AI Powered Data Quality Tool - How organizations could solve their data quality issues?
This webinar is part of the Consumer Products and Retail webinar series. In these 30 min sessions, we discuss the challenges in today’s disruptive environment, the opportunities for transformation and actions you can take to navigate as well as to plan for the future.
Please listen to our guest speaker, Frank Kody – EY Sweden’s Advanced Analytics leader, introduce how EY’s Data and Analytics teams are leveraging AI and machine learning to address data quality challenges at their clients.
Poor data quality is not a new problem faced by enterprises, but it is a growing one. Several factors are putting data quality back into the limelight:
1. Increasingly data driven enterprises
2. The exponential growth of data over the past few years
3. The increasing connectiveness and sharing of data throughout an enterprise
These factors not only lead to an ever growing technical proliferation of bad data throughout an enterprise, but they also result in significant financial loss. IBM has estimated that American companies lose $3.1 trillion a year due to poor data quality. While Gartner has mentioned that American companies on average lose $13 million a year. So why do organizations continue to use the same methods they’ve employed over the past 20 years to solve their data quality issues?
Видео AI Powered Data Quality Tool - How organizations could solve their data quality issues? канала EY Finland
Please listen to our guest speaker, Frank Kody – EY Sweden’s Advanced Analytics leader, introduce how EY’s Data and Analytics teams are leveraging AI and machine learning to address data quality challenges at their clients.
Poor data quality is not a new problem faced by enterprises, but it is a growing one. Several factors are putting data quality back into the limelight:
1. Increasingly data driven enterprises
2. The exponential growth of data over the past few years
3. The increasing connectiveness and sharing of data throughout an enterprise
These factors not only lead to an ever growing technical proliferation of bad data throughout an enterprise, but they also result in significant financial loss. IBM has estimated that American companies lose $3.1 trillion a year due to poor data quality. While Gartner has mentioned that American companies on average lose $13 million a year. So why do organizations continue to use the same methods they’ve employed over the past 20 years to solve their data quality issues?
Видео AI Powered Data Quality Tool - How organizations could solve their data quality issues? канала EY Finland
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