The Gartner presentation, “How Can You Leverage Technologies to Solve Data Quality Challenges?” by Melody Chien, underscores the critical role of data quality in modern business operations. High-quality data is the “blood” that sustains the organizational value chain—impacting everything from logistics to services, sales, and marketing. Poor data quality, on average, costs organizations $12.9 million annually, or 7% of their total revenue. However, the more alarming insight is that 59% of organizations do not measure their data quality. This lack of awareness leads to undetected issues, reactive data cleansing, and costly downstream impacts (see the webinar: https://webinar.gartner.com/673962/agenda/session/1506359?login=ML)
The presentation highlights common challenges organizations face when dealing with data quality. These include overly manual processes for identifying and resolving issues, heavy reliance on domain experts, and a need for more automation in rule design and deployment. Organizations often need help scaling their data quality initiatives across new environments or use cases, leaving them unable to monitor and resolve problems proactively. The result is a broken, reactive process that fails to prevent data quality issues at their source.
Gartner’s solution emphasizes adopting augmented data quality technologies that use automation, AI/ML-driven insights, and metadata-driven workflows to improve efficiency. Key functionalities include automated data profiling to detect patterns and anomalies, rules management to streamline the design and enforcement of quality checks, and proactive monitoring to alert and triage issues before they propagate downstream. Augmented solutions integrate advanced capabilities of AI/ML algorithms for predictive quality and advanced technologies for automated rule generation and contextual recommendation.
DataKitchen’s Open Source DataOps Data Quality TestGen software fits seamlessly into the framework Gartner has laid out. TestGen addresses many of the highlighted challenges by automating the creation and execution of data quality tests. This reduces reliance on manual processes and accelerates the identification of errors, enabling organizations to prevent issues rather than react to them. By embedding data quality tests into data pipelines, TestGen ensures continuous, proactive monitoring—delivering on Gartner’s call for scalable and automated solutions. Additionally, TestGen’s lightweight and open-source nature allows organizations to rapidly adopt the tool and scale it across diverse environments, making it ideal for tackling the growing complexity of modern data systems.
In alignment with Gartner’s vision of “fixing data, knowing data, and trusting data,” DataKitchen’s DataOps Data Quality TestGen empowers organizations to implement business-driven workflows that enforce data quality at every stage. The result is a robust, scalable solution that ensures clean, reliable data—driving operational efficiency and improving business outcomes.
