Success in Enterprise Information Management – Seven Causes

By Anne Smith

Many enterprise information management (EIM) or data management projects don’t live up to their potential. EIM technology (data dictionaries, meta data management products, data modeling, data warehousing and business intelligence, data quality) have been around for a long time. Enterprise Data Management is a mature field, even if it has been called by different names, and the field is founded on strong principles. The approaches are well-structured, cover a wide variety of situations and have worked well for many organizations. Additionally, project management processes, tools and technologies are mature and well established. So the question arises, why do data management projects / programs fail?

The answer lies in the founding perceptions of an EIM project. Should such a project be a single development initiative, or should the organization treat them as separate efforts for each component?  Actually, a combination of both views is necessary for success – enterprise and component-based.

 

Major Points to Consider

  1. EIM initiatives require significant effort, generally have high costs and require experienced management and staffing. They require sustained commitment from executives, stakeholders and staff within the organization for a long time.  Also, it is essential that the EIM program be started and maintained for the right reasons. Determining the right business goals is fundamental.  These goals must be ones that people want to be identified with and participate in.   The goals may be refined over the life of the program, but they should always relate to current business objectives for a successful implementation.
  2. Meaningful business goals provide valuable requirements. Business requirements define the scope, provide the focus and align the various EIM initiatives into a cohesive program (meta data, data governance, data quality, enterprise data architecture, data warehousing, etc.).  Each organization will choose the EIM components they want to address initially and eventually.  These choices should be driven by the business requirements.
  3.  EIM attempts to integrate diverse perceptions about business and its use of data and information.  EIM programs must be structured for shared understanding of the meaning and usage of data.  This approach points to the requirement of a data governance program that has the enterprise as its ultimate focus. The chosen stewards should strive to understand and capture the sense of the business terms and process, and catalog the context as well as the simple definition. Architect the governance program for the enterprise but start at a business unit or project level.  Remember that no data should be left unshared, and meaning is improved with cross-unit accessibility and definition.  Governance also involves any activities that revolve around data cleanliness, correctness, completeness and changes in definitions/usage.
  4. The important point to remember in an EIM initiative is that desires and concerns should not override the specific needs for which the EIM program is intended. Maintain the iterative nature of a solid development program, and ensure that the scope remains manageable. Iterative data management development can carry relatively low risks and will enable the continuation of the program despite any financial concerns.
  5. One essential point for successful EIM is the development of an enterprise conceptual data model.  This model does not require a major effort, but its benefits are demonstrable.  VERY few successful EIM programs do not have a viable enterprise conceptual data model.
  6. Experienced project management, with an EIM program focus, is another essential success factor.  EIM is a program and as such requires program management skills as well as a good understanding of each component of EIM.
  7. Although an EIM program is complex when viewed as a single unit, it can be made much simpler with attention to each of the points made here.  Accept the enterprise complexity but focus on each component for each business unit, building the program in manageable portions, until the team reaches the mountain top.

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

 
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