Joshua Powell
Staff Software Engineer, Broadcom
Pittsburgh, Pennsylvania, United States
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Currently working on the Mainframe Integration Portal effort at Broadcom's Mainframe Software Division in Pittsburgh, Joshua leverages his extensive background in enterprise systems to advance mission-critical data infrastructure and engineering practices with the goal of creating frictionless onboarding experiences in large-scale computing environments.
Prior to joining Broadcom, Joshua led Honeywell AI's New Product Innovation research team. At Honeywell, he expanded the division's technical capabilities in artificial intelligence, robotic, and supply chain solutions for industrial applications. In 2013, he founded geospatial data science venture Viable Industries where he architected enterprise-grade SaaS solutions while scaling the organization's DevOps, engineering, and data science capabilities.
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Mainframe Data Wrangling: Preparing Your Mainframe Data for Use in AI models
85% of AI projects fail according to Gartner - in this session we'll focus on one of the key reasons that the other 15% are successful and often profitable - high quality data. Additionally, they report that "53% of organizations [...] rated their own ability to mine and exploit data as "limited"". If data, especially high quality data, is one of the keys to success then we're in trouble.
Data engineering or data wrangling might be a new term to many but it's at the heart of the next wave of machine learning and artificial intelligence innovations. Successful enterprises that are adopting these tools understand that at the center of successful AI solutions is high quality data being collected to answer a specific, often narrow, business problem.
Whether you're new to working with raw data or a seasoned veteran, this session will inspire you to have another look at the business problem your trying to solve with AI and how increasing the data quality can have a huge impact on your results.
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