The rise of Enterprise Intelligence is accelerating and industry leaders are reporting dramatic efficiency gains from AI. But other organizations are experiencing data management challenges. According to McKinsey, 70% of companies are facing critical data challenges that prevent AI success and Gartner predicts a 30% failure rate for generative AI initiatives.
One critical differentiator lies in having the right infrastructure and data fabric in place to support the compound requirements of enterprise AI. The AI data lifecycle starts with data collection and a data retention plan spanning years. Whether the source of data is an IOT device or an IBM mainframe, once collected the data must first be classified, and then featurized or otherwise prepared for use before it can be pipelined to a downstream data warehouse or AI application. As data transits this complex data fabric, datasets often undergo multi-modal transformations possibly from files and tables in one format to index vectors in another, but still data governance and compliance controls must be maintained.
Solix Executive Chairman John Ottman explores the challenges and opportunities of enterprise AI in this practical solutioning review.
Download this whitepaper now
About the Author:
John Ottman has over 30 years experience with enterprise applications and cloud infrastructure. He is currently the Executive Chairman of Solix Technologies, Inc. and Co-Founder and Chairman of Minds Inc.
