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Seven Causes of Success in Enterprise Data Management

Seven Causes of Success in Enterprise Data Management

Many organizations consider Enterprise Information Management (EIM) to be too complex and not worth the design and implementation effort.  Adhering to some best practices can alleviate their concerns

Many enterprise information management (EIM) or data management projects do not live up to their potential.  EIM functions and technologies (data governance, data dictionaries, metadata management, 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.  Therefore, the question arises, why do data management projects / programs fail?

The answer lies in the founding perceptions of an EIM initiative.  Should EIM be a single development initiative, or should the organization treat each component as separate efforts?  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.  In addition, 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 encourage people want to participate in the program and be convinced of success.  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 (metadata, 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 business data 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.  Design the data 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.  Data 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 throughout the 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 subject area model and conceptual data model.  These models do not require a major effort, but the benefits are demonstrable.  VERY few successful EIM programs do not have a viable enterprise subject area and 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 solid 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.

Conclusion

Any organization can be successful in designing and implementing an enterprise information management (EIM) program by following these best practices and retaining the organization’s focus on managing data and information as enterprise assets.

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Anne Marie Smith, Ph.D.

Anne Marie Smith, Ph.D. is an internationally recognized expert in the fields of enterprise data management, data governance, enterprise data architecture and data warehousing. Dr. Smith is VP of Education and Chief Methodologist of Enterprise Warehousing Solutions, Inc. (EWS), a Chicago-based enterprise data management consultancy dedicated to providing clients with best-in-class solutions. Author of numerous articles and Fellow of the Institute for Information Management (IIM), Dr. Smith is also a well-known speaker in her areas of expertise at conferences and symposia.

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