{"id":516,"date":"2009-01-08T19:21:34","date_gmt":"2009-01-09T01:21:34","guid":{"rendered":"http:\/\/hunch.net\/?p=516"},"modified":"2009-01-09T03:42:01","modified_gmt":"2009-01-09T09:42:01","slug":"predictive-analytics-world","status":"publish","type":"post","link":"https:\/\/hunch.net\/?p=516","title":{"rendered":"Predictive Analytics World"},"content":{"rendered":"

Carla Vicens and Eric Siegel<\/a> contacted me about Predictive Analytics World<\/a> in San Francisco February 18&19, which I wasn’t familiar with. A quick look at the agenda<\/a> reveals several people I know working on applications of machine learning in businesses, covering deployed applications topics. It’s interesting to see a business-focused machine learning conference, as it says that we are succeeding as a field. If you are interested in deployed applications, you might attend.<\/p>\n

Eric and I did a quick interview by email.<\/p>\n

John >
\nI’ve mostly published and participated in academic machine learning conferences like ICML, COLT, and NIPS. When I look at the set of speakers and subjects<\/a> for your conference I think “machine learning for business”. Is that your understanding of things? What I’m trying to ask is: what do you view as the primary goal for this conference?<\/p>\n

Eric >
\nYou got it. This is the business event focused on the commercial deployment of technology developed at the research conferences you named. Academics’ term, “machine learning,” is essentially synonymous with the business world’s “predictive modeling”. Predictive Analytics World focuses on business applications of this technology, such as response modeling, churn modeling, email targeting, product recommendations, insurance pricing, and credit scoring. PAW’s goal is to strengthen the business impact delivered by predictive analytics deployment, and establish new opportunities with predictive analytics. The conference delivers case studies, expertise and resources to this end.<\/p>\n

The conference program is designed to speak the language of marketing and business professionals<\/i> using or planning to use predictive analytics to solve business challenges — but for the hands-on practitioner or analytical expert focused on commercial deployment<\/i> who wishes to speak this same language, it’s an equally valuable event.<\/strong><\/p>\n

John >
\n People at academic conferences would hope that technology developed there can transfer into business use. In your experience, does this happen? And how fast or difficult is it?<\/p>\n

Eric >
\nThe best way to catalyze commercial deployment is to show the people it really works outside “the lab” – which is why PAW’s program is packed primarily with named case studies of commercial deployment. These success stories answer your question with a resounding “yes” that the core technology developed academically is indeed put to use.<\/p>\n

But predictive analytics has not yet been broadly adopted across all industries, although success stories show at least initial reach in each vertical. So, sure, as one who previously wore a researcher’s hat, commercial deployment can feel slow; having solved the hardest theoretical, mathematical or statistical problems, the rest should go smoothly, right? Not exactly. The main challenges come in ramping up the business “consumer” on the technology so they see its value, positioning the technology in a way that provides business value, and, on the integration side, in preparing corporate data for predictive modeling (that’s a doozy!) and in integrating predict scores into existing systems and processes. These things take time.<\/strong><\/p>\n

John >
\nSometimes people working in the academic world don’t have a good understanding of what the real problems are. Do you have a sense of which areas of research are underemphasized in the academic world?<\/p>\n

Eric >
\nTo reach commercial success in deploying predictive analytics for the business applications I listed above, the main challenges are on the process and non-analytical integration side, rather than core machine learning technology; its good enough. So, I don’t consider there to be glaring ommissions in the capabilities of core machine learning (I taught the machine learning graduate course at Columbia University and still consider Tom Mitchell’s textbook to be my bible). <\/p>\n

On the other hand, there are always places where “real-world” data is going to bring researchers’ attention to as-yet-unsolved problems. A perfect example is the Netflix Prize, the current leader of which (and winner of the recent Progress Prize) will be speaking at PAW-09 – see here<\/a>.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

Carla Vicens and Eric Siegel contacted me about Predictive Analytics World in San Francisco February 18&19, which I wasn’t familiar with. A quick look at the agenda reveals several people I know working on applications of machine learning in businesses, covering deployed applications topics. It’s interesting to see a business-focused machine learning conference, as it … <\/p>\n

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