Machine learning is changing the way organizations look at analytics. Data scientists are being recognized as a key component in organizational analytics, but management often doesn't understand their work or know how to effectively manage them.
Many businesses understand that analytics has moved beyond the data warehouse, and are pushing analysts and IT to grab and analyze data from new sources, even though they may not be ready to derive business value from it.
Open source is seen as the path to machine learning innovation, despite challenges with deployment and approachable user interfaces. For organizations using or looking to adopt machine learning techniques, moving forward may be a challenge and measuring success even trickier.
In this webcast, we will:
- Discuss how different organizations are finding success with machine learning.
- Look at how organizations are feeding the creativity of data scientists, making analytics accessible to business experts, and pushing the analytics closer to the data.
- Identify how organizations are automating analytics processes in order to free up time for new analytics, new data and new business problem domains, ultimately creating real competitive advantage.
Andrew Pease, Principal Business Solutions Manager, SAS
Andrew Pease is passionate about getting the most out of data analysis, for answering big questions, and challenging the status quo. As leader of the analytics team in the SAS Global Technology Practice, he helps organizations develop and implement analytics road maps for combining internal and external data sources, exploiting Hadoop, and using high-performance analytics to embed analytics in core business processes. Pease also helps data scientists keep abreast of the latest developments in analytics approaches.
Patrick Hall, Senior Machine Learning Scientist, SAS
Patrick Hall is a Senior Staff Scientist at SAS, where he designs new data mining and machine learning technologies. He was the 11th person worldwide to become a Cloudera certified data scientist. Hall studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University in 2012.
Ben Lorica, Chief Data Scientist, O'Reilly Media
Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media Inc. He has applied business intelligence, data mining, machine learning and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining and financial engineering. His background includes stints with an investment management company, internet startups and financial services.