Data Quality Procrastination, or DQP, affects 63% of organizations, who say that they lack a “coherent, centralized approach” to their data quality strategy. This is a shocking statistic seeing as 99% of organizations believe that data is essential for marketing success, and that 83% of commercial companies believe their revenue is affected by inaccurate and incomplete customer or prospect data. Not to mention that the implementation of a data quality initiative can lead to 15-20% increased revenue, 20-40% increased sales, and a 40% decrease in operating costs (source).
How do you know if you’re suffering from Data Quality Procrastination?
Symptoms of DQP
Data Quality Procrastination is an exceptionally harmful condition, because it affects more than just your revenue. Customers are often the unsuspecting victims of DQP, and their disappointment in mis-targeted or inaccurate marketing is detrimental to their satisfaction rate. A 2014 study found that 94% of respondents reported taking at least one of the below actions in response to a company that consistently mis-targeted them in their email marketing efforts as a result of inaccurate data.
Consumers want to be marketed to with personalized, relevant content. Seventy-three percent of consumers prefer brands that use their personal information to make their shopping experiences more relevant, and 86% of consumers say personalization plays a role in their purchasing decisions. But, unfortunately, consumers of companies with Data Quality Procrastination often go without the personalized service they long for, as companies without a solid data quality management plan are unable to keep up with the constant evolution of consumer information. The longer Data Quality Procrastination goes untreated the more your database will decay, generally at a rate of 25% for B2C and 70% for B2B per year, which continues to erode at consumer opinion of your brand.
Wasted Time and Money
The average company loses 12% of revenue due to bad data, and as much as 50% of a typical IT budget can be spent on “information scrap and rework” (source). The four basic characteristics that negatively impact data quality are inaccuracies, duplications, gaps, and outdated information. If a record has any of these characteristics it decreases the quality of your database as a whole, which can mean ineffective outreach and missed opportunities.
There are many studies that attempt to quantify exactly how much 1 bad record will cost; some say it’s ten times more expensive to complete a unit of simple work with bad data, and others say it costs about $1 to verify a record as it is entered, about $10 dollars to fix it later, and $100 if nothing is done, as the ramifications of the mistakes are felt over and over again. While it’s difficult to exactly pin down a dollar amount, there are ramifications that go even beyond financial revenue.
Employee morale is a lesser known victim of low data quality as frustrations with wrong or missing information arise or workloads are increased because of inadequate databases. Often also, internal trust between departments can be damaged in situations where one department requests data from another and the delivered information is found to be incorrect, even if the delivering department had nothing to do with the data input or management.
Lots of returned direct mail pieces, bounced emails, and disconnected numbers
The average company wastes $180,000 per year simply on direct mail that is returned because of inaccurate data. Considering the fact that about 36 million people move each year, that cost most likely doesn’t even begin to cover the amount of direct mail that is received by an incorrect recipient and thrown away. The Econsultancy/Adestra Email Marketing Census found that 67% of surveyed businesses reported problems delivering email, and more than half of respondents blamed overall problems with their email campaigns on low quality data. It’s safe to say that the 50% who said this are also suffering from Data Quality Procrastination. Lastly, if you’re of the 22% who feel their contact data is inaccurate and your phone calls often hit a disconnected line, you may be suffering from Data Quality Procrastination.
Other telltale signs of Data Quality Procrastination:
- Keeping data in multiple, disconnected silos
- Conducting meetings that start with the question “How did we not know that?”
- Reading articles on data quality, but never advancing to the next step
- Ever getting these responses when contacting a prospect or customer:
Don’t be a statistic. If you or a loved one has been diagnosed with Data Quality Procrastination, please don’t hesitate to get your data’s condition under control.