In this Information age, data and information are vital to an organization’s success. And that vitality is created with a fresh supply of clean and unpolluted customer data. The Data Warehousing Institute estimates that data quality problems cost U.S. businesses more than $600 billion a year. And they weren’t just talking of the unnecessary printing, postage, and staffing costs associated with bad data. When organizations have no grip on the quality of their data, over time the confidence amongst their customer and partner community erodes.

Organizations today use data to generate a multiplicity of information assets (campaigns, operational systems, reports, dashboard etc). These assets form the basis for any strategic action the organization may take.  So when the incoming data is bad, all the downstream systems and assets are contaminated thereby jeopardizing the success of the organization. The impact of bad data has been quantified by several vendors and consulting organizations. Below are some facts and figures around the impact of bad quality (anecdotal & quantified).

  • If customer preferences (Opt outs) is not maintained accurately, enterprises have to fork some serious penalties that increase with each incident.
  • The ratio of the cost to process a transaction when data is clean and what it is accurate is 1:10. Organizations make millions of transactions a year.
  • Data Integration and BI projects either fail or delayed because of bad data.
  • Inaccurate medical diagnosis can sometimes be fatal.
  • Lack of single version of truth (for master data) results in additional spend and/or bad customer service.

To summarize, Data Quality impacts range from a pure transaction level loss up to catastrophic impact for an enterprise. In the words of Larry English,  the cost of bad data may be 10-25 percent of the company’s total revenues. Marketing folks know the cost of customer acquisition, and the renewal potential of each customer on file. Once an organization loses its loyal customer, all the associated revenue potential goes down the drain. So what exactly are the reasons for poor Customer Data Quality? I shall cover them in a later post, for now here is the result from the TDWI Data Quality Survey on this question.

Sources of Data Quality Problems

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