Data Quality Investment benchmarks
Data Quality investment benchmarks According to Gartner’s reports, organizations that prioritize data quality can see significant benefits. One report highlights that through 2025, at least 30% of GenAI projects will be abandoned due to poor data quality, underscoring the need for robust data quality investments. Gartner also notes that poor-quality data costs organizations on average $12.9 million annually. (1)
Forrester’s 2022 IT and Digital Budget Benchmarks report emphasizes the importance of benchmarking digital and data quality investments against industry standards. This report provides detailed insights into how different industries allocate budgets for digital transformation and data quality initiatives. In another Forrester report, it is mentioned that organizations investing in data quality and analytics are 23 times more likely to acquire customers and 19 times more profitable.(2)
Forrester highlights that 70% of enterprise-level companies are already using generative AI, and another 20% are exploring its use. However, the primary limiting factor for successful AI implementation is data quality. Forrester suggests that managing data quality for AI applications requires different skills and a closer alignment between technical and business teams. Investments in data quality are therefore essential to leverage AI effectively and achieve competitive advantage.(3)
A McKinsey Global Data Transformation Survey found that leading firms reduce non-value-added tasks due to poor data quality from 29% to as low as 5-10%. This highlights the significant operational efficiency gains that can be achieved through effective data quality investments.