Machine learning: What you really need to know

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Procter & Gamble’s Principal Statistician Bill Myers

Machine learning, artificial intelligence and other buzzwords are thrown around a lot, but what do these terms mean other than methods to solve problems? While many see them as automated problem-solving methods (and there is a place for automation), these methods are no substitute for clarifying the problem at hand and thinking critically about the questions you hope to answer. So says Procter & Gamble’s Principal Statistician, Bill Myers, in his plenary for Southeast Asia’s first Statistically Speaking.

Bill’s extensive experience in realizing value from data is evident in the wisdom and examples he shares. He provides context for machine learning in the analysis process from data to value. He even speaks about the synergies machine learning methods can have with other analysis methods, including design of experiments. And he models the roles that mentoring and collaboration play. He was a critical component of Procter & Gamble’s participation in collaborative research with Georgia Institute of Technology, which won the 2020 SPAIG Award (Statistical Partnerships in Academe, Industry, and Government).

Miao Chen, Data Scientist at TEL Singapore

Following Bill’s plenary, Miao Chen, Data Scientist at TEL Singapore, provides great examples of where automation can speed extracting value from data, furthering my assertion that there is a place for automation. From more easily sourcing data, freeing up resources, and strategically using JMP scripting to automate analyses, these examples show how automation can facilitate the data-to-value process.

Both speakers understand how to leverage analytics to build and maintain competitive advantage. The livestream attracted many viewers from many time zones, but the on-demand version is always available at your convenience.