How bad data quality is losing your business money every day
From miscalculations to missed opportunities, bad data quality can be expensive.
In 2019, a data issue caused Hawaiian Airlines to charge customer credit cards in loyalty points instead of dollars. This meant some customer cards were charged up to $674,000.
This was an embarrassing issue for Hawaiian. Fixing the damage cost them thousands in goodwill payments, not to mention the damage to their reputation.
This example shows that financial losses due to bad data quality aren’t only caused by data protection or regulatory fines. There are several ways bad data quality can negatively impact your business.
Missed opportunities
Even If you had the best customer churn prediction model in the world, if the customer data you have is inaccurate or invalid, then it’s not worth having. You’ll just end up losing the opportunity to keep your customers.
Even simple customer journeys such as renewing a customer contract can go terribly wrong if you don’t have correct data. This can leave your customers frustrated, which will have a direct effect on your bottom line.
Poor decision making
Data is key to making important business decisions. Decisions such as where to allocate your marketing budget, or which customers to accept for a loan.
It’s one thing not having the data you need, and understanding the limitations of that. But it’s another to have bad quality data, and using it as if it were accurate. That can give you a false sense of security, and inevitably lead to the wrong decisions being taken, costing your business time and money.
I’ve seen this situation in real life, where click counts on a marketing campaign landing page were duplicated. This data then fed into campaign tracking dashboards, which caused marketing managers to come to the wrong conclusions. Of course in this case I worked on a fix for the upstream issue and told the team what had happened. But it demonstrates how having the right controls and data governance procedures in place is important.
Wasted time
It’s well known that data analysts and data scientists spend the majority of their time preparing data. This is valuable time which could be spent analysing the data, but instead it’s spent cleaning and transforming messy data. In bigger companies, this work could be duplicated where there are silos between teams.
And it’s not just data scientist who suffer the consequences of bad data quality. It impacts anyone who’s ever looked at a business report or dashboard.
Conclusion
Bad data quality is caused by many factors, from manual data entry errors to lack of data governance rules. Whatever the root cause, it’s important to monitor data quality at various points on your data’s journey to end users.
If you’re struggling to convince your team that data quality should be a priority, then I’ve found that looking at the financial impact of bad data quality usually does the trick!