EIM Component Framework Dependencies – Part 1

By Mike Jennings

Is your organization realizing its full potential from its EIM initiatives?

Many organizations struggle to obtain the full benefits of their EIM initiative by approaching the framework components as a set of projects and not as an interdependent program. This type of approach leads to a series of enterprise initiatives without a cohesive information strategy or goals. In this type of method, the EIM Framework component projects fail to leverage the co-dependency and benefits required to build an efficient and agile data management organization with enhanced capabilities for information creation, capture, distribution, and consumption.

Figure 1- Framework for Enterprise Information Management

This is the first part of a two part series examining the interrelationships and dependencies between the various components comprising the EIM Framework. In this installment of the series, we will look at some of the guiding principles that need to be adhered to in a successful EIM program. In the second part of this series, we will look deeper at the dependencies between the EIM components to gain a better understanding of their value and importance.

There are some critical foundational areas and key activities that must be addressed before an organization can begin to use EIM principles and practices effectively to support existing or planned enterprise projects. While time is of the essence in many organizations and your company must move as quickly as possible to support and address these critical enterprise projects you need an understanding of the base foundational activities and dependencies. To begin this understanding about EIM, there areguiding principles that represent fundamental basis about Enterprise Information Management and how to implement it. These key principles should shape andinform all organization recommendations concerning EIM. They also demonstrate some of the key dependencies that need to exist between the EIM components for an organization program to be successful. These key principles may seem obvious, but many EIM efforts have failed by not adopting them.

  1. Data and information are enterprise assets.
    1. As such, they must be managed to ensure quality, appropriate use and maximum business value.
    2. Enterprise data orientation requires a conscious change of focus from individually developed applications to a unified, cross-functional view of data and its use as the foundation of information and knowledge.
    3. Enterprise thinking requires stepping outside and beyond functional job responsibilities, interests and perspectives.
  2. Management of data assets is a shared responsibility between business data stewards and technical data stewards.
    1. Business Data Stewards: Trustees of enterprise data assets, with assigned ownership and accountability on behalf of the enterprise.
    2. Technical Data Stewards: Custodians of enterprise data assets, providing professional services and guidance under the governance of Business Data Stewards.
  3. Data stewardship and governance are most effective when all participants take an enterprise-wide perspective.
    1. An enterprise-wide Data Governance Council (DGC) should plan and oversee EIM strategy, policies, standards, projects and procedures, chaired by a Chief (Business) Data Steward.
    2. Data Stewardship Teams under the DGC should be organized by subject area, not by organization, function or application.
    3. However, no two data stewardship and governance programs are alike – each must be customized for the organization and its culture.
  4. Metadata is the key to managing data assets.
    1. The managed meta data environment (MME) is the platform supporting effective data management.
    2. Meta data management enables data governance and provides the characteristics to measure data quality.
    3. Meta data is the fabric that connects all of the other components of EIM, it is key for EIM success.
    4. Meta data (including data definitions) is an enterprise resource contributing directly to improved information quality, reference & master data, and enterprise capabilities for data usage.
  5. The enterprise information architecture provides the blueprint for managing data assets.
    1. The focus of information architecture is to define and use master blueprints for semantic and physical integration of enterprise data assets.
    2. These master blueprints provide a clear definition of how the data is structured, collected, shared, maintained, and stored from both the IT and business community perspectives.
  6. Enterprisedata warehousing and business intelligence is the most effective means of enabling more informed and effective decision-making.
    1. Data warehousing / business intelligence environments must be managed to make data easy to access, understand, manipulate and safeguard.
  7. EnterpriseInformation Management is not an easy effort; it is a strategic commitment and can be accomplished with consistent dedication from all parts of the enterprise.
    1. Most organizations underestimate the complexity of EIM and the difficulty in establishing it.
    2. EIM requires significant cultural changes and can be a very challenging effort.
    3. EIM can be accomplished and has been successful implemented at many different types of organizations.

Organizations can realize many benefits from embracing an Enterprise Information Management initiative by adopting these key principles into their program and understanding the interdependency the framework components share and demand for success. We will be exploring in greater depth this inter-reliance between the EIM components in next month’s installment.

About the Author

Michael Jennings is a recognized industry expert in enterprise information management, business intelligence/data warehousing and managed meta data environment. He has more than twenty years of information technology experience in government, manufacturing, telecommunications, insurance, and human resources industries. Mike has published numerous industry articles for DM Review and Intelligent Enterprise magazines. He has been a judge for the 2002 - 2007 DM Review World-Class Solutions & Innovative Solution Awards and 2003 Wilshire Award for Best Practices in Meta Data Management. Mike speaks frequently on enterprise information/architecture issues at major industry conferences and has been an instructor of information technology at the University of Chicago's Graham School. He is a co-author of the book “Universal Meta Data Models” and a contributing author of the book “Building and Managing the Meta Data Repository”. He may be reached at MJennings@EWSolutions.com

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