How To Do a Data Warehouse Assessment (And Why)

　　The challenge of data warehouse assessment, then, is that there is a lot of complexity to look at in a short period of time. A successful data warehouse assessment approach must provide a roadmap and sufficient structure to accomplish a breadth of analysis, at the right level of detail, in a limited time period. It should also provide a set of key artifacts and best practices to look for.

　　Complexity, itself, can be a barrier to success of data warehousing efforts. In troubled warehouse initiatives, it may be the case that many bright and capable people have simply been caught up in and overwhelmed by the complexity. Assessment represents an opportunity to step back, evaluate key gaps and dependencies, and restructure the direction of the warehousing effort. Assessment offers opportunity to re-establish a well-balanced and coordinated warehousing strategy that will leverage strengths, mitigate risks, and address weaknesses as the warehouse moves ahead. It also represents an opportunity to review initial design and technology decisions in light of current realities.

　　There are often significant organizational and methodological issues to be evaluated. The data warehouse and its evolution cross organizations and functions. Warehousing is tightly bound with business strategy and data stewardship on the one hand, and closely coupled with technology on the other. As a result, responsibility for definition, development and operation of the data warehouse often doesn't readily integrate into either the current business or the current IT organizations, structures, methods, and processes. Additionally, the information management infrastructure required for ongoing success of the data warehouse of the data warehouse may be lacking and not well understood.

　　Warehousing assessment is further challenged by the need to maintain neutrality and objectivity. Regardless of who initiated the assessment, it must be performed from the perspective of information as an asset to the enterprise. In the ideal scenario, assessment is a joint effort undertaken in partnership by business and IT sponsors. In this scenario, the assessment provides an opportunity to objectively identify the roles, responsibilities, and quality metrics needed to successfully manage the delivery and analysis of business critical information. A comprehensive data warehouse assessment approach provides a framework of roles and responsibilities that may be quickly applied to the current environment. The framework serves to identify organizational and methodological gaps, and tailor a best organizational solution for the warehousing initiative under review. Organizational positioning of the warehouse is also important. Understanding of, and agreement upon the appropriate roles of the data warehouse within the broader context of enterprise knowledge management are crucial to managing and meeting expectations. Resolving the organizational issues may be among the most significant of an assessment's contributions to progress with and long term viability of the data warehouse.

　　When a data warehouse assessment is initiated, it is frequently expected to produce much more than an identification of current weaknesses and recommendations of how address them. It is particularly common that the assessment is expected to produce a complete statement of business requirements – to provide a business context that was missing or incomplete when the warehousing initiative was started. While comprehensive requirements analysis is possible, it may be impractical within the scope and time constraints of an assessment. With the actual scope and overall state of affairs generally in question going into the assessment, it is difficult to estimate the time and effort of additional business requirements analysis. Business alignment analysis may be more appropriate to the scope of assessment.

　　A final challenge of data warehouse assessment is the need to establish clarity and consensus on the scope, exact deliverables, and expected outcome of the assessment. The assessment approach needs to include techniques for rapid assessment of warehouse business alignment. The framework should be designed to ensure stakeholder involvement and feedback, and to support rapid evaluation of overall business expectations. In many assessments this also represents one of the largest opportunities for improvement – by rapidly re-establishing the baseline, prioritizing business needs, and mapping needs to current data warehouse capabilities and directions. This realignment offers opportunity for tremendous improvement in the clarity of direction and the focus of implementation strategy.

　　The opportunities of data warehouse assessment are many and varied. The complexity and inherent challenges of data warehousing create a climate rich with opportunity. An assessment approach that supports rapid review and evaluation of a data warehouse, with attention to the challenges described above, provides a framework through which these opportunities may be realized. This framework, and the results of its application, help bring order to the detail and complexity inherent in the data warehouse, and assist the data warehousing team to make informed choices and move the warehouse into the future.

　　Decomposing the Assessment Problem

　　As already stated, data warehousing represents a large, complex undertaking with many, interdependent parts. The first step of a data warehouse assessment (as with a data warehouse itself) is determining where to begin, what to produce, and how to produce it. Complexity of the assessment is compounded by partial artifacts of previous projects, missing history, and multiple agendas. As with any complex undertaking, assessment is most successful when the large, complex problem is divided into smaller, more manageable pieces. Our experience has shown the following decomposition, depicted graphically in Figure 1, below of data warehouse investigation and to be most effective:

　　Business Needs

　　Information Architecture

　　Technical Architecture

　　Methodology and Project Management

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