While we wouldn’t let it happen in the car, we often let it happen in our work. Data quality? It sounds technical, boring and definitely like ‘’not my department's problem’’. But the truth is: Data quality is the safety belt in our business. And without it, we’re just driving blind.
Take the case of Toys "R" Us. In 2017, this biggest US toy retailer filed for bankruptcy after 70 years in the business. Their customer data was fragmented and outdated. Such a situation blocked their ability to personalize marketing efforts and compete with online giants like Amazon. They struggled with data quality issues and failed to adapt.
"Bad data is worse than no data," reflects recent Gartner's report. "It creates a false sense of confidence while going in the wrong direction’’.
The stakes are high
According to Gartner's Data quality study, businesses lose an average of $12.9 million per year due to poor data quality. Plus countless missed opportunities and damaged customer relationships. What is more, bad data creates regulatory risk for your company. Example? GDPR fine costs up to €20 million with an average fine of €4.7 million (EU Commission, 2023)
Professor Thomas Davenport from MIT Sloan School of Management notes in his Harvard Business Review article, "Data quality is not just a technical issue. It impacts every decision in the company’’.
- IBM estimates that the yearly cost of poor data quality in the US alone exceeds $3.1 trillion
- A Gartner study reveals that 60% of organizations don't measure the annual financial cost of poor-quality data
- McKinsey research shows that companies with high-quality data achieve 20% higher financial performance than their competitors.
So, what do companies do about it?
JPMorgan Chase’s digital transformation journey highlights the importance of data quality. In 2020, the bank identified a major issue: inconsistent customer data across different systems led to missed cross-selling opportunities and frustrated customers who had to repeatedly provide the same information.
To resolve this, JPMorgan Chase launched a major data quality initiative focused on:
· Unifying customer profiles across all platforms
· Implementing real-time data validation to prevent manual errors
· Creating a single source of truth for customer information
The results within 18 months were significant:
· 34% increase in successful cross-selling
· 45% reduction in customer complaints
· $2.1 billion in additional revenue from better-targeted services.
Beyond this project, JPMorgan Chase continues to invest heavily in data management. In 2023, the bank estimated that its AI and data projects would generate over $1.5 billion in business value. The key lesson here? Investing in data technologies delivers real financial results.
Data quality impacts also our own lives. International Journal of Medical Informatics study shows that up to 60% of electronic health records (EHR) have at least one quality problem - from wrong phone numbers to mixed-up medical histories.
Think about that for a moment. Would you feel comfortable if your doctor was working with incomplete or incorrect information about your allergies or medications? This isn't just about messy databases. It's about our lives!
The study shows that when hospitals implemented data quality technologies, the results were significant. They’ve cut documentation errors by 40%, reduced duplicate tests by 25% and made patients much happier. As the authors summarize: "When healthcare providers can trust their data, they can focus on what really matters - taking care of patients."
OK. So what can I do about it?
Gartner proposes the following framework for improving data quality in your company.
- Build a data-driven culture. Make data quality everyone's responsibility through training and clear communication.
- Integrate processes. Embed data quality checks into every business process. Don't treat it as a separate function, but as a basic part of daily operations. Connect data quality with your KPIs.
- Invest in the right technology. Invest in tools that automate data validation and cleansing.
- Measure and iterate data quality metrics.
The next steps
In the age of AI, data quality becomes even more critical. "AI systems are only as good as the data they're trained on," warns Dave Friend from Forbes. "Poor quality data doesn't only impact decisions today. It can also amplify mistakes into the future."
Our message is clear: in today's business world, the quality of your data determines the quality of your decisions. The latest technologies are addressing this challenge. For example, the Black Tiger framework streamlines data quality management by connecting all your data sources in one place. Enabling teams to directly control and improve data quality. By reducing technical complexities, such solutions deliver reliable data when you need it most.
As concluded in Forbes: ‘’In the end, data quality is about trust. When you can trust your data, you can trust your decisions. And in business, that makes all the difference.’’
Written by Michal KOLATAJ
