In today’s world, big data is becoming more and more commonplace. Along with big data, bad data is also becoming more prevalent. Even if only 5% of the data we collect is error-ridden, from 1 Exabyte of data, 50 Petabytes of that information is useless. That’s a lot of data! All that money spent on collecting the 50 Petabytes of data is fruitless as conclusions from malformed data will be incorrect. No organization endorses wasteful spending. Let’s take a look at the cost of bad data quality, and how much money you could be saving by cleaning up that data.
What Is Bad Data?
To understand what bad data quality is, we first need to remember what good data quality is. Data quality is measured by many factors. These measurement factors include:
When organization data includes these key factors, we can trust and rely on that data for analysis and trust the decisions and recommendations the studies surface.
Poor data can include missing, incorrect, outdated, and inaccurate information. This bad data can cost you more money than you realize. According to RingLead, each record with bad data costs you about $100. RingLead credits this cost to things like printing and mailing to wrong addresses, server space lost to duplicate files, incorrect marketing segmentation and losing dissatisfied customers.
What is the True Cost of Bad Data?
The cost of cleaning bad records is pretty high. In 2015 it was $200,000 but dropped to $10,000 this year. As you can see from the graphs to the right provided by RingLead, the cost of bad records is even higher than the cost to clean those bad records. Overall, if you clean your bad records, yes you will be out some money. But compared to the cost of not cleaning your data, that small payment to clean your data doesn’t seem so big now.
Cost savings are great, but many times managers don’t just want savings. They want to see revenue as well. Clean data not only provides cost savings, but it also provides income. In the image to the left, we can see an example provided by RingLead. This hypothetical company gets $24.5 million more in revenue because of clean data, and $1.8 million in savings from cleaning their data. While this example is hypothetical, it’s very realistic as well.
With clean, accurate data comes greater credibility and a better decision-making foundation. By cleaning your organization’s data, you can make business decisions and trust that your data is telling you the truth. Those decisions are what turn into extra revenue.
What Can I Do?
Data is becoming more and more important to every type of business. Don’t settle for bad data just because paying to clean it up may be an expensive decision for your business. The costs savings alone from cleaning up the data are worth investing, not to mention the additional revenue you can make from more reliable data. From automation and modeling to identify malformed data elements to full evaluations of your measurement stack to isolate the data corruption source, there are many options for combating your data quality issues, but the real question still stands: How much is your bad data costing you?