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.

About the Author

Anne Marie Smith is a leading consultant in Information Management and is a frequent contributor to various IS publications. Anne Marie has over 20 years experience in information management for several corporate entities and has successfully led the development of data resource management departments within corporations and consulting organizations. Anne Marie is active in the local chapter of DAMA and serves on the board of directors of DAMA International, and is an advisor to the DM Forum. She has been an instructor of Management Information Systems (MIS) with Philadelphia, PA area colleges and universities. Anne Marie has taught topics such as: data stewardship and governance, data warehousing, business requirements gathering and analysis, metadata management and metadata strategy, information systems and data warehouse project management. Anne Marie’s areas of consulting expertise include metadata management including data stewardship and governance, information systems planning, systems analysis and design, project management, data warehouse systems assessment and development, information systems process improvement and information resource management/data resource management. Anne Marie holds the degrees Bachelor of Arts and a Master's of Business Administration in Management Information Systems from La Salle University; she has earned a PhD in MIS at Northcentral University. She is a certified logical data and process modeler and holds project management certification. Anne Marie can be reached at amsmith@ewsolutions.com