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An event based log about a service offering
Due to the fact that data is there before a data quality project, and it is there after a data quality project, data quality is not as clear an impact on the business as a traditional application development project. This is particularly true of customer data management oriented data quality projects where the primary objective is to “de-dup” or consolidate the data. Afterall, in the end there is just less data.
When this is looked upon purely from a software perspective there’s not much difference. Sure, there are cost savings associated with the reduction in the storage requirements. There might even be some increased performance in dependent applications due to the reduced volume. However this is hardly a justification for the investment that a typical data quality initiative requires. This is particularly inconvenient considering most of the investment is in software and other technology related resources.
However consider the impact of a data quality project which consolidates customer data from a business perspective and see a different side of things. Consider the benefits of less, unnecessary, possibly inaccurate customer data.
Now (re)consider the substantial impact that can be realized from a consolidation effort. Furthermore as long as data quality initiatives are implemented into ongoing operational data services, these cost reductions extend into the future producing benefits in the long term. This further justifies the cost of implementing data quality services into an organization as a long term solution.
This is why it is critical to the success of a data quality project to have clear goals that are aligned with a business initiative.
However this is not the end of the line when it comes to ensuring success. To do this you have to start with a goal like the ones listed above and define ways in which these types of goals can be measured.
For example the first bullet point is a data quality goal tied to the business initiative of reducing duplicate customer data. To support this a data quality matching process can be defined that uses criteria to identify redundant customer transactions and consolidate them into a survivor record. The affect the data quality initiative has on this business process can be measured in terms of the reduction in total mailings required to complete a marketing campaign. More importantly, it can be measured in terms of a reduction in total dollars required to fund the new and more concise direct mailing campaign. Now the data quality process and its results can be linked directly to a reduction in budget. Clearly metrics like these make it obvious that a data quality initiative that merely reduces data has a tremendous amount of value.
If you define a list like this with business stakeholders driving the process, before the data quality project is implemented, there will be a clear path to success as well as an easy way to quantify it once the solution is deployed!