watsonx.governance

Many organizations set out with the best intentions when embarking on AI and analytics projects but often encounter significant hurdles due to a crucial shortcoming: an insufficiently developed data strategy. The effectiveness and trustworthiness of AI systems are intrinsically tied to the quality and governance of the data on which they are built. Businesses frequently struggle to deliver high-quality data for business use, a challenge intensified by the ever-changing data privacy and security regulations. As data becomes more diverse and widespread across different sources and environments, comprehensive data governance becomes essential.

In the realm of Generative AI, the growing complexity of managing structured and unstructured data and ensuring compliance with various regulations highlights the urgent need for robust governance strategies. Without effective data governance, organizations risk using flawed or biased data in their AI models, leading to unreliable insights and decisions. Therefore, establishing a holistic strategy that spans across all data sources, pipelines and AI models is critical to harnessing the full potential of data while ensuring regulatory compliance, data quality and risk management.

IBM responds to these challenges with a robust suite of tools that transform data governance, enhancing productivity, providing scalable data quality and governance across complex data landscapes. Through AI and machine learning, IBM automates data profiling, cleansing and monitoring to ensure consistent data quality and accuracy.

We are excited to share that Gartner recently named IBM a Leader in the 2024 Gartner® Magic Quadrant™ for Data and Analytics Governance Platforms. 

Read the full article and access the Gartner Report.