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Developing a Meta Data Integration Architecture 70859_Poole_CH06I 10/29/2001 2:24 PM Page 169 CHAPTER 6 Developing Meta Data Solutions Using CWM The preceding chapters have described CWM in considerable detail. In partic- ular, we have seen that CWM is a standard specification for defining data warehousing and business analysis meta data in terms of descriptive models. These models are independent of any particular technology and live outside of the various software products that use them as the basis for integration. We also have seen how one goes about constructing CWM models to represent common problems in data warehousing and business analysis. This chapter provides a much broader description of how CWM relates to the overall problem of building a comprehensive meta data integration solu- tion. In particular, this chapter describes the implications that CWM has for both meta data management strategies and meta data integration architec- tures. We also describe the necessary architectural components required to deploy CWM integration solutions in the data warehousing and business analysis environments. 169 70859_Poole_CH06I 10/29/2001 2:24 PM Page 170 170 Common Warehouse Metamodel The Need for a Meta Data Management Strategy In Chapter 2, “The Value Proposition for CWM,” we learned that two of the major components necessary for a successful, model-based meta data integra- tion solution, which were not specifically defined by CWM, are the following: II A meta data management strategy II A corresponding meta data integration architecture In fact, both are required for any successful meta data integration effort, not just the model-based approach taken by CWM. However, the use of CWM for a model-based, meta data integration solution has certain implications for both strategy and architecture (perhaps more so for the architecture than the strategy, which is more high-level). A meta data management strategy is the overall definition of what is to be accomplished by any meta data integration effort and the management poli- cies, requirements, and constraints necessary to ensure the successful integra- tion of the environment at the meta data level. Meta data integration architecture is the technical, system architecture that largely realizes, or imple- ments, the meta data management strategy. This subsection discusses meta data management strategies in general, and the following subsections address their architectural realizations and the implications of using CWM as the basis for such architectures. Note that this chapter does not define, nor recommend, any particular strategy or architecture, but rather highlights the salient fea- tures and describes the CWM touch points that are relevant to these features. For a more thorough treatment of meta data management and meta data inte- gration architectural techniques, see David Marco’s Building and Managing the Meta Data Repository (Marco, 2000). The Importance of a Meta Data Management Strategy For any meta data integration effort to be successful, it must be grounded in a coherent and sound management strategy. This management strategy defines the objectives and requirements for meta data integration, sharing, and reuse in the target environment. Any given meta data management strategy is specifically tailored toward a particular data warehousing or business analy- sis or ISC deployment. However, any such meta data management strategy must adhere to general principles to be successful. Often, the need for a meta data management strategy either goes unac- knowledged or is naively assumed to be a problem largely addressed by soft- 70859_Poole_CH06I 10/29/2001 2:24 PM Page 171 Developing Meta Data Solutions Using CWM 171 ware tools or technology standards. The need for sound meta data manage- ment, however, is not solved by technological capabilities. Meta data integra- tion tools and enabled software products, no matter how powerful or robust, are no substitute for a sound and coherent meta data management strategy. In fact, strategy must precede the definition of meta data integration architecture and tool selection if the overall integration effort is to be successful. Regard- less of how meta data management is ultimately carried out in the target envi- ronment (whether meta data is managed by a centralized repository or a distributed or federated network), some central, global policy must define the overall requirements and behaviors for meta data management. The industry experts have been very clear on this matter for quite some time. For example, consider several reports on this issue by Gartner Group. In Meta Data Management Alternatives for DWs and Data Marts (Gartner, 1999), it is acknowledged that tool capabilities alone are no panacea for meta data man- agement problems and that any decisions on tool selection should be based on an overall meta data management plan and architecture. The report, Data Warehousing Business Meta Data Management Issues (Gartner, 2000a), elaborates further, concluding that a sound meta data management strategy will maxi- mize meta data reuse and enhance ROI, in part by helping to overcome the cultural and political issues impeding meta data integration. The companion report, Data Warehousing Technical Meta Data Management Issues (Gartner, 2000b), states that technology alone cannot solve the meta data integration problem if an organization is unable to secure agreement on fundamental meta data management issues. Another recent Gartner report on CWM, OMG’s Common Warehouse Meta- model Specification (Gartner, July 2000), acknowledges CWM as being a posi- tive step toward ensuring meta data integration and reuse but also recognizes that CWM by itself will not be effective when an organization has no agreed- upon meta data management policy. Where such a policy does exist, however, the report states that CWM will indeed allow for a much greater semantic understanding between CWM-enabled technologies. The report goes on to state the importance of encouraging vendors to adhere to the CWM and XMI standards to maximize meta data reuse. Finally, David Marco, in Building and Managing the Meta Data Repository, clearly delineates the need of ownership of meta data (the concept of data stew- ardship, pp. 61—62). He discusses prerequisites to the success of any meta data repository or integration effort as including clear management direction, backing, and policy (see p. 185 and pp. 115—121). Also provided throughout the book are numerous recommendations and checklists for verifying the effi- cacy of any meta data integration or repository effort. Issues of organizational culture, politics, and budget rarely have technolog- ical solutions. Neither does the overall absence of a meta data management strategy. However, when a clear, well-understood, and widely agreed-upon 70859_Poole_CH06I 10/29/2001 2:24 PM Page 172 172 Common Warehouse Metamodel management policy is in place, organizations can significantly benefit from the use of a model-based, meta data integration standard like CWM. The use of CWM will greatly enhance meta data integration, sharing, and reuse, and the presence of a sound and coherent meta data management strategy ensures that nontechnical issues (political, cultural, lack of ownership, agreements on meaning and semantics, and so on) will not subvert a meta data integration solution. Elements of a Meta Data Management Strategy What defines an effective meta data management strategy? The authors of this book do not attempt to define a meta data management strategy, because any particular strategy must be specific to a particular meta data integration prob- lem or environment. However, we will attempt to delineate some of the essen- tial elements of an effective meta data management strategy in the following list. This list could be used as the basis for evaluating any proposed meta data management strategy. Also, wherever CWM supports the realization of these policy elements is noted in the descriptions. (Recall that any meta data inte- gration architecture is an implementation of some meta data management strategy and that CWM is a key component of any model-based meta data integration architecture.) Any sound, coherent meta data management strategy will generally incor- porate most of the following essential characteristics: II An overall security policy for meta data II Identification of all meta data sources and targets II Identification of all meta data elements II Agreement on the semantics of each meta data element II Ownership of each meta data element II Rules for sharing and modifying meta data elements II Rules for republishing meta data elements II Versioning of meta data elements II Reuse targets for meta data elements II Elimination of manual processes II Elimination of meta data redundancy These essential characteristics are further elaborated on in the subsequent subsections. 70859_Poole_CH06I 10/29/2001 2:24 PM Page 173 Developing Meta Data Solutions Using CWM 173 An Overall Security Policy for Meta Data Security is a vitally important (and oft neglected) aspect of meta data manage- ment (Marco, 2000, pp. 62—63 and 196). Meta data is a highly sensitive and strategically valuable information asset because it describes almost all aspects of an information system or computing environment. Meta data is strategi- cally valuable because it inherently contains a wealth of information regard- ing the characteristics of business data and forms the basis for strategic, business knowledge. Meta data cannot be comprised. A sound meta data management strategy must include a
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