Only 8% of Chief Data Officers are satisfied with data quality in their organizations.   While unstructured data quality for classification, such as sentiment analysis, has long been a frequent complaint (and pervasive problem) in the industry, the advent of Gen AI technologies are accelerating the dangers and heightening the risk for organizations to make poor business decisions.  The good news:  there are new effective “trusted AI” approaches and technologies to not only help effectively address pervasive data quality issues, but  help ensure alignment and adherence to  emerging global AI standards (such as the EU AI act) for accuracy, transparency, validation and more.

This session will examine

• The state of “data quality” and challenges in the social and media analysis industry.
• How to measure and understand data quality
• Specific actions and approaches to validate, track and fine tune for better accuracy, engender “trust” in the data and drive deeper adoption for more advanced research, such as predictive analytics.