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G. Philip Rogers, PMP Senior Business Analyst, School of Public Health, Instructional and Information Systems, UNC Chape
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G. Philip Rogers, PMP Senior Business Analyst, School of Public Health, Instructional and Information Systems, UNC Chape

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  1. Building the Business Case for Metadata in the Enterprise: Looking At Models, Architectures, and Business Processes As Building Blocks for Cost Benefit Analysis and ROI G. Philip Rogers, PMP Senior Business Analyst, School of Public Health, Instructional and Information Systems, UNC Chapel Hill g.philip.rogers@unc.edu Doctoral Student, Information Science, UNC http://www.ils.unc.edu/~gerogers/

  2. About Me • Professional experience. Over the past 20 years, worked in Business Analyst, Project/Program Management, Technical Communications, and Management roles (before joining UNC, worked for Cisco Systems, Web startup, Intel Corporation, USAF). • Academic interests. Doctoral student in UNC’s School of Information and Library Science – academic interests include metadata interoperability, Semantic Web, Business Intelligence, and the role of IT as an enabler for addressing research challenges.

  3. Corporate Circle Goals • The goals of the Global Corporate Circle are to: • Promote the use of the Dublin Core standard by enterprise organizations/corporations for both internal and external information. • Coordinate with developers and information providers to ensure interoperability with enterprise-wide applications. • Develop a body of work which provides best practices, case studies and examples of how Dublin Core is implemented and its' value to the organization. Examples can include what elements are used, how they are interpreted for the organization, values/controlled vocabularies developed and the return on investment (ROI) of metadata, specifically Dublin Core, for a company.

  4. DCAM and Its Applicability to Business Process and EA Modeling • Pulis & Neville propose a UML-compliant model of the DCAM as a means of moving toward the development of a UML meta-model so that UML can be used to develop DC-conformant Application Profiles. • Perhaps additional modeling languages could be considered as a basis for additional DC-conformant Application Profiles, as a means of enhancing interoperability.

  5. Do You Know Where Your Data Is? … And what would you do with it if and when you find it? If someone were to ask you, in a work context, “How’s it going?” – what would your answer be? • The key to answering this question is of course defining “it.” • Financial results (profitability, market share) • Customer-related metrics (satisfaction, loyalty) • Quality measures (defects, discrepancies) • Performance measures (employee productivity)

  6. Working Definitions of Metadata • Semantic layer between IT systems and business users (McComb) • “Structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource” (NISO) • “Metadata is the information and documentation which makes data understandable and shareable for users over time. Data remain useable, shareable, and understandable as long as the metadata remain accessible.” (ISO/IEC 11179-1)

  7. Enterprise Metadata Management • Enterprise metadata management should provide insight into: • What data exists • Where data is being used • How data is labeled and referenced • How data is related to other information assets • Who uses the data • Why the data is needed • When the data was last accessed or updated

  8. The Changing Role of Metadata • The role of metadata has been transformed – it has gone from being an afterthought to being an architectural principle (McComb) • Metadata plays a critical role in investments in data warehousing, data mining, business intelligence, customer relationship management, enterprise application integration, and knowledge management (to name some of the big-ticket items in which enterprises have invested over the past 5 to 10 years).

  9. Framing the Question • Because the term metadata is abstract and not widely understood in a corporate environment, asking someone “how they use metadata” in their job is a question many people will struggle to answer. • Asking someone to describe their job, such as the systems and tools that they use, and to what extent and how they might use the data entered in those systems and tools, should make it possible to make deductions about the role metadata plays in their job and in their organization.

  10. Solutions Intended to Address Gaps In Systems and Processes • ETL. Extract, Transform, Load (reading data from one database, performing transformations on the data so that that it can be read in a different database, and writing the transformed data to the target database) • Data warehouses, data marts. Provide a central repository and enable data mining. • Middleware. Hides inconsistencies in underlying architectures (recent examples include web services integration, enterprise service buses) .

  11. Problems with Scoping and Justifying Metadata Projects • Projects where metadata is the central component often are not successful, because they are not: • Driven by a distinct and evident business need • Clearly defined • Based on achievable or measurable goals • Properly resourced, both during and after the project is completeMetadata repository projects are prone to failure because their contents are not sufficiently integrated across the enterprise – for example, they are not fully compatible with ETL or data integration applications. Focusing too much attention on the metadata itself, as opposed to accomplishing clear goals with metadata, can be a costly mistake.

  12. Challenges Associated with Metadata Repositories “The vast majority of metadata repositories are unidirectional. Modeling tools that extract, transform, and load information into the repositories are responsible for capturing both business (business metadata) and IT (technical metadata) meta information flow in only a single direction… Many enterprise tool vendors are trying to solve this particular problem, but …an organization would need to fully embrace the metadata repository approach for it to work and could not adopt it in bits and pieces. The repository will be required to store the latest version of the metadata source in which it will propagate changes. Concurrency issues will arise in this situation… [and] integration interfaces will have to be constructed to map and move metadata repository information back and forth to the metadata source.” - McGovern

  13. Has Something Like This Ever Happened Where You Work? • A large amount of money is allocated to building a data warehouse. Despite management support and ample funding, the initiative fails mainly due to inflexible business processes and lack of access to or understanding of critical business data.

  14. The Need For Metadata Management Tools AND Frameworks “ A metadata-driven framework is MANDATORY to enable companies to understand the different forms, types, and definitions that common data elements share with each other. It is important to maintain the distinction between managing metadata through a generalized metadata tool versus having a metadata-driven framework designed for a specific purpose, such as supporting customer data integration and master/reference data management. In my experience, the most successful companies combine 'best practices' from both approaches.” - Anurag Wadehra, VP of Marketing, Siperian

  15. Metadata Management Maturity Curve

  16. Observations About Enterprise Behavior • Profit-driven enterprises are often heavily influenced by short-term, one-quarter-at-a-time, “tactical” thinking. • Business justification for individual projects tends to be driven by short-term needs. • Developing business cases and calculating ROI for long-term investments is very difficult in such an environment.

  17. The Executive Sponsor’s Dilemma • A certain project is expected to produce benefits, but will require a capital investment • That same capital could be invested elsewhere, potentially producing a different set of benefits • How does the sponsor decide which project or projects to invest in?

  18. Building the Business Case • Building a business case typically includes steps such as the following: • Estimate future expected costs • Estimate future expected benefits • Determine implied return • Compare implied return to alternatives

  19. The Cruel World of Estimation • Projections of expected costs and benefits are often educated guesses, at best, particularly for software projects. • The greater the degree of uncertainty (risk) about a potential project, the higher the expected rate of return to justify the project. • A preliminary business case may be only a first step preceding additional analysis, understanding of requirements, and preparation of a more formal business case. The assumption is that investing additional time should reduce uncertainty (risk).

  20. Estimating Costs and Benefits • When estimating costs: • Level of cost is directly proportional to complexity of requirements • Uncertainty about costs reduces as requirements are refined • When estimating benefits: • An existing problem is solved or at least mitigated in order for the expected benefit to materialize • The problem and the cost of living with the problem are well understood

  21. Standard ROI Calculation ROI is often calculated as the average benefit over a specified time period divided by the cost. That is, • Given the sum of the costs • Given the sum of the benefits • Given other parameters Then the ROI can be computed in a number of ways. However, the calculation of costs and benefits is not always based on realistic data, and under what is often tight schedule pressure, insufficient time is typically allocated for the preparation of business cases and similar deliverables.

  22. Strategic Perspectives on Enterprise Data Management • Understanding enterprise business processes is an essential part of strategic thinking. • In order to help the enterprise attain its goals, enterprise architecture must be aligned with enterprise business processes.

  23. Business Process Definition • Set of business events that enable the delivery of an organization’s products or services to its customers. Categories for business processes: • Information – processing of data within and movement of data among systems • Operations – individual contributors, equipment, operational policies and procedures • Management – managers, authority, organizational dynamics, management policies and procedures

  24. Business Process Modeling A business process: 1. Has a Goal 2. Has specific inputs 3. Has specific outputs 4. Uses resources 5. Has a number of activities that are performed in some order 6. May affect more than one organizational unit. 7. Creates value of some kind for the customer (internal or external).

  25. Potential Business Process Focus Areas • Generalized (broadly applicable) • Business Intelligence/Knowledge Management • Content Management • Enterprise Resource Planning • Portfolio Management • Customer Relationship Management • Specialized (industry-specific) • Academia/government (grant-funded research) • Financial services (investment banking) • Health care (patient health records) • Libraries/archives (digitization) • Pharmaceuticals (clinical drug trials) • Semiconductors (microprocessor design)

  26. Enterprise Architecture (EA) Definition • Principles, methods, and models that shape the organizational structure, business processes, information systems, and infrastructure of an enterprise. “Enterprise architecture captures the essentials of the business, IT and its evolution. The idea is that the essentials are much more stable than the specific solutions that are found for the problems currently at hand. Architecture is therefore helpful in guarding the essentials of the business, while still allowing for maximal flexibility and adaptability. Without good architecture, it is difficult to achieve business success.” - Lankhorst

  27. EA Views • Business architecture. Shows how business is done -- models the enterprise using business processes and the events that trigger them. • Information (data) architecture. Enables the enterprise to develop a shared, distributed, consistent data resource -- consists of data models and databases that serve all participants in the enterprise business environment and the strategies, standards, policies required to develop and implement them. • Application architecture. Supports business processes, provides automated solutions, manages information storage and retrieval, links the Data and Business architecture. • Technology (infrastructure) architecture. Meets the infrastructure needs of business clients -- interoperates with and supports the Application, Business, and Data Architectures to provide interoperable technology platforms.

  28. EA View Components

  29. Business Processes Applications Service Framework Application Foundation Access Mechanisms Infrastructure Services Operating Software Physical Infrastructure Architecture Stack

  30. Sample Business Architecture User interaction Order Pay Quote Track Support Process integration B2B Business policies/rules Pricing ERP SC HRM CRM Transaction processing Business Intelligence CustData Data Data Data Data management

  31. Metadata Requirements Stack

  32. EA Governance Categories and Instruments

  33. EA Governance Instruments • Strategic Management (BSC). Emphasizes a balanced approach (traditional management focus is on finances) based on customer, financial, business process, and learning/growth perspectives. • Strategy Execution (EFQM). Inspired by Malcolm Baldridge (USA) and Deming (Japan), provides management framework for performance excellence. • Quality Management (ISO 9001). Focuses on integrated design, management, and documentation of business processes and supporting IT systems. • IT Governance (COBIT). Provides control objectives and management guidelines for 34 IT processes. Also provides IT governance maturity model. • IT Service Delivery and Support (ITIL). Provides set of best practices and training materials for IT service delivery. • IT Implementation (CMM/CMMI). Model for evaluating maturity of software development processes.

  34. Finding Data Where It Lives:Semantic Elicitation from Processes • Long duration business transactions (LDBTs) are a valuable source for uncovering semantics in business processes (workflows). • A good place to start for any enterprise is the “predominant flow” – typically a flow that occurs frequently, has significant cost implications, and is central to the core mission of the enterprise. • Looking at variations in the primary flow, whether the variations make business sense, and whether they merit a time investment can yield valuable information about how to manage critical business data (McComb).

  35. On Data Governance • Due in part to relatively recent business drivers related to compliance such as Basel II and Sarbanes-Oxley, data governance is an area that is seeing substantial enterprise investment. • Data governance seeks to ensure that there is a management framework that can deliver availability, usability, integrity, and security of enterprise data. Such a framework should include a governing body, a defined set of procedures, and a plan to execute those procedures.

  36. Why Metadata for Governance?

  37. Business Imperatives Driving Data Governance • Agility (ability to respond more quickly) • Simplification (reduce unnecessary complexity, and ideally, costs) • Rapid increase in the volume of information • Rapid business growth • Geographic dispersion (due to outsourcing and other factors) • Compliance

  38. Metadata Uses in Data Governance • Strategic (data stewardship; information reuse; information management; data integration strategy) • Tactical (project flexibility and adaptability; portfolio management)

  39. Architecture (BPM, Metadata Repository, and EA) Tools Tools that at least partially address the central EA challenge of representing enterprise information and technology portfolios: • BPM (top down) tools: • ARIS (IDS Scheer); Corporate Modeler (Casewise); MEGA International Software Suite; ProVision (Proforma). • Metadata repository tools: • Architecture Manager (Adaptive Enterprise). MOF-compliant repository that integrates with many modeling tools. http://www.adaptive.com/products/eamanager.html • Rochade (Allen Systems Group). Provides publication, visualization of models; CWM support. http://www.rochade.com/index_flash.html • EA Tools: • Architect (BiZZdesign); Enterprise Framework (Ptech); Metis (Computatis); System Architect (Popkin); Troux 4 (Troux Technologies).

  40. Metadata Extraction Tool Example: Saphir • Saphir (Silwood Technology) is a tool that reads the data structures of Peoplesoft,, SAP (BW & mySAP ), Siebel, and JD Edwards databases and extracts the definitions and relationships of the tables and columns, which can then be exported into tools such as ERwin, PowerDesigner, Popkin System Architect, or Visio. • “Data warehouse designers, reporting teams and data architects use this powerful application to analyse their data requirements from the key enterprise applications. Saphir helps you take control of your data management projects as you strive to understand exactly where vital business information is stored.” http://www.silwoodtechnology.com/saphir.htm

  41. Saphir (continued)

  42. Facilitating Interoperability: An EA/BPM Perspective • Metadata interoperability projects have generally been based on one of the following approaches: • Application profiling/schema customization • Derivation (e.g., MODS and MARC Lite are derived from MARC21) • Crosswalking/mapping • Switching schema (e.g., OAI) • Lingua franca (set of core attributes derived from multiple schemas) • Metadata framework/container (e.g., RDF, METS) • For EA/BPM, possible areas for further research: • Survey individuals working in areas such as EA, business process modeling (Architects, Business Analysts) • “Crosswalk of frameworks/models,” leveraging GRAAL framework for conceptualizing and comparing IT architectures and Value-Based IT Alignment (VITAL) approaches • Business process model (flow) registry • Further application profiling (e.g, BPML-compliant model of DCAM)

  43. Formulating a Business Case for Enterprise Metadata Management • What is driving investment in projects and initiatives -- Organizational needs? Business requirements? Technology demands? • What are the main limitations in the use of information? Inappropriate organizational structures? Cumbersome business processes? Outdated technologies?

  44. Simple Information Assessment Evernden & Evernden information diagnostic: • There is a clear and distinct vision of information as a corporate resource • There is an organization unit responsible for information and knowledge that is distinct from the information technology function • There is a well-defined strategy and action plan for improving the effectiveness of information use across the organization • Information that is vital and necessary to make key decisions is always readily and easily available • All information is available in a consistent and integrated format • Management believes that there is considerable value to be gained from the organization’s use of information • Information management is seen as the responsibility of business people as well as the information technology functions • Information has a key role in all business processes • Financial approval is readily available for investment in the information infrastructure of the organization (as opposed to technology investments) • Information is used to support innovation and creativity in product and service development, business processes, and customer support Total Score:

  45. Surveying EA/BPM Practitioners • Questions that might yield insight via a survey (or similar instrument), possibly using an approach such as the COBIT IT Maturity Model, could focus on areas such as: • Enterprise data warehouse or metadata repository initiatives attempted or planned • Modeling frameworks or tools being used • Extent to which business processes are understood and documented • Extent to which EA aligns with business processes

  46. EA Frameworks and Business Process Models: Is There a Role for Dublin Core? • Pulis & Neville have already proposed a UML-compliant model of the DCAM – how might this model be leveraged as part of the larger OMG Model-Driven Architecture (MDA), which includes the Meta Object Facility (MOF) and the Common Warehouse Meta-model (CWM)? • Other frameworks and models that appear to have traction in the U.S. are The Open Group Architecture Framework (TOGAF), the Business Process Modeling Initiative (BPMI), and the Federal Enterprise Architecture Data Reference Model (FEA DRM).

  47. Selected References • ANSI X3.285, Metamodel for Management of Shareable Data metadata-stds.org/Document-library/Draft-standards/X3-285-Mgmt-of-Sharable-Data/X3-285.PDF • Cook, M. (1996). Building Enterprise Information Architectures: Reengineering Information Systems. Prentice Hall. • Evernden & Evernden (2003). Information First: Integrating Knowledge and Information Architecture for Business Advantage. Elsevier. • Finneran, T. (2003). Enterprise Architecture: What and Why. http://www.tdan.com/i007ht03.htm • ISO/IEC 11179-1. Specification and standardization of data elements - Part 1: Framework. metadata-stds.org/metadata-stds/11179/ • IT Governance Institute (2006). COBIT 4.0. • Lankhorst, M., et al. (2006). Enterprise Architecture at Work: Modelling, Communication, and Analysis. Springer. • McComb, D. (2004). Semantics in Business Systems: The Savvy Manager’s Guide. Morgan Kaufmann. • McGovern, J., et al. (2004). A Practical Guide to Enterprise Architecture. Prentice Hall. • NISO. Understanding Metadata. http://www.niso.org/standards/resources/UnderstandingMetadata.pdf • Silverston, L. (2001). The Data Model Resource Book, Revised Edition, Volume 2: A Library of Universal Data Models by Industry Types. Wiley.

  48. COBIT Maturity Model for IT Governance • The Control Objectives for Information and related Technology (COBIT) for IT governance, first published in 1996 by ISACA, along with control objectives and management guidelines for 34 IT processes, also includes an IT governance maturity model. • The maturity model has five levels, from the lowest (“Ad Hoc”) level where there are no standardized processes, to the highest (“Optimized”) level, where processes have been refined to the level of external best practices.