Developing a Roadmap for an Enterprise Information Management Program

By Mike Jennings

How does your organization manage its most valuable resource, information?

Many organizations have begun to realize that the data and information they create every day is a valuable enterprise asset. It is difficult to imagine any enterprise achieving its goals without pertinent information. Those with accessible and higher quality information will make more informed and more effective business decisions. This is as true for all organizations since all enterprises need quality information to guide their decision-making and to provide optimum service to their customers. For organizations whose primary asset is information, this need is even more critical.

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.

Full organizational commitment to managing information as an enterprise asset requires the establishment of enterprise policies and the development of a support environment shared by the organization. That is what an EIM program provides.

Even for organizations that have identified the need to manage information as an enterprise asset, determining what that really means and how to get started can be very difficult. The discipline of EIM is not as mature as some other disciplines that have evolved over time to manage the enterprise assets of money and people. The management of information is an enterprise effort, and cannot be left to each department or business unit without impacting the overall effectiveness and business value EIM can provide.

It is critical to note that EIM is not a single technology or component but a framework of disciplines for information management across the enterprise (see figure 1). It is important to have this framework to guide the development and implementation of an EIM program. The scope, extent and scale of the EIM components vary across organizations due to the varying requirements, size, means and experience of the organizations. While all these components must be considered when developing an EIM program, not all will require the same degree of emphasis for every organization. The following EIM framework represents a very effective way to structure and manage an EIM program.

Figure1 – Framework for Enterprise Information Management

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.
  • InformationArchitecture– 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 of 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 & 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, 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:
    • 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
    • Data Lifecycle Services – external data acquisition, backup and recovery, performance monitoring and tuning, storage management, archive management
    • 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.

Once these framework components are in place, the keys to an effective EIM program include business participation, business impact, technologies and education. Real and effective business participation means executives and key managers must not only be empowered with ownership, but with measurable accountability. Those charged with participating, due to their strong knowledge and expertise of the business must also define the procedures, polices, data concepts and requirements for the EIM program. The amount of business impact the EIM program can have on the enterprise varies from one organization to the next depending on their level of maturity. It is imperative that organizations define the business impact and measurement criteria from the beginning of the EIM program in order to quantify the initiative value to the organization.

Organizations need to take an active and planned approach to managing their information to obtain the maximum business value and impact to succeed in today’s business environment. An Enterprise Information Management initiative provides the framework and roadmap for an organization to achieve real information knowledge and true business impact.

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

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