Data Governance Assessments – An Opportunity to Discover

Organizations considering a new approach to enterprise data management frequently wonder about the current state of their data management efforts.  They don’t know where to start, what should be repaired, what should be left intact, and how to align the changes and continuing tasks into a cohesive practice.    In this confused state, organizations embark on less productive and sometimes conflicting paths, making data management improvement a synonym for “failed initiative”.

In the financial and accounting disciplines, audits are seen as a way to assess the current state of financial health and the solidity of the accounting practices used in the organization.  Usually, financial governance is the foundation on which the audit and subsequent improvements are built.  Similarly, an assessment of the organization’s data governance efforts and practices is considered a proper first start in developing a valid representation of the data management practices and areas of strength and weakness.

The most general definition of an audit is an evaluation of a person, organization, system, process, project or product. Audits are performed to ascertain the validity and reliability of information, and also provide an assessment of a system's internal controls.  The goal of an audit is to express an opinion on the system under evaluation based on work done in the past.  Conversely, an assessment is the process of documenting, usually in measurable terms, knowledge, skills, attitudes and beliefs about a topic.  Since assessments can be objective or subjective, the optimal assessment combines attributes of an audit while maintaining the assessment qualities of documentation and impressions.

An assessment can help an organization measure its growth and maturity in a particular area of data management or prepare the organization for a new effort.  Many companies request a data managment asssessment to determine:

• readiness for developing a data warehouse or other business intelligence initiative
• state of business and technology capabilities for meta data management
• current state of enterprise data / information management capabilities, including data governance and stewardship
• readiness for embarking on a technical or organizational change involving information management
• current state and desired direction for infrastructure or enterprise architecture

Rather than fear the assessment process or dismiss its possibilities, organizations should embrace the concept of an independent assessment of their data management practices.  A good assessment would include a review of the implementation of data governance and enterprise data management concepts and techniques.  This enables companies to learn what is working, what is not working, the reasons for each and how to improve their data management efforts.

Each assessment should be tailored to the organization’s needs, but all data management assessments should include the following points:

1. Review existing key documentation concerning data management (data governance, meta data management, data warehousing, enterprise data modeling and architecture, etc).  Some assessments will include all of these areas, other will focus on a subset.
2. Conduct interviews with stakeholders.
3. Assess current business needs for the selected areas of data management under review, including regulatory or compliance requirements as well as preferred business goals.
4. Assess technical architecture and infrastructure for the selected areas:
   • Business architecture
   • Data architecture and data models
   • Meta data architecture
   • Data integration architecture
   • Data delivery methods and tools
   • Technical architecture
   • Project architecture
   • Organization architecture
   • Security architecture, not limited to data security
5. Develop future architectures as needed (see above list).
6. Develop phased implementation plan for the selected areas of data management under review.
7. Align the areas’ plans for a unified enterprise approach to data management.
8. Recommend an overall strategy for delivering the desired state for the selected areas, including project scope, overall methodology, and guidance on appropriate items, based on best practices and industry standards.
9. Present results, begin detailed project planning for initial phase – frequently focused on implementation of data governance.

In conclusion, an assessment of the current state of data management for an organization will provide an objective review, offer an approach to improvement using best practices and industry standards where applicable, facilitate the development of business goals for data and its management, and provide impetus for refining the various architectures that affect data management.