The EIM Puzzle – August 2014

EIM-PuzzleIn the world of terms and phrases, confusion can develop quickly when people use one term interchangeably, or combine the definitions of two or more items into one term. This phenomenon seems to be happening with the terms “enterprise information management” and “data governance”. Many organizations, and people, seem to be confusing one for the other, or ascribing the definition and attributes of one for the other term. In fact, enterprise information management (EIM) is not the same thing as data governance – and it is important to understand how they differ and why these differences matter.

Enterprise Information Management is defined as a program that manages the people, processes and technology in an enterprise to control the structure, processing, delivery and usage of information required for management and business intelligence purposes.  Enterprise Information Management (EIM) consists of a collection of domains, all related to the goal of providing and preserving information as a business asset that remains secure, easily accessible, meaningful, accurate and timely.

Data governance is a holistic approach to managing corporate data and information by implementing processes, roles, controls and plans that treat data/information as a valuable business asset. Data Governance is one of the foundational domain components of an Enterprise Information Management program, but it is not synonymous with EIM. It is only part of a complete EIM program – and should not be confused with all of enterprise information management.

Enterprise Information Management, as defined in the DAMA International Data Management Body of Knowledge © (DAMA-DMBOK), consists of:

  • Data Governance – planning, oversight, and control over management of data and the use of data and data-related resources; development and implementation of policies and decision rights over the use of data.
  • Data Architecture – the overall structure of data and data-related resources as an integral part of the enterprise architecture.
  • Data Modeling & Design – analysis, design, building, testing, and maintenance of data.
  • Data Storage & Operations –  structured physical data assets storage deployment and management.
  • Data Security – ensuring privacy, confidentiality and appropriate access to data.
  • Data Integration & Interoperability – acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization and operational support.
  • Documents & Content Management – storing, protecting, indexing, and enabling access to data found in unstructured sources (electronic files and physical records), and making this data available for integration and interoperability with structured (database) data.
  • Reference & Master Data – Managing shared data to reduce redundancy and ensure better data quality through standardized definition and use of common data values.
  • Data Warehousing & Business Intelligence – managing analytical data processing and enabling access to decision support data for reporting and analysis.
  • Metadata – collecting, categorizing, maintaining, integrating, controlling, managing, and delivering the context of data (definitions, calculations, descriptions, sources, etc.).
  • Data Quality – defining, monitoring, maintaining data integrity, and improving accuracy, completeness, validity, timeliness, consistency of data.
As a program, EIM manages all these component disciplines, so they operate in harmony, to achieve the organization’s goals to improve the return on the use of information assets. Data governance, as a foundational component of enterprise information management, allows a company to develop and implement the policies that establish responsibility for the care and control of the organization’s critical data. It is the central component of an organization’s EIM effort, but it is only one part of that EIM program.

To be effective, data governance needs the other EIM component disciplines, some for support and others for input for the processes and content that data governance creates. Data governance needs metadata management capabilities in the organization so the stewards can define the critical data according to standards; data governance needs data quality management so stewards know how to support the data quality professionals in their activities, such as profiling; data governance needs master data management as the stewards identify the common data and support the MDM specialists in their activities, etc. The EIM program sees that all these parts are connected and that no gaps exist and that any redundant efforts are eliminated.

Why is it important to distinguish between EIM and data governance when naming the programs? Does it matter what a company calls the program as long as the function is performed? Yes, it matters, and it matters a great deal. Companies need Enterprise Information Management – all the component disciplines – to manage their data and information effectively and efficiently. Focusing only on data governance leaves out the other 10 components, thereby ignoring the benefits of data quality, metadata management, data architecture and data modeling, etc., as stated above.

If the company calls their EIM program “data governance” will they know they should connect the other components to achieve the maximum benefit? They may overlook the other aspects of EIM and not include master data management, they may not see the need to develop a data architecture, and they may not make the connections between proper metadata management (or data quality) and the performance of data stewardship. Also, they may not develop the foundational EIM program needed to organize all the components, thereby forgoing the benefits of coordinated information management (seeing that the data quality program is aligned with the master data management program properly, enabling the data governance program to support the data architecture and data modeling efforts with business metadata, etc.).

Having only a “data governance” program will not provide a company with the necessary information architecture, master data management, professional data quality management or other parts of EIM that really enable the organization to improve the value of its information across all departments and functions. Although a data governance program is the start of any successful EIM initiative, it is not the only component and data governance is not synonymous with enterprise information management.

 As organizations become more aware of the need to treat data and information as corporate assets, they will become more aware of the need to develop enterprise information strategies that include all the parts of EIM and the connections that support the strategic management of data and information assets, through enterprise information management programs that include data governance and the other disciplines, and they will become aware of the need to use the correct terms for each component and for the overarching program that supports all the components (EIM).

An understanding of EIM and of data governance will allow organizations to identify gaps in their approaches and make the necessary changes in their organization that will enable them to develop a holistic view of data and information for their situation. Knowing the differences between EIM and data governance gives the organization the opportunity to incorporate industry best practices for all aspects of information management into the company’s information strategies, not just for data governance.

The implementation of the strategies may be incremental, and data governance may be the foundational discipline of the program, but the implementation of enterprise information management cannot stop at data governance. EIM must be called by its proper term to ensure that EIM receives the recognition it needs to secure its place in the organization. Then, it can enable the organization to generate value from its data and information assets though the application of all the program’s components, not just data governance.

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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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