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Modernizing Data Quality for the Era of Cloud & AI

Over the past few years, the evolution of technology for storing, processing and analyzing data has been staggering. Businesses now have the ability to work with data at a scale and speed that many of us would have never thought possible. Yet, why are so many organizations still struggling to drive meaningful ROI from their data investments? The answer starts with the quality of your data

The flow of data – raw, diverse, and frequently unstructured – can quickly turn analytics initiatives into failures. Without good data preparation technologies and practices, users of all types are frustrated; their productivity and satisfaction suffer because it’s too hard to get accurate data that’s appropriately structured for their specific project.

Listen to Trifacta’s David McNamara, Bloor Group’s Eric Kavanagh and dbINSIGHT’s Tony Baer to learn:

  • What the business impact of poor data quality is today and how that will exponentially increase with the advent of machine learning and AI
  • Why the speed of modern business requires a new approach to data quality – transitioning from the legacy, siloed processes of the past, to a new approach that prioritizes speed, scale and efficiency, without sacrificing governance and accuracy
  • How the emergence of new trends in analytics such as cloud, machine learning and AI have impacted the data quality landscape