Yesterday, I had attended a webinar organized by Training magazine Network (http://www.trainingmagnetwork.com) on How Big Data has transformed Learning and Talent Development. The facilitator for this session was Jeffery Berk, who is Chief Operating Officer for KnowledgeAdvisors. KnowledgeAdvisors is a human capital analytics solutions and technology firm that helps organizations measure, communicate and improve the impact of their people by better managing processes through reliable metrics.
Jeffrey works closely with clients to optimize their talent development investments through measurement and analytics tools. Jeffrey, a CPA, is also an adjunct professor of management at Loyola University and author of the book "Champions of Change: The Manager's Guide to Sustainable Process Improvement" and co-author of the book
"Human Capital Analytics: Measuring and Improving Learning and Talent Impact".
The objective of this session was to:
- Define ‘Big Data’ and its impact on business
- Provide ‘Big Data’ fundamentals for understanding and context
- Discuss ‘Big Data’ in the context of L&D and Talent Development
- Provide examples and suggestions to L&D and Talent managers to leverage ‘Big Data’
- Volume: Defines the large amounts of data. He gave a perfect example of Walmart and how they use Big Data to collect more data to fill 20 million filling cabinets of data in a day.
- Velocity: Defines the speed of collection and processing. He illustrated this concept with an example of location data on the mobile phones and how it predicts the cars in shopping malls to determine the holiday sales.
- Variety: Defines the range of data types and sources. He illustrated this component with an example of how Smartphone users user different applications such as email, messaging, music, Internet, and so on.
- Value: Defines better decision with the data. Jeff illustrated this component with an example of how Memphis police use data to determine where to patrol thus reducing crimes by 25%
- Veracity: Defines reliable and accurate data. Jeff illustrated this concept with an example of “Who wants to be Millionaire” program and how it predicts the audience to be right 91 percent of the time and the expert to be correct 65 percent of the time.