growing data repositories.

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? Anira's Data Governance module makes it possible to meet enterprise priorities while remaining compliant with ever more stringent data-privacy laws.

Maturity Levels of Anira`s 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 Level	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.
2	Repeatable level	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 Level	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.
4	Managed Level	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 Level	Use practices involved in Level 1 to 4 to continually improve data availability, usability, integrity, security and database performances.