By David Roe
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The Rise Of Self-Learning Enterprise AI
Ajay Khanna, vice president of marketing at Reltio, a data management company based in Redwood City, Calif., said that while there is quite a bit excitement about the prospects of AI and machine learning (ML), most companies are still not ready for any serious level of cognitive computing. “The key challenge is the quality of the data to attempt such an endeavor. Machine learning requires a reliable data foundation to ensure that algorithms are acting on the right information,” he said.
One challenge, not just for machine learning, but for advanced analytics in general, has been the tediousness of synchronizing data models between operational applications and data sources, and downstream data warehouses and lakes that are being used as the data catalog for ML. Ensuring reliable data requires blending and correlating profile attributes across disparate siloed sources, applications, and formats.
In the longer term, he said, enterprises will move towards self-learning enterprises, which requires making sense of the output from machine learning and advanced analytics and closing the loop between insight, action, and outcomes. “A continuous feed of reliable data, relevant insights and recommended actions generated from AI/ML provide additional value to have a closed loop where users can contribute to data reliability, and improvement of business processes and customer experiences,” he said.