EIM Component Framework Dependencies – Part 2
Is your organization realizing its full potential from its EIM initiatives?
In the previous installment of this series, we saw some examples of the guiding principles organizations can use to realize the many advantages from embracing an Enterprise Information Management initiative. Those who embraced these key principles into their EIM initiative and understood the interdependency the framework components share were more likely to realize the immense business benefits. Some corporations still 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 engraining itself as an accepted method of doing business.
EIM Framework Component Review
Before we look at some of the inter dependencies of the EIM framework components, lets review the nine components.
- Data Governance & 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 the master blueprint for semantic and physical integration of data assets across the enterprise, defining information products and the information supply chain.
- Meta Data Management – Meta Data 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 – protecting the privacy, confidentiality and competitive advantage of information assets.
- Information Quality Management - Information quality is 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 – 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 – management of physical database assets throughout the data lifecycle.
- Unstructured Data Management – management of information, content management, found in documents, images and web pages.
Many EIM components exist, at some level of maturity, in every organization. Structured Data Management (development, production, technology services) has existed in information technology departments for decades. Meta Data Management also exists in every organization whether formalized or not. Even at the lowest maturity level, meta data exists as a subject matter expert’s knowledge, in application logic, in documents, the all too common spreadsheet, and many others 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).
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 interdependency of 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 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 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