How Bad Data Costs Businesses: From Missed Opportunities to Lost Revenue

Did you know that poor data quality costs businesses an average of $12.9 million per year?1

In an era where data is often touted as "the new oil," its value is undeniable. However, the integrity of that data is just as crucial. Bad data frequently infiltrates organizational data supply chains, undermining analytics efforts and leading to unreliable insights, misguided decisions, and significant revenue loss.

In this blog, we'll explore the real-world consequences of bad data, prominent cases of data disasters, and discuss how you can safeguard your business against such costly errors.

The High Stakes of Data Quality

In 2017, The Economist declared data more valuable than oil. Today, organizations rely on data for everything from shaping strategies to making informed decisions. The quality of your data directly impacts the effectiveness of these tasks.2

Bad data doesn't just lead to minor hiccups; it can result in substantial financial losses and reputational damage.

Prominent Cases of Data Disasters

1. Samsung Securities: The $300 Million Typo

In a striking example of how a simple error can lead to massive losses, Samsung Securities faced a catastrophic data mishap in 2018. An employee intended to distribute a dividend of 1,000 won per share to employees. Instead, due to a typo, they issued 1,000 Samsung Securities shares per employee.

This mistake led to the unintended issuance of shares worth $105 billion. Although the error was corrected within 37 minutes, Samsung Securities suffered a loss of $300 million. This incident underscores the critical need for stringent data assurance processes and automated checks to flag anomalies before they escalate into financial disasters.3

2. Uber's Costly Oversight: A Data Dilemma

Uber faced its own data quality crisis when an update to their accounting system resulted in drivers being underpaid. The system failed to deduct taxes and fees before calculating Uber's 25% commission, meaning drivers received less than they were owed.

The error went unnoticed for years due to the lack of a system to double-check their data. It wasn't until a manual update of their terms of service that the mistake was discovered. Uber had to compensate their drivers, costing the company millions of dollars—approximately $900 per driver.4 This situation highlights the importance of taking data quality seriously and implementing checks to catch errors before they snowball.

The Broader Impact of Bad Data

Bad data doesn't just lead to isolated incidents; it has far-reaching consequences:

• Lost Revenue Opportunities: Businesses lose up to 25% of their potential revenue due to poor data quality.
• Operational Inefficiencies: Data errors can halt operations, costing companies an average of $15 million annually.5
• Eroded Customer Trust: 84% of customers believe the experience a company provides is as important as its products and services. Bad data can lead to negative customer experiences, driving them away.6
• Regulatory Non-Compliance: Inaccurate data increases the risk of non-compliance with regulations like GDPR and CCPA, leading to hefty fines.

How to Mitigate the Impact of Bad Data

To protect your organization from the pitfalls of bad data, consider the following strategies:

Implement Data Management and Quality Assurance

• Data Governance: Establish policies and procedures to manage data effectively.
• Regular Audits: Conduct frequent data quality assessments to identify and rectify issues.

Leverage Data Reliability and Observability Tools

Investing in a data reliability tool that offers end-to-end observability into your data supply chain is crucial. Such a tool can:

• Predict and Prevent Data Breaks: By monitoring data in real-time, it can alert you to potential issues before they become problematic.
• Direct You to Root Causes: Quickly identify where and why a data error has occurred.
• Enhance Decision-Making: Provide trustworthy data that underpins strategic decisions and daily operations.

Why Choose PRYZM?

PRYZM is one of the most trusted data reliability and observability platforms that meticulously monitor every data point, delivering the trust you need for confident decision-making and streamlined operations.

With PRYZM, you can stay ahead of data issues, ensuring accuracy and compliance across your organization.

Conclusion

The consequences of bad data extend beyond immediate financial losses—they can damage your reputation, erode customer trust, and hinder long-term success. By prioritizing data quality through robust management practices and advanced observability tools like PRYZM, you can safeguard your business against these risks.

Ready to protect your business from the hidden costs of bad data? Schedule a demo with PRYZM today and discover how our advanced data reliability and observability platform can empower your data-driven decisions.

References:

*1 Gartner, Article, How to Improve Your Data Quality

2 The Economist, Article, The world’s most valuable resource is no longer oil, but data

3 IIIE Spectrum, News Article, Samsung Securities' $105 Billion Fat-Finger Share Error Triggers Urgent Regulator Inquiry

4 Business Insider, Article, Uber is paying its NYC drivers 'tens of millions' because of an accounting error that underpaid them for years

5 Gartner, Article, How to Stop Data Quality Undermining Your Business

6 Salesforce Buyers Say Experience a Company Provides Matters

*