The EIM Puzzle – August 2013

Generally, enterprise information management (EIM) is a long-term strategic initiative, but EIM can deliver short-term, tactical benefits as well. The need for both strategic and tactical approaches to data management contributes to an organization’s confusion on where to begin. Many organizations have struggled with a business rationale for understanding the need for understanding their data, although data is one of an organization’s critical assets.

Many organizations are interested in the concepts of enterprise data / information management, but when asked if they want to explore how to implement these concepts, they need help to see the need for such a strategic initiative. Management can be more concerned with addressing the immediate problems of project delivery and application problem-solving, rather than thinking about how to prevent future problems that involve data definition, data usage, data protection and data consolidation. This focus on immediate actions also can obscure the need for understanding the value of data architecture and meaning of the data, making this critical asset less useful than it should be.

This concentration on immediate issues presents a challenge to those who see the benefits of strategic planning as a foundation for information management. Well governed, properly architected and organized data would help deliver better information for improved decision making, can offer a competitive advantage and would satisfy the requirements from multiple user domains for commonly defined and managed data. The problem of persuading an organization to adopt an enterprise approach to data management lies in the fact that it is not easy to demonstrate how these benefits can be achieved quickly enough to reassure those within the organization who need to see results promptly. Balancing tactical needs with the desire for strategic planning can show an organization that paying attention to EIM can be rewarding in many dimensions and can offer results faster than may be expected.

One method that can show both immediate and strategic results for an approach is maturity modeling, which can be tailored for almost any discipline (software process, software integration, project management, data management, governance, etc.). Using a maturity model to organize the process of implementing best practices and evaluating the organization’s success at the effort is becoming more common as maturity models grow in popularity. Starting with the “Capability Maturity Model for Software Development” in the 1990s, organizations have found that this framework allows them to measure their current state, determine both interim and long-term goals for improvement, provide the best practices that will move them to the next stage and assess their progress at any point in the process.

The Capability Maturity Model for Software Development (CMM-SW) and its derivatives instantiated the concepts of examining progression toward “maturity” in some discipline. The CMM-oriented models measure how much an organization uses defined processes to manage some activity (once again: software process development, system integration, data management, etc.).

Maturity modeling is the general technique that the CMM and all related models employ. Some common points in maturity modeling are:

  • Organizations tend to adopt the parts of a method in a similar progression, leading to the development of a scale that can be used to assess the organization’s current maturity against others.
  • Mapping an organization’s current position on the scale gives an indication of its maturity in the method, both as an internal benchmark and as a gauge against others in their industry. 
  • The maturity level can show an organization the most appropriate next steps, and give an indication of the benefits of improving its maturity in the process / function.
  • Maturity levels are incremental; an organization cannot progress from Level 1 to Level 3 without having satisfied the components of Level 2.
  • Maturity can be delineated into key areas within a function, and each organization can be more mature in one or more areas than in others at the same level.

Some models start with the level “non-existent”. This level indicates that the organization has no enterprise approach to the management of data, does not understand the need for governance of data and has no stewardship functions. There is no management of master (common and reference data) and there are no programs for data quality management, or enterprise information architecture. There is no recognition of the Data Management Body of Knowledge or the DAMA-DMBOK Framework of Data Management disciplines (or another industry standard framework).

Most maturity models use a standard five-level approach. An enterprise information management (EIM) maturity model could have the following levels:

  1. Initial. Organization performs occasional, non-standardized data administration activities but has no formal enterprise approach to data management. There is no enterprise governance program, no enterprise approach to master data management, only a few specific focused data quality improvement efforts, no enterprise information architecture, etc. Recognition of the DAMA-DMBOK © and acknowledgement of the existence of a framework that applies to data management disciplines may have occurred in some areas of the organization, but no attempt has been made to implement any enterprise approach.
  2. Repeatable. Organization has adopted a data management framework, and some efforts have been made in many, if not all, of the disciplines, starting with data governance and metadata management, master data management, and enterprise information architecture as the foundations. Other disciplines have made some repeatable success based on organization’s needs.Defined. Organization has instituted formal EIM in all foundational disciplines for all business units and has made sustained progress in most disciplines for most business units.
  3. Managed. Data management is an established practice across the enterprise, involving business and information technology and management in partnership. Improvements in all disciplines are sustained and progress shown in periodic assessments.
  4. Optimized. EIM is an unquestioned and normal part of the operation of the organization, and recognized as an essential contributor to the organization’s competitive advantage.

Using such a maturity model can assist an organization in understanding EIM, and allows any organization to measure its maturity in each component of enterprise information management (data governance, master data management, data quality management, enterprise information architecture, data warehousing and business intelligence, data security, metadata management, etc..). Having the process, key areas, process indicators and methods of a complete EIM model can give an organization focus in developing and continually refining their approach to EIM and its components.

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Anne Marie Smith, Ph.D.

Anne Marie Smith, Ph.D.

Anne Marie Smith, Ph.D., is an acclaimed data management professional, consultant, author and speaker in the fields of enterprise information management, data stewardship and governance, data warehousing, data modeling, project management, business requirements management, IS strategic planning and metadata management. She holds a doctorate in Management Information Systems, and is a certified data management professional (CDMP), a certified business intelligence professional (CBIP), and holds several insurance certifications.

Anne Marie has served on the board of directors of DAMA International and on the board of the Insurance Data Management Association.  She is a member of the MIS faculty of Northcentral University and has taught at several universities. As a thought leader, Anne Marie writes frequently for data / information management publications on a variety of data-oriented topics.  She can be reached through her website at and through her LinkedIn profile at

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