By now, we all know that data drives decision-making, fuels growth, and underpins the operations of businesses across industries. However, it’s important to remember the dark side of data, one that can cripple an organisation financially: poor data quality.
According to Gartner, poor data quality costs organisations an average of $15 million per year due to lost revenue, operational inefficiencies, non-compliance with data regulations (e.g., HIPAA or GDPR), and reputational damage. In the U.S. alone, IBM reported that the annual cost of poor data quality is around $3.1 trillion, encompassing lost revenue opportunities, inefficiencies, and the additional effort required to correct data errors. Experian’s research indicates that on average, organisations believe poor data quality directly impacts 23% of their revenue, highlighting the significant financial consequences of not maintaining high data quality standards.
Several companies have faced significant fines for non-compliance with data regulations, emphasising the importance of robust data protection practices. In May 2023, Meta Platforms Ireland Limited was fined a record €1.2 billion by the Irish Data Protection Commission for unlawfully transferring personal data to the United States without adequate protection mechanisms. Similarly, in July2021, Amazon was fined €746 million by the Luxembourg National Commission for Data Protection for violations related to its advertising targeting system, which processed personal data without proper consent.
These fines underscore the stringent requirements of data protection regulations and the severe financial consequences of non-compliance. However, the broader impact of poor data quality goes beyond regulatory fines. It affects operational efficiency, customer trust, and business growth.
Given the high cost of poor data quality, investing in Data Quality Management (DQM) should be a core data objective. Here’s how organisations can improve their data quality:
1. Identify Critical Data Assets: Start by canvassing business users for pain points to prioritise focus and resources. Identify Critical Data Elements (CDEs) crucial for business operations.
2. Track and Categorise Data Errors: Common errors include duplicates, incomplete records, outdated information, and incorrect data entries.
3. Estimate Correction Costs: Calculate the time and resources spent on correcting data errors, including employee hours and software tools.
4. Evaluate Business Impact: Assess how data errors affect business processes, decision-making, and customer satisfaction. Quantify the impact of delays, rework, and inefficiencies caused by poor data quality.
5. Prioritise High-Impact Areas: Focus on areas where data improvement can have the most significant impact.
6. Implement a Data Quality Improvement Plan: Use DQM practices such as data cleansing, validation, enrichment, and governance to maintain high data quality standards.
The cost of poor data quality extends far beyond immediate financial losses. By investing in robust Data Quality Management, organisations can transform their data into a powerful asset, driving better decisions, enhancing customer experiences, and achieving sustainable growth. Remember, managing data quality should be a continuous activity.
At Acuity Data, we specialise in helping businesses improve their data quality and unlock the full potential of their data assets. Contact us today to learn how we can help you implement a successful Data Quality Management strategy.
