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Too often, organizations ignore the costs associated with bad data from within their databases. A recent Harvard study found that 47% of newly-created records have some sort of quality issue. As expected, research supports that maintaining poor data quality can dramatically hurt organizations financially.
So, what exactly is the financial impact? Here are cost estimates of poor data quality from leading industry analysts who have studied the problem:
1-10-100 Rule: Companies incur an annual cost of $100 for maintaining each incorrect record. For a company maintaining 50,000 records with some sort of quality issue, that organization will incur an annual $5 million cost.
Gartner: On average, organizations assume an annual cost of $15 million for maintaining bad data.
Forrester: Approximately 1/3 of all data analysts spend 40% of their time vetting and validating data before it can be analyzed for decision makers.
IBM: A 2016 study found the yearly cost of poor data quality in the U.S. alone to be $3.1 trillion (yes, that is with a ‘T’).
The costs associated with poor data quality come in many different forms. Some of these costs could be related to your CRM platform if your systems contain inaccurate customer information. For example, one of your loyal client’s email could be linked to a different client’s name. Having established personal digital marketing campaigns, sending just one personal email to the wrong email address could cost your organization future customer trust and loyalty.
Real World Example
A real-world example of the ramifications of poor data quality concerns a man, James Lloyd, when he booked his airline ticket to London. The layover time for one of the flights on Lloyd’s itinerary said 47 years. Glancing at the ticket, Lloyd thought that 47 years was an unusually long time to wait at the airport. While the whole ordeal was eventually laughed off in the end, the story went viral and had an embarrassing impact on the airline company.
The race to become a data-driven organization is more competitive than ever. Digital transformations are occurring rapidly worldwide, requiring data agility to be the main driver towards ‘big data’ technological developments. Ensuring quality data sets the stage for data agility and thus is vital towards the continued lifespan of every business entity.