Paper Title: Unlocking the Value of Unstructured Data with Machine Learning

Paper Abstract: Every year, the amount of data that businesses collect, store, analyze, and manage increases substantially. Digital transformation has resulted in most organizations using software in the form of automated platforms and applications to track customer accounts, sales, production processes, employee productivity, customer satisfaction and feedback, financials, and so on. All of that data is extremely valuable, especially when it's analyzed together by ML systems to root out the hidden correlations. Most of that information is unstructured data, which can consist of information from all of those digital platforms and applications, as well as from sensors, telemetry systems, social media accounts, and more. To get maximum value out of ML systems, it's critical that organizations find a way to integrate all of that unstructured data into a unified platform. In this technical session, Pure Storage will discuss how organizations can unlock the value of unstructured data through machine learning, and the role that storage infrastructure plays.

Paper Author: Miroslav Klivansky, Global Practice Leader Analytics/AI, Pure Storage

Author Bio: Miroslav Klivansky is a Principal Field Solutions Architect at Pure Storage. He has extensive experience in storage systems, workload modeling, system architecture, tuning, and benchmarking. Miroslav loves both learning and teaching, and has translated those passions into helping organizations get value from innovative technologies.