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Frequently Asked Questions About
PAW & TAW NYC's Agenda
You asked for it, you got it. The central FAQs regarding Predictive Analytics World and Text Analytics World NYC's Oct 16-21 conference agenda, answered by the founding chair, Eric Siegel.
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Eric Siegel
Conference Program Chair
Predictive Analytics World
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Why should I come to Predictive Analytics World NYC?
Predictive Analytics World is the leading and largest cross-vendor conference covering commercial deployment. There's nowhere else you'll find as much content and as many leading experts.
What's new - what are the hot topics covered at PAW-NYC and TAW-NYC?
PAW's agenda covers black box trading, churn modeling, crowdsourcing, demand forecasting, ensemble models, fraud detection, healthcare, insurance applications, law enforcement, litigation, market mix modeling, mobile analytics, online marketing, risk management, social data, supply chain management, targeting direct marketing, uplift modeling (net lift), and other innovative applications that benefit organizations in new and creative ways. Click here to view the agenda at a glance.
I recently wrote a white paper on one of these topics, uplift modeling — access it here. My PAW keynote is on uplift modeling, as is this session from Fidelity.
Since predictive analytics in the financial sector is hot hot hot, we've introduced a new third track devoted to that topic. Click here to view the agenda overview.
TAW's agenda covers hot topics and advanced methods such as churn risk detection, customer service and call centers, decision support, document discovery, document filtering, financial indicators from social media, fraud detection, government applications, insurance applications, knowledge discovery, open question-answering, parallelized text analysis, risk profiling, sentiment analysis, social media applications, survey analysis, topic discovery, and voice of the customer. Click here to view the agenda at a glance.
Can I attend both the PAW and TAW conferences?
Predictive Analytics World and Text Analytics World run concurrently and are designed to work together. Cross-registration options are available.
PAW is October 16-21 — is it really six days long?
PAW's core conference program, which jams widely diverse sessions into 3 tracks, is two days: October 19-20 — alongside Text Analytics World's sessions on the same two days. The other days include 9 pre- and post-conference full-day workshops — click here for details.
Where can I get an overview of the conference agendas?
You can see the complete PAW agenda and TAW agenda at a glance. Also download the complete PAW brochure and TAW brochure.
With PAW's 3 tracks and over 40 speakers, plus TAW's sessions, how do I choose which sessions to attend?
There are a lot of aspects to guide which session to attend: the industry sector, level (expert practitioner or all-audiences), brand name leading company, business application (churn modeling, fraud detection, credit scoring, etc.), core analytical method, and data source (social data, textual data, etc.)
Industry sector or vertical is only one of many aspects to consider. So, for example, you may spend a lot of time in Track 3 — Financial Services — even if you're not in that industry; PAW and TAW accomplish loads of cross-industry sharing.
Finally, bring more people. Many delegates bring along colleagues to divide and conquer — an especially common technique across leading analytics groups. There's a discount for registering as a group — click here for details.
Who goes to Predictive Analytics World?
It's a healthy mix of expert practitioners and business leaders. Click here for the detailed breakdown of attendees.
Who is "Watson" and why is IBM Research keynoting at a non-research conference?
PAW and TAW are honored to have David Gondek, IBM Technical Lead, Watson presenting a joint keynote addres:
Watson is IBM Research's computer, which defeated the all-time TV game show Jeopardy! champions. Beyond delivering a significant triumph of machine over man — and providing the most entertaining temptation to anthropomorphize a computer ever — this is a monumental step in R&D that has demonstrated the potential of ensemble models, a hot method in predictive analytics, and has achieved a new level of machine competency working with textual data — both in processing the game show trivia questions themselves, as well as the massive stores of textual data such as Wikipedia employed by Watson as its sources of knowledge.
It was huge when IBM Research's "Deep Blue" system beat Kasparov at chess in 1997, and it was huge once again when Watson triumphed earlier this year — more huge, in fact.
How is predictive analytics different from forecasting?
Predictive analytics is something else entirely, going beyond standard forecasting by producing a predictive score for each customer or other organizational element. In contrast, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.
Predictive Analytics World events include select sessions on forecasting — such as this one on demand forecasting — since it is a closely related area, and, in some cases, predictive analytics is used as a component to build a forecast model.
I'm completely new to predictive analytics — where can I learn more?
Check out the Predictive Analytics Guide for articles, training options and resources.
What is text analytics?
Text analytics — the topic of Text Analytics World — leverages and learns from massive quantities of textual data to reveal customer intentions and sentiment, driving enterprise decisions and providing strategic insights. It is a set of analytical methods designed to operate across the written word, including call center notes, blogs, tweets, web pages, newspaper articles and much more. This kind of data is strongly distinct from that stored uniformly in database tables, and demands unique technology that integrates linguistics, grammar, math and knowledge engineering.
Read more from the general Predictive Analytics World FAQ:
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