Data quality is the measure of how usable and applicable data is for supporting an organization’s priority use cases, including AI and machine learning initiatives. It is typically a primary goal of effective data management and governance. However, too often, organizations overlook data quality, treating it as an afterthought rather than a foundational priority. This oversight can significantly undermine the effectiveness of advanced initiatives, from analytics to AI, making a proactive, embedded approach to data quality essential for driving reliable insights and innovation.

Data quality is essential as poor data quality costs organizations an average of $12.9 million annually, according to Gartner. Beyond financial losses, it undermines decision-making, operational efficiency, and customer trust, making reliable, high-quality data a critical asset for business success. Source: https://www.gartner.com/en/data-analytics/topics/data-quality

1. Many organizations overestimate deployment time for data quality tools, often by double, creating unnecessary barriers and trust issues between business and IT. We ensure a time-efficient deployment through an all-inclusive onboarding process, making it easier and faster to launch a successful data quality program. Source: https://www.gartner.com/smarterwithgartner/how-to-stop-data-quality-undermining-your-business 2. The biggest data quality challenge, as identified by Gartner, stems from data stored in disconnected silos, leading to overlaps, gaps, and inconsistencies that complicate standardization. Neomir addresses this with a system-agnostic approach, enabling seamless connections and streamlined data quality across platforms. Source: https://www.gartner.com/en/data-analytics/topics/data-quality 3. According to Gartner, nearly 60% of organizations fail to measure the financial impact of poor data quality, leading to reactive responses, missed growth opportunities, higher risks, and lower ROI. Neomir changes this by making data quality measurable. The more you work with the tool, the greater the insight into—and impact on—your data quality. Source: https://www.gartner.com/smarterwithgartner/how-to-stop-data-quality-undermining-your-business