The EIM Puzzle – October 2011

Many organizations realize that data and its management are important aspects of a successful enterprise. It is difficult to imagine that any organization will be successful without access to the right data, at the right time, for the right people, to address the right purpose. However, many organizations only manage one aspect of their data – the physical storage – and neglect or ignore the other components that are needed to truly use data to its fullest extent.

Enterprise Information Management (EIM) is the set of business processes, disciplines and practices used to manage the information created from an organization’s data as an enterprise asset. EIM functions ensure that high quality information is available, protected, controlled and effectively leveraged to meet the knowledge needs of all enterprise stakeholders in support of the enterprise mission.

EIM is not a technology or a single process; rather, it is a set of disciplines in which each component contributes to the management of data in one or more ways and the components support and complement each other. As such, EIM can be called a framework, and an EIM framework allows an organization to apply rigor and proven techniques to the management of the data assets.

As defined by the DAMA Data Management Body of Knowledge (DAMA-DMBOK), the functions or components of an EIM framework are:

  • Data Governance – Data Governance is the exercise and enforcement of authority over the management of data assets and the performance of information management functions. The data governance function controls how all other information management functions are performed. Data governance is at the heart of effectively managing the enterprise data resource.

  • Data Stewardship – Data Stewardship is the enterprise role that ensures organizational information and metadata meet high levels of quality, accuracy, format, and value criteria, ensuring that information is properly defined and understood (standardized) across the enterprise.

  • Information Architecture – Information Architecture is the function of defining and using master blueprints for semantic and physical integration of enterprise data assets (e.g., enterprise data model, enterprise data flows). 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.

  • Metadata Management – Metadata is the data context that explains the definition, control, usage and treatment of data content within a system, application or environment throughout the enterprise.  Metadata management enables other EIM components in the framework and provides the characteristics to measure information quality. Metadata is the fabric that interconnects all the other components of EIM and promotes the enterprise use of information management tools and techniques.

  • Information Quality Management – Information Quality Management is the function for defining data quality metrics, analyzing and profiling data quality, certifying and auditing data quality service levels, cleansing data proactively and reactively, identifying data quality requirements and ensuring source system requirements are met.

  • Reference and Master Data Management – This functional component controls the capture, storage, synchronization and usage of the core business entities of the enterprise. Master data (i.e., reference data) provides the context for transaction data and business intelligence. Master data consolidates disparate data between heterogeneous data sources. Master data management ensures the quality and use of controlled reference data values and their business meaning (i.e., metadata). 

  • Data Warehouse / Business Intelligence – A Data Warehouse is a system designed for archiving and analyzing an organization’s historical data such as sales, salaries or other information from day-to-day operations. This functional component is responsible for establishing, controlling and supporting the data needed for analysis, data integration and information delivery, as well as supporting the usage and tools.

  • Information Security Management – Information Security ensures privacy and control of data by establishing, implementing, administering and auditing policies, rules and procedures (e.g., role and row level security) within the enterprise.

  • Structured Data Management – This functional component is responsible for managing physical structured data resources, including:

    1. Development Lifecycle Services – designing physical databases; defining SOA data services; maintaining production, development and test environments; controlling configurations and change; creating test data; validating data requirements; migrating and converting data

    2. Data Lifecycle Services – external data acquisition, backup and recovery, performance monitoring and tuning, storage management, archive management

    3. Data Infrastructure Services – data technology installation, administration and support

  • Unstructured Data Management – This functional component covers a broad range of related disciplines (conceptual and physical), including management of documents, reports, images, forms, records, email, spreadsheets, web pages, XML documents, geospatial data and the collective knowledge of the enterprise.

In this column, we will examine the parts/components of the EIM puzzle, and try to arrange the pieces to form a comprehensive view for managing information across the enterprise. We will include the organizational dynamics and human aspects of EIM and address technologies where appropriate. We will include discussions and cases, and we actively solicit reader comments and suggestions for topics for articles and discussions.

EIM implementation is dependent on many things within each organization, but there are some foundational aspects that cannot be overlooked or discarded if the enterprise wants to manage information successfully. Additionally, some EIM components may not apply to some organizations, and others may be implemented differently within a successful program. This column will present these varieties and offer the opportunity for dialogue to expand the adoption of EIM as the optimal framework to achieve real information knowledge and true business impact.

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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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