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Businesses often delude themselves into thinking they can conform as they go. We can build a data mart for Sales, the theory goes, and then adjust it as needed when we get around to inventory. This approach underestimates the level of effort required to adjust
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5 Conformed Dimensions 107
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dimensions once they are in production. In addition to the redevelopment, retesting, and redeployment of the ETL routines that maintain the dimension, there are also existing queries, reports, dashboards, and other processes that will be impacted by changes to dimension design. Whether well informed or misguided, the risks inherent in the choice to push forward with a subject area solution can be mitigated, although not eliminated. While the organization may not tolerate the development of a dimensional model of enterprise scope, it may be possible to take some time to focus on key dimensions. This is accomplished by adjusting project scope to spend extra time looking at enterprise needs with respect to the chosen dimensions. This requires that developers have the insight to recognize what portions of the model may impact other subject areas. If the team can identify dimensions that may have enterprise implications, these dimensions can be fully developed with respect to the business requirements they satisfy and the operational sources that feed them. Rather than limit their design to what is required by the solution under development, they can be fleshed out in recognition of the needs of other areas. Rather than limit their sources to the operational systems that are a part of the subject area in question, all operational sources for the dimension are considered. This approach strikes a delicate balance between the development of a complete enterprise dimensional model with a conformance framework, and the rigid focus on a single subject area. Although it does not guarantee an easy ride when future subject areas are considered, it mitigates some of the risk that incompatibilities will be discovered. While it does not entail dedication of resources to an intensive strategy product, it does expand project scope to consider business needs and operational sources from other parts of the organization. As with the other techniques for coping with stand-alone data marts, the choice of this route should be an informed decision that is made jointly by key stakeholders from IT, business units, and executive management.
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Stand-alone data marts can be brought into conformance with the remaining data warehouse infrastructure by retrofitting dimensions. This option is appealing when a bulwark of conformed dimensions is in place, and one or more legacy applications do not conform. When it becomes necessary to enhance one of the legacy stand-alone data marts, its dimensions can be retrofitted for conformance. This process sounds easier than it turns out to be. Successful retrofitting of an existing data mart may require a complex system of views and other tricks, used to minimize the impact of changes on other parts of the system as the retrofitting takes place. The process is akin to reconstructing a bridge while traffic is driving over it; the challenges can be monumental. With respect to the schema design, the task may seem disarmingly simple: Map each row in the nonconforming dimension to one in the corresponding table from the enterprise dimensional model. Add any additional attributes that are missing from the enterprise dimensional model. In the stand-alone data mart, use the mappings to reassign fact table foreign keys so they refer to the conforming dimension.
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Though reasonable on the surface, this short list fails to take into account deep incompatibilities in the data, and completely ignores the impact on the front-end applications that query the stand-alone data mart. On the data side, it may not be possible to establish a one-to-one match between the rows of two nonconforming dimension tables. This may result from inconsistent definitions of what the dimension represents but can also be more insidious. If the two dimensions handle slow change processing differently, for example, it may not be possible to consolidate them without referring back to the original source data to mediate differences. Simple mappings are not sufficient, and fact table keys must be adjusted based on natural keys and dates associated with each row. Equally serious is the impact on the front-end reports and applications. While it may be possible to bring a dimension into conformance through a retrofitting process, each dependent front-end information product will be impacted queries, reports, dashboards, and so on. Each will require redevelopment based on the new information structure. Changes in content will also have an impact; filters or query predicates may need to be changed to be consistent with newer representations of the information.
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Conformed dimensions are a crucial component of the successful dimensional design. Conformed dimensions allow users to ask questions that cross process boundaries, within a single subject area or across the enterprise. Answers to these questions can be very powerful. The key performance indicators of many businesses include metrics that can only be evaluated in this way. This chapter has provided you with the knowledge necessary to develop conformed dimensions of many types. It explored the process of drilling across, providing you with insight into how dimension design can make or break this process. Drilling across fails when two or more fact tables do not share dimensions with the same structure and content. Having identical dimensions avoids this failure, but dimensions can also conform when one summarizes another. Using this insight, requirements for conformance were developed. Four kinds of conformance were developed. Tables that are identical in structure and content conform. Shortcomings in these areas can sometimes be worked around, but the cost is increased complexity in developing reports, poor performance, and a risk of inaccurate results. Tables can conform when the dimension attributes of one are a subset of another. The tables are known as a rollup dimension and a base dimension. They will not share a common surrogate key, but the common attributes must possess the same structure and content. Degenerate dimensions can serve as the basis for conformance. Again, the corresponding columns should be consistent in structure and content, but it is not required that every fact table share the same set of instance combinations, as to do so would force violations of sparsity.
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