Risks of bad data
Data is the backbone of your business and drives decision-making by providing key business insights into your customers. Each touchpoint you have with a customer is an opportunity to learn more about their needs, purchasing behavior, and how that aligns with your company’s business offerings and goals.
However, the quality of your data will deteriorate over time if care is not taken, in much the same way that your health will deteriorate if you only consume sweets, soft drinks, and junk food.
When you are deliberate about eating nutritious foods, you provide your body with the fuel it needs to be strong and healthy. By ignoring your body’s needs and fueling it with junk food, your health suffers, and the cost of repair can be great, not to mention time consuming. For example, losing 30 kilograms/pounds is harder work than maintaining a healthy weight to begin with.
Think of data as the fuel that determines the overall health of your business. Poor data quality affects all aspects of the business including operational efficiency and the ability to communicate accurately and relevantly to customers. Additionally, poor data quality can lead to manufacturing delays, lost revenue, incorrect analysis of market trends, and increased costs, among other challenges.
Erroneous decisions made from bad data are not only inconvenient but also extremely costly. According to Gartner research, “the average financial impact of poor data quality on organizations is $9.7 million per year.” (https://www.anodot.com/blog/price-pay-poor-data-quality) According to Thomas C. Redman, in 2016, “IBM estimated the yearly cost of poor-quality data, in the US alone, to be $3.1 trillion.” (https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year)
Forrester reports that “nearly one-third of analysts spend more than 40 percent of their time vetting and validating their analytics data before it can be used for strategic decision-making” (https://www.anodot.com/blog/price-pay-poor-data-quality) while Redman estimates that “it costs 10 times as much to complete a unit of work when the data is flawed in any way as it does when the data is good.” (https://hbr.org/2022/11/bad-data-is-sapping-your-teams-productivity)
The importance of data quality cannot be ignored. Imagine a salesperson following up with a customer on their order, only to discover that the phone number and email on file are not correct. What is the cost to a business if a product was shipped to an old physical address? Not only will the company bear the cost of the replacement shipment, but also the labor costs of tracking down the correct address and doing damage control for the now unhappy customer.
Employee time spent correcting errors instead of making sales, lost revenue and your company’s reputation are costly prices to pay as a result of poor-quality data.
