Overview
Data quality metrics are a set of standards used to measure the accuracy, completeness, and consistency of data. According to a study by Gartner, poor data quality costs organizations an average of $12.9 million per year. The most commonly used data quality metrics include accuracy, completeness, consistency, and timeliness. For instance, a study by Experian found that 77% of companies believe that data quality is crucial for business decision-making. However, achieving high-quality data is a challenge, with 60% of companies citing data quality as a major obstacle. As data continues to grow in volume and complexity, the importance of data quality metrics will only continue to increase, with some predicting that the data quality tools market will reach $1.5 billion by 2025. The use of data quality metrics will be crucial in ensuring that organizations can trust their data and make informed decisions. What will be the next major development in data quality metrics, and how will it impact the way we approach data management?
Key Facts
- Year
- 2022
- Origin
- Vibepedia
- Category
- Data Science
- Type
- Concept