If implementation, maintenance, and support of a data quality program isn't a line item in your budget, it should be…. It’s probably on your competitor's. Here’s why:
If your company handles outreach to consumers on any level, then ensuring you reach the intended person the first time is always the goal. Comparing the small cost to update a contact record to the cost of the materials mailed out alone should demonstrate the value. If you are reusing data several times without cleaning it, the costs only multiply.
For certain industries, compliance with regulations can be imperative to remaining profitable. The Telephone Consumer Protection Act (TCPA), in particular, typically has a penalty of up to $1500 per calling violation. If a company makes thousands of calls before realizing a data quality issue even exists, fines from TCPA violations could be the hole that sinks the ship.
3. Fraud Prevention
With e-commerce continuing to expand rapidly year after year, the need to mitigate the risk of online fraud continues to grow. If your business interacts with consumers, your success is dependent upon keeping your fraud costs down. Having the ability to correctly verify identities and validate consumers with quality data is imperative to success.
According to research by Gartner, businesses see roughly 40% of their initiatives fail due to poor quality or incomplete data. While this is clearly a revenue issue, poor data quality also creates a drain on efficiency for many departments in an organization. Developing a strategy to implement and maintain a data cleaning solution can have a multi-fold benefit including reduced revenue waste, increased operational efficiency, as well as likely improved morale of staff members that aren’t having to clean up messes left behind by poor quality or incomplete data.
Forecasting is a great benefit for those that use it. It can also be a mirage in the desert of competition for revenue, market share, and influence. Great data quality and a good forecasting tool can give you a leg up on your competition. The longer poor data quality is left to fester the worse your forecasting can become over time. The difference between your forecast estimates and actual reality may be very minimal for a month or two but could devolve into serious consequences after a year.
These are just a few potential issues that can arise from poor data quality. Businesses need to fully understand the potential impacts of not having a data quality solution. If you would like to discuss further, please contact us or email us directly at email@example.com.