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EIM Framework Components and Dependencies

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Every enterprise data / information management program contains aspects of several component areas, and each program should leverage the alignments and dependencies between these components for their maximum benefits and value to the organization.

Many organizations find that using an Enterprise Data / Information Management framework can give them support and allow them to realize many advantages.  Those who embrace the guiding principles of EIM are more likely to realize the immense business benefits.  Some corporations continue to struggle by approaching the framework components and the EIM initiative as projects, not as a cultural change.  For an EIM initiative to be successful and provide real business benefits, it must be integrated into the processes, procedures, and standards of the organization at all levels. An EIM initiative then becomes invaluable by positioning itself as an accepted method of doing business. Since every EIM program has several component disciples and all are related, using a framework is critical to success. 

EIM Framework Component Review

Following is a short overview of the Enterprise Data / Information Management Framework components.

  • Data Governance & Data Stewardship – Data Governance is the planning, implementing, guiding, monitoring and promotion of strategies, policies, standards and procedures for the effective control and use of an organization’s information assets.  Data Stewardship is the assigned accountability of individuals for responsibilities as trustees of enterprise data.
  • Information Architecture – Information Architecture is domain responsible for the master blueprints that control semantic and physical integration of data assets across the enterprise, defining information products and the information supply chain (e.g., data models, enterprise models, data integration models, etc.).
  • 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.  Managing metadata enables data governance in the organization and provides the characteristics to measure data quality in the enterprise.
  • Information Security Management – Information Security is responsible for protecting the privacy, confidentiality and competitive advantage of information assets.
  • Information Quality Management –Information quality defines and manages the health of information for an intended use, measured by key indicators such as accuracy, consistency, freshness and completeness for specific purposes.
  • Reference and Master Data Management – Reference and master data management consolidates the capture, storage, synchronization, and usage of core business information across the enterprise.
  • Data Warehousing and Business Intelligence – DW/BI/analytics are responsible for the management of the data, technologies and resources required to support business intelligence, providing the business with answers to “any question” “any time” across all data subject areas through a secure environment.
  • Structured Data Management – The oldest component of EIM, Structured Data Management is responsible for the management of physical database assets throughout the data lifecycle.
  • Unstructured Data Management – Content and Document Management is responsible for the identification and management of content found in documents, images, video, and web pages.

Component Dependencies

Many EIM components exist in every organization, at some level of maturity.  Structured Data Management (development, production, technology services) has existed in information technology departments for decades.  Metadata Management also exists in every organization whether formalized or not.  Even at the lowest maturity level, metadata exists as a subject matter expert’s knowledge, in application logic, in documents, the all too common spreadsheet, and many other areas throughout an organization.

The real business value and impact EIM can bring to an organization is by integrating the various framework components together to achieve. Most organizations should tactically focus on foundational EIM component areas (Data Governance, Structured Data Management, Meta Data Management, Information Architecture) to increase their EIM maturity level while keeping a strategic view on all the EIM framework components (see example in figure 1 below).

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Figure 1 – EIM Component Ranking Example

Some examples of where framework component dependencies are important include:

  • Data Governance and Data Stewardship bring responsibility, process, procedures, and standards to information management. Meta Data Management provides the technical backbone, enablement, to support Data Governance/Stewardship. Together, these components provide an enterprise wide meta data management environment that provides access to data semantics, format and source information, which ensures semantic uniformity, correlation of format from all sources
  • Data Governance and Data Stewardship processes together with a defined meta data attributes (accuracy, completeness, uniqueness, consistency, and currency) focused around information quality ensure Information Quality Management processes (metrics, service levels, business rules, validation, and auditing) are comprehensive and applied.
  • Information Security Management ensures privacy and control of data by establishing, implementing, administering and auditing policies, rules and procedures. Business rules around security policy and access are identified by data stewards and documented in the Meta Data Management component. Operational Meta Data (usage/access) from Structured Data Management and/or Data Warehousing/Business Intelligence components can also potentially be integrated and/or linked to the Meta Data Management component in accordance with Information Security Management.
  • Subject Area identification from an Enterprise Data Model (Information Architecture component) influences data steward assignment and organization (Data Governance/Data Stewardship).
  • Data integration (Information Architecture) between operational source systems to the data warehouse (transaction -> BI data) identifies data quality issues (Information ,Quality Management)
  • Taxonomies (Information Architecture) developed with data steward guidance (Data Governance/Data Stewardship), help information consumers find information residing in structured and unstructured data sources in the enterprise (Structured/Unstructured Data Management)

In order for the business benefits of an EIM initiative to be realized, organizations must understand the dependencies within the framework components. The components will typically fail if treated as independent (siloed) projects as opposed to a larger enterprise program initiative. Foundational framework components should typically be focused on first, depending on a corporation’s EIM maturity level, while still keeping a planned EIM perspective.


Michael F. Jennings

Michael F. Jennings is a recognized industry expert in enterprise architecture and information management. He has more than twenty-five years of information management experience in the healthcare, retail, telecommunications, health insurance, and other industries.

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