In Is Your Data Any Good? Six Questions to Help Score Your Data Resources, Simon Oliver suggests that some data sources simply aren’t worth integrating into your marketing analytics environment.
Simon’s article is pitched at big data but his points are sound and should be applied to any customer data centric project – such as a new CRM implementation.
Simon proposes six questions that can be used to assess value (or risk of harm) of any data source you are planning to add to your big data environment in order to provide accurate insights. In his experience the lack of skills available to manage the technical complexity of integrating diverse data sets, of varying levels of quality, is the most common reason that data driven marketing programmes struggle to reach their undoubted potential.
Self service data profiling and discovery tools can help companies struggling with these technical complexities to focus on the business ROI, rather than on the technical integration problems that hamstring many projects.
However, as Simon points out: Not all data adds value. Many sources are not worth integrating.[Tweet this]
He proposes six questions to measure the value of each potential data source. Accurate data quality metrics could enhance the accuracy of Simon’s suggested scores – making the results more meaningful, as Simon’s questions are largely focussed on the quality of the source data.
Simon’s Data Quality Questions:
1. Is there a unique identifier that can be used to match the new data source with the main marketing database?
2. Can a unique ID be created using available information?
3. Can a unique ID be created using information not currently available?
4. How many of the customers and prospects in the CMD (central marketing database) are likely to be found within this feed?
Simon’s Operational Question
5. Do you have the ability to communicate back to the individuals within the new feed?
Simon’s Value Question
6. Assess internally the financial benefit likely to result from integrating the data source.
Simon’s approach shows the strong relationship between high quality customer data and marketing analytics but can be equally applied to other sales and marketing initiatives such as lead management, customer segmentation and channel management.
Marketing can no longer afford to waste budget on poor quality or valueless data.[Tweet this]
A formal data quality assessment of each new, or proposed source, allows Simon’s first four questions to be answered objectively. Data cleansing may increase the value of an unusable source, or it may be discarded without massive investment if the value simply cannot be found.
Emerging marketing methods require a significant investment in data. This investment can be great and the benefits may be hard to predict. [Tweet This]
An adaption of Simon’s suggested approach, incorporating formal data quality metrics, can save significant time and money
Contact us to learn how to accurately assess the quality of your lists
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