Economic profitability: How data quality can affect you

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Discover how data quality helps improve customer service and where the economic return on data quality lies.

May 19, 2017
Technicians and business managers often have difficulty justifying the cost of implementing data quality in their organizations. This is so despite the many studies and surveys that attest to the importance of data quality in achieving greater economic profitability .

The problem lies in the potential disconnect between those who actually see data quality problems and those who make the decisions and need to understand the impact of data quality on the economic viber database of their company.

In this article we are going to analyze a specific business case for the implementation of a data quality solution across the entire company. This case represents a real-life example of data quality that reveals the benefits that a well-designed data quality solution for the entire company brings in terms of economic profitability .



Data quality as an essential part of MDM


Economic profitability: how data quality helps improve customer service
Speed, accuracy, and agility are hallmarks of a successful customer experience. Do you know how much a customer service call can cost your organization? According to data from BI-BestPractices , costs range from $300 to $350, so you need to make sure that this effort pays off.

This figure includes costs for equipment and infrastructure, staff, training, downtime and, of course, call duration. Managing each interaction with the consumer well not only impacts their perception of the way the company does things, but also influences their loyalty and future purchasing decisions.

Data quality is a key aspect for a fluid and satisfactory experience for the consumer and for economic profitability to be a reality, as far as the organization is concerned.

When the company's information that its users can access is at the ideal level of data quality (integrated, consistent, error-free and without duplications), it is possible to achieve:

Greater empathy in customer service : since customer service personnel have more information, derived from historical data of each consumer and others with a similar profile.
Faster response times: Increases the speed at which customer requests can be responded to, as it is easier to find the correct information.
Optimizing sales process management: derived from a deeper and more complete understanding of purchasing patterns.
Reduced staff costs: When information is of sufficient data quality, the economic return is reflected in things like the need for fewer employees to cover the same number of calls in a call center. The speed and accuracy with which they can access the correct information in each case allows them to serve more customers.
Increased possibilities of closing sales and cross-selling: the increase in data precision achieved by ensuring its consistency, completeness, accuracy and updating is crucial when it comes to identifying the profiles of the most receptive and advanced customers in their purchasing cycle.