DataOps is a modern engineering practice that can improve the speed and accuracy of analytics. In a pre-conference workshop at Data Summit 2019, Mark Marinelli, head of product at Tamr, identified the processes, technologies, and roles involved in DataOps to improve the speed and availability of data for analytics—as well as the common pitfalls to avoid.
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Machine learning (ML) is on the rise at businesses hungry for greater automation and intelligence with use cases spreading across industries. At the same time, most projects are still in the early phases. From selecting data sets and data platforms to architecting and optimizing data pipelines, there are many success factors to keep in mind.
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Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends, may be in demand, but it requires an understanding of many different elements of data analysis. Extracting actionable knowledge from all your data to make decisions and predictions requires a number of skills, from statistics and programming to data visualization and business domain expertise.
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At Data Summit 2019, Susan E. Feldman, president, Synthexis, and David Bayer, executive director, Cognitive Computing Consortium, offered an overview of key cognitive computing considerations in a workshop titled "Cognitive Computing 101."
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