Data Quality Scorecard: the smart way to predict your business success
Dr. Christiana Klingenberg Uniserv GmbH
Today’s business is driven by data and information. New big data platforms emerge, buzzwords like „digital transformation“ or „operational excellence“ are widespread. When asking about the meaning for the operational business or for practical hints and tricks on how to improve or predict business success, good answers are rare.
One of the most interesting points in discussions about digital transformation is the quality of the data and therefore the quality of the information. How data quality KPIs can be generated and how to deal with them? And how this information on data quality can be used to ensure business success?
A simple example might help: a marketing manager wants to increase revenue by executing marketing campaigns. He is willing to invest a certain amount of money to reach a certain amount of customers and prospects. As a result of the campaign he expects a return rate of a given percentage of his invest. He is taking into account, that all his selected customers and prospects receive the mailing.
And here is the issue: if he doesn´t know if the customer data are good enough, and if he can’t estimate, if all the mailings will arrive, his prediction on campaign success comes with a huge uncertainty.
How smart would it be, if he could predict the success of the campaign before sending out the mailing? If he knows, if all addresses are correct and still valid? If he can make sure that there are no duplicates?
Having all information on the quality of the data before starting data management, the change from re-active to pre-active data handing is feasible. Data can be optimized before sending out mailings. Decisions can be made, if it is better to invest in data quality improvements instead of sending out mailings and expecting many returns due to wrong customer data.
The solution: it is all about measuring the quality of the data in a proper context. As a precondition, the requirements on the data has to be defined, aligned and communicated. Tools for measuring data quality have to be implemented and a discussion on data quality improvements is initiated.
A smart way to measure the quality of data on the basis of individual rules in the context of business processes is the Data Quality Scorecard from Uniserv. Via different levels on aggregation, a data quality KPI can be generated over all records and all defined rules. With drill down functionality the exact point of reasonable data quality improvement can be identified, actions can be initiated.
The benefit for our marketing manager is obvious: he knows beforehand, if it is worth to invest in the marketing campaign and he is able to predict the success on fact based information on data quality.
Dr. Christiana Klingenberg
Solution & Product Manager, Uniserv GmbH