Rich Clayton, VP of Business Analytics and Big Data Product Group at Oracle, described the age of collaborative intelligence in which humans and machines work together.
Rich Clayton began his remarks at the Leadership in Big Data and Analytics Forum held on December 7 in Chicago, by stating, “Machine learning is the new big data. It isn’t just about humans putting biases into rules and rules executing on the humans’ behalf. It’s about algorithms being developed through training data that, yes, humans provide. That training data has bias in it, but machine learning has continued to evolve over the last decade,” he said.
“Machine learning is the new big data. It isn’t just about humans putting biases into rules and rules executing on the humans’ behalf.”
“We talk about deep learning, artificial intelligence, natural language generation, and all these fancy terms, but we need to look at the application of this,” he said. “I see multiple things happening simultaneously that are creating more interest in the topic—not just more data and cheaper storage but a younger workforce that’s engaged and interested, and robotics and sensors that are producing data. There are more opportunities than there were five or 10 years ago,” he said.
“I always like to give the consumer point of view. I submit that most of us use social media, and we’ve been the beneficiaries of this. Amazon makes recommendations, and Netflix and Facebook all use algorithms generated based on machines. Companies are going to be using algorithms to create personalized and specialized products.”
“I always like to give the consumer point of view. Amazon makes recommendations, and Netflix and Facebook all use algorithms generated based on machines. Companies are going to be using algorithms to create personalized and specialized products.”
Clayton continued, “What about intelligent agents or smartbots that make recommendations about what business insights you should consider based on what you’ve looked at, what location you’re in, texts you’ve received, etc.? Why don’t the analytics systems come to us rather than us having to go look for them?”
Narrative creation is another application of machine learning. “Most sports analysis these days is written by machines. I’d argue that some highly structured data—like financial data—could be synthesized by a machine faster, as a starting point, than what a financial analyst could do. There are opportunities in going voice to text to voice,” said Clayton.
“We see machine learning being applied to a variety of sales, service, and marketing activities. There are machines behind the scenes that are driven by customer service.”
Clayton gave an example of human resistance to machine learning in an airline marketing setting involving a company making offers to customers. ‘How could a machine possibly be better than me?’ is a common statement made by someone resistant to machine learning, said Clayton. “This is an experience you’ll all find as you try to put algorithms into your business and try to automate day-to-day operations and day-to-day offers. When you take the human out of it and put the machine in the middle, you can take data at rest and data in motion, mash the two, and use these to make an offer.”
‘How could a machine possibly be better than me?’ is a common statement made by someone resistant to machine learning. “This is an experience you’ll all find as you try to put algorithms into your business and try to automate day-to-day operations and day-to-day offers.”
To maximize the opportunities of machine learning, Clayton suggested:
• Learn about the notion of a data lab. “A data lab, also called a data lake, isn’t necessarily a center of excellence. Google says, ‘More data in a simple model is better than less data in a complex model.’”
• Create an ethics committee. “Algorithms have unintended consequences.” Auditing may be a good idea, and an ethics committee is an obvious source of oversight.
• Understand what you need to do to prepare your staff. “I believe that hiring more data scientists isn’t the solution. We need to ask better questions across all functions and lines of business so the data scientists can be more productive. It’s the combination of these two things.”
In conclusion, Clayton noted, “Aristotle said, ‘When people work together, it’s called collective intelligence.’ I think we’re in Collective Intelligence 2.0, in which we’ll be collaborating with machines. Machines won’t replace human judgment, but we have to figure out how to work with them and get smarter about how to apply high-capacity, high-compute capabilities rather than saying, ‘Not for us.’”
ABOUT RICH CLAYTON:
Rich Clayton is Vice President of Business Analytics Product Group and is responsible driving global adoption of Oracle’s Business Intelligence, Big Data Analytics, and Enterprise Performance Management solutions. He has a passion for helping companies transform their operations with analytics and driving change in financial management processes. Mr. Clayton is continuously researching industry trends, interacting with customers and partners, and presenting at finance forums, big data events, and analytic symposiums around the world on Oracle’s strategy and the future of analytics.
Mr. Clayton currently serves on the Board of Regents for Loras College in Dubuque, Iowa. Collaborating with faculty and administration, he founded the Loras College Center for Business Analytics and led the development of the first MBA program in the state of Iowa focusing on business analytics.
Prior to joining Oracle, he was vice president of product strategy for Hyperion, where he was responsible for go-to-market planning, market strategy, pricing, and sales enablement. Mr. Clayton has held several executive marketing positions for venture-backed cloud startups. Before joining the technology industry, Rich held various corporate finance roles and was a public auditor with McGladery in Chicago.
Mr. Clayton earned his bachelor’s degree in accounting from Loras College.