Challenging EIM Issues Ahead

By Mark Mosley

Enterprise Information Management (EIM) is finally getting increased attention from CIO’s and other business leaders. Executives see the value of a shared partnership with the business and the importance of data stewardship and data governance. The field of information management is maturing, and many organizations are making remarkable progress towards managing information as an enterprise asset.

But now that we have their attention, EIM professionals face several critical challenges, requiring further maturity. Over the next few years, EIM leaders and practitioners need solutions to these challenges, or we risk losing management’s attention and our own credibility. Here are nine of the major issues I believe we must resolve to make EIM a permanent, respected and contributing function in any enterprise.

  1. EIM Leadership – EIM programs must integrate and coordinate all related data management efforts. This is of utmost importance to the success of today’s CIO, who needs a “right hand” responsible for this leadership. An EIM Executive might be a VP in some organizations, or a director in others, but this person should report directly to the CIO, as a peer to similar executives responsible for managing the technology infrastructure and the application portfolio. A direct reporting relationship to the CIO ensures continued attention and accountability. We need executives who are passionate about the importance of information management and savvy enough to be successful in the organization. An EIM Executive will get key messages to senior management.  Most data architects, team leaders and first line managers never get that opportunity.
  2. Sustained Data Governance – Most data governance programs are dependent on the leadership of one or two key individuals. These people inevitably move on to other opportunities, and the data governance initiatives they started inevitably die on the vine. Sustainable data governance programs depend on sustained executive sponsorship, collaborative leadership, clear data stewardship roles and responsibilities, dedicated facilitative support staff, productive and well planned meetings that optimize business data steward time commitments, effective communications, and repeatable governance processes (i.e., business data definition, quality requirements definition, policy and standards review, issue tracking and resolution).
  3. Information Valuation Techniques – Very few people have been able to quantify the business value of information. Current accounting practices consider information as intangible assets, estimating the business value of enterprise information assets within the general balance sheet category of “Goodwill.”  Project funding is dependent on estimating the business value of information access and information quality.  The more successful organizations have captured anecdotal “value stories” from respected business subject matter experts and extrapolated savings from a single circumstance based on its enterprise-wide prevalence. Until accounting practices mature, EIM professionals need to develop their ability to find, capture and extrapolate anecdotal business value. For many of us, this is a new way of thinking and a foreign language, but one in which we must become fluent. If any EIM topic requires creative thinking and intellectual thought leadership, it’s the need for practical techniques for data valuation.
  4. Management Metrics – There is little consensus today about what measures should be captured and what metrics should be tracked to manage data and the EIM function. As noted above, business value measures are the most difficult to determine (what increased business value can be attributed to the EIM program due to data standardization and improved data management discipline?).  Compliance and conformance measures evaluate the level of adoption, regulatory compliance and conformance to standards introduced by the EIM program. The earliest measures available are activity and participation measures, indicating the level and breadth of acceptance, organizational coverage and sustained commitment. For a while, these measures will have to suffice –but for how long? The EIM community would greatly benefit from a portfolio of practical measures and graphs from which to select in building an EIM scorecard for you and your senior management.
  5. Cost Effective Meta Data Management – Large organizations with significant resources have invested in building enterprise-wide Managed Meta Data Environments (MME), but the cost of the repository software tools, support staff, skills and process implementation have been far too restrictive for most organizations. We know that easy access to high quality, integrated meta data is essential to managing enterprise information assets, improving information quality, integrating data across applications, and supporting informed business intelligence. But we need creative cost-effective solutions that provide an immediate return on modest investments.
  6. Focus on Meta Data Delivery – Meta data repository implementations begin by focusing  on capturing and integrating meta data from multiple sources. Continuing this focus without paying attention to how to provide business value through meta data access and delivery has led many repositories to be characterized as “roach motels” – meta data goes in, but never comes out! Wouldn’t it be wiser to focus on leveraging an initial scope of meta data to the fullest extent possible, demonstrating business value? Frankly, most meta data repository software is not easy to use. Successful MMEs pursue a wide variety of access and delivery channels. Meta data administrators must worry about meeting the real needs of business and technical users, and focus attention on enabling ease of use and publishing.  “Meta data marts” fed from the repository need not be dimensional schemas – they may be web pages, reports and documents  that provide an easy-to-reference “retail” alternative to direct query and reporting against the technically intimidating meta data repository.
  7. Information Architecture Integration – While an enterprise data model is the heart and soul of any information architecture, there are other valuable components, including:
        • Information value chain analysis (CRUD matrices identifying the relationships between data and process, data and organizational roles, data and organizational units, and data and application systems)
        • Information supply chain analysis (data flow diagrams tracing information products as inputs and outputs of business processes)
        • Reference data sets – standard code values, their hierarchies (taxonomies) and associations (cross-references)
        • Semantic ontologies, integrating these closely related models with the enterprise data model, providing a consistent way of looking at both structured and unstructured data across the enterprise
        • Meta-models and the MME (meta data integration) architecture
        • Data integration architecture, including the master data management (MDM) architecture
        • Data warehousing and business intelligence architecture

    These related models should be integrated and internally consistent, linking application business objects and semantic web services to the enterprise data model (itself a semantic model), and linking data with business processes and other elements of enterprise architecture.  We must share perspectives across factions, reconcile our own semantics and speak with one clear voice.

  8. Information Architecture Usage – EIM professionals need to ensure the information architecture gets used in business planning, IT planning, application and project portfolio management, project scoping, requirements analysis and application design. Cynics in your organization already consider the information architecture to be “shelfware” – let’s prove them wrong. Business and IT planners are not likely to immediately see how architecture may help them. Data architects need to “sell” and demonstrate how to put information architecture to good use.
  9. Expanded Business Intelligence Support – If you’ve built it and they still haven’t come, you need to better support the various user communities within your organization.  Casual users need an easy to use portal accessing a library of standard periodic reports and parameter-driven real-time reports. Power users need training, access to business meta data, and guidance from BI support specialists. The true data-intensive “numerati” in your company will benefit from advanced analytics, including statistical analysis, data mining and predictive analysis.  All these technologies require user training and continuing support from helpful BI specialists with “people skills.”

I’m sure you can think of countless other critical issues to resolve in your enterprise, but these are enough to keep me up at night.  I look forward to working together to resolve these key issues.

About the Author

Mark Mosley, Principal Consultant with EWSolutions (, is a leading expert in enterprise information management (EIM). Mark has over 25 years experience in data modeling, data warehousing, data architecture and organizational change management. As a consultant, enterprise data architect and director of data resource management for multiple corporations, Mark has coordinated several successful data governance programs, developed enterprise data models and implemented enterprise data warehouses and master data management solutions. During his 13 years with IBM, Mark led the development of IBM’s AD Effectiveness Consulting Methodology and trained consultants worldwide in its techniques. Mark has B.S and M.S. degrees from the University of Illinois at Urbana-Champaign. Mark is a guest lecturer at DePaul University and a Certified Data Management Professional (CDMP). Mark serves as chief editor for The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK Guide) and the editor of the DAMA Dictionary of Data Management. He is the author of the DAMA-DMBOK Framework white paper, available for free download at You can email Mark at

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