According to Gartner, “Every year, poor data quality costs organisations an average of $12.9 million.” Data quality is essential for any business that relies on data for decision-making and uses data in data modelling. Poor data quality can lead to a range of negative impacts on a business, including financial losses, missed opportunities, and reputational damage. This blog post will explore the cost of poor data quality and how it can impact your business.
First, let’s define what we mean by “poor data quality.” Poor data quality can include inaccurate, incomplete, inconsistent, or outdated data. It can also include data that is not properly maintained, secured, or validated. Poor data quality can arise from a variety of sources, including human error, system issues, data integration problems, and more.
So, what is the cost of poor data quality? The answer can vary widely depending on the business, the industry, and the specific context. However, some common costs of poor data quality include:
- Lost revenue: Poor data quality can lead to inaccurate sales forecasts, missed sales opportunities, and customer churn. This can result in significant revenue losses for a business.
- Increased operational costs: Poor data quality can lead to inefficient processes, increased manual effort, and higher operational costs. This can include the cost of correcting data errors, conducting manual data entry, and managing data quality issues.
- Compliance violations: Poor data quality can lead to compliance violations and penalties. This can include violations of data privacy laws, data security regulations, or other industry-specific regulations. An analysis of how to stay on top of your cyber security requirements has been discussed in detail here.
- Reputation damage: Poor data quality can damage a business’s reputation, leading to decreased customer trust and loyalty. This can have long-term impacts on a business’s bottom line.
Overall, the cost of poor data quality can be significant, both in terms of financial losses and other negative impacts. Investing in data quality management and implementing best practices can help to mitigate these costs and ensure that your business is making decisions based on reliable, accurate data. In the next section, we’ll explore some strategies for improving data quality in your organisation.
| Lessons from Uber: Accounting Error Resulted in “Tens of Millions” of Underpaid Compensation |
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| In 2017, Uber admitted an overcalculation of its commission cut in its accounting system caused drivers to be underpaid. The error started from an update in 2014 to Uber’s terms of service. As a result, Uber skipped subtracting taxes and fees before collecting a 25% commission. Because of the accounting error, Uber had to repay its drivers “tens of millions” of dollars. In the end, it cost Uber $900 per driver. |
| Lessons from Hawaiian Airlines: The Airline Accidentally Charged Customers Up To $674,000 For Award Tickets |
| When vacation-goers booked flights with Hawaiian Airlines in 2019, they were surprised to find that their tickets — which were intended to be free award flights — actually cost tens of thousands of dollars. The culprit of this was a faulty airline booking application that accidentally charged customer accounts in dollars instead of airline miles. A ticket that was supposed to be redeemed for 674,000 miles turned into a sky-high price of $674,000 USD! |
Strategies for Improving Data Quality in your Organisation
- Implement data quality processes: Establishing data quality processes is essential for identifying and correcting data quality issues. These processes can include data profiling, data cleansing, and data validation. Implementing these processes will help to identify and correct data quality issues before they become major problems.
- Use data quality tools: Data quality tools can help to automate data quality processes and make them more efficient. These tools can include data profiling tools, data cleansing tools, and data validation tools. Implementing these tools will help to streamline data quality processes and ensure that they are applied consistently across the organisation.
- Ensure data accuracy and completeness: Accuracy and completeness are key components of data quality. To ensure data accuracy, implement processes for data validation, verification, and auditing. To ensure data completeness, ensure that all required data fields are populated and that there are no missing values.
- Train employees: Employee training is essential for ensuring that data quality processes are followed correctly. Provide training on data quality best practices, data entry standards, and data quality tools. This will help to ensure that employees are aware of the importance of data quality and are following the correct processes.
- Establish data governance: Data governance involves establishing policies, procedures, and standards for managing data quality. This can include defining roles and responsibilities, establishing data quality metrics, and monitoring compliance with data quality standards.
By implementing these strategies, you can improve the quality of your data and reduce the costs associated with poor data quality. In conclusion, poor data quality can have significant costs for a business. The key to reducing these costs is to implement data quality management processes, use data quality tools, ensure data accuracy and completeness, train employees, and establish data governance. By investing in data quality, your business can make more informed decisions, improve operational efficiency, and enhance customer satisfaction.
References:
https://www.businessinsider.com/uber-paying-nyc-drivers-tens-of-millions-accounting-error-2017-5?international=true&r=US&IR=T
https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality
About Skillfield:
Skillfield is an Australian based IT services consultancy company empowering businesses to excel in the digital era. Across our two main practices of Cyber Security & Data Services, our talented and committed professionals provide smart and simplified solutions to complex cyber security and big data challenges.
