The Staggering Cost of Poor Data
- Brad Boldenow
- Oct 10, 2023
- 3 min read
In today's fast-paced business world, data is the lifeblood of decision-making. It empowers companies to gain insights, drive growth, and maintain a competitive edge. However, if you don't take of your data, it won't take care of you. In fact, if you're not taking the time to properly care for your data, your data will end up costing you a lot of money.
Big Money
Gartner research has found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses. Additionally, Forrester found between 10% and 40% of all business intelligence efforts go to waste due to poor data quality.
Data quality issues are not just a few headaches or nickels and dimes. When you think about it, it's not terribly surprising. Think about how much of your processes are dealing with data: probably almost all of them. When data touches nearly all aspects of a business or operation, it shouldn't come as a surprise that the impact poor quality data can have is super-sized.
It Adds Up Quickly
These big numbers can seem abstract. The real cost of poor data quality is better felt when we break things down. The 1-10-100 Rule is a widely accepted framework when trying to calculate the granular costs of poor data. The rule states that it costs:
$1 to identify each incorrect data record
$10 to fix each incorrect data record
$100 for each incorrect data record that an organization chooses to ignore.
For example, let's say you have a digital advertising campaign with 5 ad groups, each ad group having 10 creatives. If these creatives are labeled incorrectly, to fix this it will cost your business $550. If you don't catch the issue it will end up costing you $5,000. All of this adds to the cost of your marketing and eats away at your return on investment.
But it doesn't stop there...
The reason why poor data quality costs so much can be best summarized with the renowned "Rule of Ten" coined by Thomas Redman. It takes 10x as much time to complete a unit of work when the input data are defective compared to when the data is correct.
If we follow the digital advertising campaign example, it will take your team 10x the amount of time to report on this campaign because of the data quality issues. That's time wasted in meetings simply trying to figure out up from down and doing data grunge work such as manually cleaning data. No one has ever enjoyed any of these things - it's a drain on morale that could have easily been avoided. Additionally, because it takes 10x longer to report, odds are by the time reporting is ready, the insights will be outdated and your team will be busy with the next task having learned nothing.
Why Are We Doing This to You?
Okay we'll stop. By this point I hope you understand how poor data quality is impacting and costing your organization. So why did we put you though that? Well, because that's what we have been going through until we finally had enough and built Namie.
Namie helps you prevent poor data quality at its source, rather than trying to identify it and fix it after its damage has already been done. With Namie, you can set up conventions for names and URLs and use these conventions to create consistent and clean names and URLs in seconds.
Namie's emphasis on clean and consistent data guarantees that you not only save money but also unlock the true potential of your data. Want to learn more about Namie? Reach out to us at the form at the bottom of this page!