Tag: predictive

PAWCON Bay Area March

The biggest Predictive Analytics Conference comes back to the SF Bay in March next year.

From

http://www.predictiveanalyticsworld.com/sanfrancisco/2011/

Predictive Analytics World March 2011 in San Francisco is packed with the top predictive analytics experts, practitioners, authors and business thought leaders, including keynote speakers:


Sugato Basu, Ph.D.
Senior Research Scientist
Google
Lessons Learned in Predictive Modeling 
for Ad Targeting
Eric Siegel, Ph.D.
Conference Chair
Predictive Analytics World
Five Ways Predictive Analytics
Cuts Enterprise Risk



Plus special plenary sessions from industry heavy-weights:


Andreas S. Weigend, Ph.D.
weigend.com
Former Chief Scientist, Amazon.com
The State of the Social Data Revoltion
John F. Elder, Ph.D.
CEO and Founder
Elder Research
Data Mining Lessons Learned



Predictive Analytics World focuses on concrete examples of deployed predictive analytics. Hear from the horse’s mouth precisely how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling, and what kind of business impact it delivers. Click here to view the agenda at-a-glance.

PAW SF 2011 will feature speakers with case studies from leading enterprises. such as:

PAW’s March agenda covers hot topics and advanced methods such as uplift (net lift) modeling, ensemble models, social data, search marketing, crowdsourcing, blackbox trading, fraud detection, risk management, survey analysis and otherinnovative applications that benefit organizations in new and creative ways.

Join PAW and access the best keynotes, sessions, workshops, exposition, expert panel, live demos, networking coffee breaks, reception, birds-of-a-feather lunches, brand-name enterprise leaders, and industry heavyweights in the business.

 

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Complex Event Processing- SASE Language

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Complex Event Processing (CEP- not to be confused by Circular Probability Error) is defined processing many events happening across all the layers of an organization, identifying the most meaningful events within the event cloud, analyzing their impact, and taking subsequent action in real time.

Software supporting CEP are-

Oracle http://www.oracle.com/us/technologies/soa/service-oriented-architecture-066455.html

Oracle CEP is a Java application server for the development and deployment of high-performance event driven applications. It can detect patterns in the flow of events and message payloads, often based on filtering, correlation, and aggregation across event sources, and includes industry leading temporal and ordering capabilities. It supports ultra-high throughput (1 million/sec++) and microsecond latency.

Tibco is also trying to get into this market (it claims to have a 40 % market share in the public CEP market 😉 though probably they have not measured the DoE and DoD as worthy of market share yet

– see webcast by TIBCO ‘s head here http://www.tibco.com/products/business-optimization/complex-event-processing/default.jsp

and product info here-http://www.tibco.com/products/business-optimization/complex-event-processing/businessevents/default.jsp

TIBCO is the undisputed leader in complex event processing (CEP) software with over 40 percent market share, according to a recent IDC Study.

A good explanation of how social media itself can be used as an analogy for CEP is given in this SAS Global Paper

http://support.sas.com/resources/papers/proceedings10/040-2010.pdf

You can see a report on Predictive Analytics and Data Mining  in q1 2010 also from SAS’s website  at –http://www.sas.com/news/analysts/forresterwave-predictive-analytics-dm-104388-0210.pdf

A very good explanation on architecture involved is given by SAS CTO Keith Collins here on SAS’s Knowledge Exchange site,

http://www.sas.com/knowledge-exchange/risk/four-ways-divide-conquer.html

What it is: Methods 1 through 3 look at historical data and traditional architectures with information stored in the warehouse. In this environment, it often takes months of data cleansing and preparation to get the data ready to analyze. Now, what if you want to make a decision or determine the effect of an action in real time, as a sale is made, for instance, or at a specific step in the manufacturing process. With streaming data architectures, you can look at data in the present and make immediate decisions. The larger flood of data coming from smart phones, online transactions and smart-grid houses will continue to increase the amount of data that you might want to analyze but not keep. Real-time streaming, complex event processing (CEP) and analytics will all come together here to let you decide on the fly which data is worth keeping and which data to analyze in real time and then discard.

When you use it: Radio-frequency identification (RFID) offers a good user case for this type of architecture. RFID tags provide a lot of information, but unless the state of the item changes, you don’t need to keep warehousing the data about that object every day. You only keep data when it moves through the door and out of the warehouse.

The same concept applies to a customer who does the same thing over and over. You don’t need to keep storing data for analysis on a regular pattern, but if they change that pattern, you might want to start paying attention.

Figure 4: Traditional architecture vs. streaming architecture

Figure 4: Traditional architecture vs. streaming architecture

 

In academia  here is something called SASE Language

  • A rich declarative event language
  • Formal semantics of the event language
  • Theorectical underpinnings of CEP
  • An efficient automata-based implementation

http://sase.cs.umass.edu/

and

http://avid.cs.umass.edu/sase/index.php?page=navleft_1col

Financial Services

The query below retrieves the total trading volume of Google stocks in the 4 hour period after some bad news occurred.

PATTERN SEQ(News a, Stock+ b[ ])WHERE   [symbol]    AND	a.type = 'bad'    AND	b[i].symbol = 'GOOG' WITHIN  4 hoursHAVING  b[b.LEN].volume < 80%*b[1].volumeRETURN  sum(b[ ].volume)

The next query reports a one-hour period in which the price of a stock increased from 10 to 20 and its trading volume stayed relatively stable.

PATTERN	SEQ(Stock+ a[])WHERE 	 [symbol]   AND	  a[1].price = 10   AND	  a[i].price > a[i-1].price   AND	  a[a.LEN].price = 20            WITHIN  1 hourHAVING	avg(a[].volume) ≥ a[1].volumeRETURN	a[1].symbol, a[].price

The third query detects a more complex trend: in an hour, the volume of a stock started high, but after a period of price increasing or staying relatively stable, the volume plummeted.

PATTERN SEQ(Stock+ a[], Stock b)WHERE 	 [symbol]   AND	  a[1].volume > 1000   AND	  a[i].price > avg(a[…i-1].price))   AND	  b.volume < 80% * a[a.LEN].volume           WITHIN  1 hourRETURN	a[1].symbol, a[].(price,volume), b.(price,volume)

(note from Ajay-

 

I was not really happy about the depth of resources on CEP available online- there seem to be missing bits and pieces in both open source, academic and corporate information- one reason for this is the obvious military dual use of this technology- like feeds from Satellite, Audio Scans, etc)

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Brief Interview with James G Kobielus

Here is a brief one question interview with James Kobielus, Senior Analyst, Forrester.

Ajay-Describe the five most important events in Predictive Analytics you saw in 2010 and the top three trends in 2011 as per you.

Jim-

Five most important developments in 2010:

  • Continued emergence of enterprise-grade Hadoop solutions as the core of the future cloud-based platforms for advanced analytics
  • Development of the market for analytic solution appliances that incorporate several key features for advanced analytics: massively parallel EDW appliance, in-database analytics and data management function processing, embedded statistical libraries, prebuilt logical domain models, and integrated modeling and mining tools
  • Integration of advanced analytics into core BI platforms with user-friendly, visual, wizard-driven, tools for quick, exploratory predictive modeling, forecasting, and what-if analysis by nontechnical business users
  • Convergence of predictive analytics, data mining, content analytics, and CEP in integrated tools geared  to real-time social media analytics
  • Emergence of CRM and other line-of-business applications that support continuously optimized “next-best action” business processes through embedding of predictive models, orchestration engines, business rules engines, and CEP agility

Three top trends I see in the coming year, above and beyond deepening and adoption of the above-bulleted developments:

  • All-in-memory, massively parallel analytic architectures will begin to gain a foothold in complex EDW environments in support of real-time elastic analytics
  • Further crystallization of a market for general-purpose “recommendation engines” that, operating inline to EDWs, CEP environments, and BPM platforms, enable “next-best action” approaches to emerge from today’s application siloes
  • Incorporation of social network analysis functionality into a wider range of front-office business processes to enable fine-tuned behavioral-based customer segmentation to drive CRM optimization

About –http://www.forrester.com/rb/analyst/james_kobielus

James G. Kobielus
Senior Analyst, Forrester Research

RESEARCH FOCUS

James serves Business Process & Applications professionals. He is a leading expert on data warehousing, predictive analytics, data mining, and complex event processing. In addition to his core coverage areas, James contributes to Forrester’s research in business intelligence, data integration, data quality, and master data management.

PREVIOUS WORK EXPERIENCE

James has a long history in IT research and consulting and has worked for both vendors and research firms. Most recently, he was at Current Analysis, an IT research firm, where he was a principal analyst covering topics ranging from data warehousing to data integration and the Semantic Web. Prior to that position, James was a senior technical systems analyst at Exostar (a hosted supply chain management and eBusiness hub for the aerospace and defense industry). In this capacity, James was responsible for identifying and specifying product/service requirements for federated identity, PKI, and other products. He also worked as an analyst for the Burton Group and was previously employed by LCC International, DynCorp, ADEENA, International Center for Information Technologies, and the North American Telecommunications Association. He is both well versed and experienced in product and market assessments. James is a widely published business/technology author and has spoken at many industry events

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Predictive Analytics World March2011 SF

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Message from PAWCON-

 

Predictive Analytics World, Mar 14-15 2011, San Francisco, CA

More info: pawcon.com/sanfrancisco

Agenda at-a-glance: pawcon.com/sanfrancisco/2011/agenda_overview.php

PAW’s San Francisco 2011 program is the richest and most diverse yet, including over 30 sessions across two tracks – an “All Audiences” and an “Expert/Practitioner” track — so you can witness how predictive analytics is applied at Bank of America, Bank of the West, Best Buy, CA State Automobile Association, Cerebellum Capital, Chessmetrics, Fidelity, Gaia Interactive, GE Capital, Google, HealthMedia, Hewlett Packard, ICICI Bank (India), MetLife, Monster.com, Orbitz, PayPal/eBay, Richmond, VA Police Dept, U. of Melbourne, Yahoo!, YMCA, and a major N. American telecom, plus insights from projects for Anheiser-Busch, the SSA, and Netflix.

PAW’s agenda covers hot topics and advanced methods such as uplift modeling (net lift), ensemble models, social data (6 sessions on this), search marketing, crowdsourcing, blackbox trading, fraud detection, risk management, survey analysis, and other innovative applications that benefit organizations in new and creative ways.

Predictive Analytics World is the only conference of its kind, delivering vendor-neutral sessions across verticals such as banking, financial services, e-commerce, education, government, healthcare, high technology, insurance, non-profits, publishing, social gaming, retail and telecommunications

And PAW covers the gamut of commercial applications of predictive analytics, including response modeling, customer retention with churn modeling, product recommendations, fraud detection, online marketing optimization, human resource decision-making, law enforcement, sales forecasting, and credit scoring.

WORKSHOPS. PAW also features pre- and post-conference workshops that complement the core conference program. Workshop agendas include advanced predictive modeling methods, hands-on training and enterprise decision management.

More info: pawcon.com/sanfrancisco

Agenda at-a-glance: pawcon.com/sanfrancisco/2011/agenda_overview.php

Be sure to register by Dec 7 for the Super Early Bird rate (save $400):
pawcon.com/sanfrancisco/register.php

If you’d like our informative event updates, sign up at:
pawcon.com/signup-us.php

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Quantifying Analytics ROI

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I had a brief twitter exchange with Jim Davis, Chief Marketing Officer, SAS Institute on Return of Investment on Business Analytics Projects for customers. I have interviewed Jim Davis before last year https://decisionstats.com/2009/06/05/interview-jim-davis-sas-institute/

Now Jim Davis is a big guy, and he is rushing from the launch of SAS Institute’s Social Media Analytics in Japan- to some arguably difficult flying conditions in time to be home in America for Thanksgiving. That and and I have not been much of a good Blog Boy recently, more swayed by love of open source, than love of software per se. I love equally, given I am bad at both equally.

Anyways, Jim’s contention  ( http://twitter.com/Davis_Jim ) was customers should go in business analytics only if there is Positive Return on Investment.  I am quoting him here-

What is important is that there be a positive ROI on each and every BA project. Otherwise don’t do it.

That’s not the marketing I was taught in my business school- basically it was sell, sell, sell.

However I see most BI sales vendors also go through -let me meet my sales quota for this quarter- and quantifying customer ROI is simple maths than predictive analytics but there seems to be some information assymetry in it.

Here is a paper from North Western University on ROI in IT projects-.

but overall it would be in the interest of customers and Business Analytics Vendors to publish aggregated ROI.

The opponents to this transparency in ROI would be market leaders in market share, who have trapped their customers by high migration costs (due to complexity) or contractually.

A recent study listed Oracle having a large percentage of unhappy customers who would still renew!, SAP had problems when it raised prices for licensing arbitrarily (that CEO is now CEO of HP and dodging legal notices from Oracle).

Indeed Jim Davis’s famous unsettling call for focusing on Business Analytics,as Business Intelligence is dead- that call has been implemented more aggressively by IBM in analytical acquisitions than even SAS itself which has been conservative about inorganic growth. Quantifying ROI, should theoretically aid open source software the most (since they are cheapest in up front licensing) or newer technologies like MapReduce /Hadoop (since they are quite so fast)- but I think that market has a way of factoring in these things- and customers are not as foolish neither as unaware of costs versus benefits of migration.

The contrary to this is Business Analytics and Business Intelligence are imperfect markets with duo-poly  or big players thriving in absence of customer regulation.

You get more protection as a customer of $20 bag of potato chips, than as a customer of a $200,000 software. Regulators are wary to step in to ensure ROI fairness (since most bright techies are qither working for private sector, have their own startup or invested in startups)- who in Govt understands Analytics and Intelligence strong enough to ensure vendor lock-ins are not done, and market flexibility is done. It is also a lower choice for embattled regulators to ensure ROI on enterprise software unlike the aggressiveness they have showed in retail or online software.

Who will Analyze the Analysts and who can quantify the value of quants (or penalize them for shoddy quantitative analytics)- is an interesting phenomenon we expect to see more of.

 

 

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PAWCON -This week in London

Watch out for the twitter hash news on PAWCON and the exciting agenda lined up. If your in the City- you may want to just drop in

http://www.predictiveanalyticsworld.com/london/2010/agenda.php#day1-7

Disclaimer- PAWCON has been a blog partner with Decisionstats (since the first PAWCON ). It is vendor neutral and features open source as well proprietary software, as well case studies from academia and Industry for a balanced view.

 

Little birdie told me some exciting product enhancements may be in the works including a not yet announced R plugin 😉 and the latest SAS product using embedded analytics and Dr Elder’s full day data mining workshop.

Citation-

http://www.predictiveanalyticsworld.com/london/2010/agenda.php#day1-7

Monday November 15, 2010
All conference sessions take place in Edward 5-7

8:00am-9:00am

Registration, Coffee and Danish
Room: Albert Suites

9:00am-9:50am

Keynote
Five Ways Predictive Analytics Cuts Enterprise Risk

All business is an exercise in risk management. All organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance companies do.

Predictive analytics does the trick, one customer at a time. This technology is a data-driven means to compute the risk each customer will defect, not respond to an expensive mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for a telephone solicitation that would have landed a sale, commit fraud, or become a “loss customer” such as a bad debtor or an insurance policy-holder with high claims.

In this keynote session, Dr. Eric Siegel will reveal:

  • Five ways predictive analytics evolves your enterprise to reduce risk
  • Hidden sources of risk across operational functions
  • What every business should learn from insurance companies
  • How advancements have reversed the very meaning of fraud
  • Why “man + machine” teams are greater than the sum of their parts for

    • enterprise decision support

 

Speaker: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World

[ Top of this page ] [ Agenda overview ]

IBM9:50am-10:10am

Platinum Sponsor Presentation
The Analytical Revolution

The algorithms at the heart of predictive analytics have been around for years – in some cases for decades. But now, as we see predictive analytics move to the mainstream and become a competitive necessity for organisations in all industries, the most crucial challenges are to ensure that results can be delivered to where they can make a direct impact on outcomes and business performance, and that the application of analytics can be scaled to the most demanding enterprise requirements.

This session will look at the obstacles to successfully applying analysis at the enterprise level, and how today’s approaches and technologies can enable the true “industrialisation” of predictive analytics.

Speaker: Colin Shearer, WW Industry Solutions Leader, IBM UK Ltd

[ Top of this page ] [ Agenda overview ]

Deloitte10:10am-10:20am

Gold Sponsor Presentation
How Predictive Analytics is Driving Business Value

Organisations are increasingly relying on analytics to make key business decisions. Today, technology advances and the increasing need to realise competitive advantage in the market place are driving predictive analytics from the domain of marketers and tactical one-off exercises to the point where analytics are being embedded within core business processes.

During this session, Richard will share some of the focus areas where Deloitte is driving business transformation through predictive analytics, including Workforce, Brand Equity and Reputational Risk, Customer Insight and Network Analytics.

Speaker: Richard Fayers, Senior Manager, Deloitte Analytical Insight

[ Top of this page ] [ Agenda overview ]

10:20am-10:45am

Break / Exhibits
Room: Albert Suites

10:45am-11:35am
Healthcare
Case Study: Life Line Screening
Taking CRM Global Through Predictive Analytics

While Life Line is successfully executing a US CRM roadmap, they are also beginning this same evolution abroad. They are beginning in the UK where Merkle procured data and built a response model that is pulling responses over 30% higher than competitors. This presentation will give an overview of the US CRM roadmap, and then focus on the beginning of their strategy abroad, focusing on the data procurement they could not get anywhere else but through Merkle and the successful modeling and analytics for the UK.

Speaker: Ozgur Dogan, VP, Quantitative Solutions Group, Merkle Inc.

Speaker: Trish Mathe, Life Line Screening

[ Top of this page ] [ Agenda overview ]

11:35am-12:25pm
Open Source Analytics; Healthcare
Case Study: A large health care organization
The Rise of Open Source Analytics: Lowering Costs While Improving Patient Care

Rapidminer and R were the number 1 and 2 in this years annual KDNuggets data mining tool usage poll, followed by Knime on place 4 and Weka on place 6. So what’s going on here? Are these open source tools really that good or is their popularity strongly correlated with lower acquisition costs alone? This session answers these questions based on a real world case for a large health care organization and explains the risks & benefits of using open source technology. The final part of the session explains how these tools stack up against their traditional, proprietary counterparts.

Speaker: Jos van Dongen, Associate & Principal, DeltIQ Group

[ Top of this page ] [ Agenda overview ]

12:25pm-1:25pm

Lunch / Exhibits
Room: Albert Suites

1:25pm-2:15pm
Keynote
Thought Leader:
Case Study: Yahoo! and other large on-line e-businesses
Search Marketing and Predictive Analytics: SEM, SEO and On-line Marketing Case Studies

Search Engine Marketing is a $15B industry in the U.S. growing to double that number over the next 3 years. Worldwide the SEM market was over $50B in 2010. Not only is this a fast growing area of marketing, but it is one that has significant implications for brand and direct marketing and is undergoing rapid change with emerging channels such as mobile and social. What is unique about this area of marketing is a singularly heavy dependence on analytics:

 

  • Large numbers of variables and options
  • Real-time auctions/bids and a need to adjust strategies in real-time
  • Difficult optimization problems on allocating spend across a huge number of keywords
  • Fast-changing competitive terrain and heavy competition on the obvious channels
  • Complicated interactions between various channels and a large choice of search keyword expansion possibilities
  • Profitability and ROI analysis that are complex and often challenging

 

The size of the industry, its growing importance in marketing, its upcoming role in Mobile Advertising, and its uniquely heavy reliance on analytics makes it particularly interesting as an area for predictive analytics applications. In this session, not only will hear about some of the latest strategies and techniques to optimize search, you will hear case studies that illustrate the important role of analytics from industry practitioners.

Speaker: Usama Fayyad, , Ph.D., CEO, Open Insights

[ Top of this page ] [ Agenda overview ]

SAS2:15pm-2:35pm

Platinum Sponsor Presentation
Creating a Model Factory Using in-Database Analytics

With the ever-increasing number of analytical models required to make fact-based decisions, as well as increasing audit compliance regulations, it is more important than ever that these models can be created, monitored, retuned and deployed as quickly and automatically as possible. This paper, using a case study from a major financial organisation, will show how organisations can build a model factory efficiently using the latest SAS technology that utilizes the power of in-database processing.

Speaker: John Spooner, Analytics Specialist, SAS (UK)

[ Top of this page ] [ Agenda overview ]

2:35pm-2:45pm

Session Break
Room: Albert Suites

2:45pm-3:35pm

Retail
Case Study: SABMiller
Predictive Analytics & Global Marketing Strategy

Over the last few years SABMiller plc, the second largest brewing company in the world operating in 70 countries, has been systematically segmenting its markets in different countries globally in order optimize their portfolio strategy & align it to their long term country specific growth strategy. This presentation talks about the overall methodology followed and the challenges that had to be overcome both from a technical as well as from a change management stand point in order to successfully implement a standard analytics approach to diverse markets and diverse business positions in a highly global setting.

The session explains how country specific growth strategies were converted to objective variables and consumption occasion segments were created that differentiated the market effectively by their growth potential. In addition to this the presentation will also provide a discussion on issues like:

  • The dilemmas of static vs. dynamic solutions and standardization vs. adaptable solutions
  • Challenges in acceptability, local capability development, overcoming implementation inertia, cost effectiveness, etc
  • The role that business partners at SAB and analytics service partners at AbsolutData together play in providing impactful and actionable solutions

 

Speaker: Anne Stephens, SABMiller plc

Speaker: Titir Pal, AbsolutData

[ Top of this page ] [ Agenda overview ]

3:35pm-4:25pm

Retail
Case Study: Overtoom Belgium
Increasing Marketing Relevance Through Personalized Targeting

 

Since many years, Overtoom Belgium – a leading B2B retailer and division of the French Manutan group – focuses on an extensive use of CRM. In this presentation, we demonstrate how Overtoom has integrated Predictive Analytics to optimize customer relationships. In this process, they employ analytics to develop answers to the key question: “which product should we offer to which customer via which channel”. We show how Overtoom gained a 10% revenue increase by replacing the existing segmentation scheme with accurate predictive response models. Additionally, we illustrate how Overtoom succeeds to deliver more relevant communications by offering personalized promotional content to every single customer, and how these personalized offers positively impact Overtoom’s conversion rates.

Speaker: Dr. Geert Verstraeten, Python Predictions

[ Top of this page ] [ Agenda overview ]

4:25pm-4:50pm

Break / Exhibits
Room: Albert Suites

4:50pm-5:40pm
Uplift Modelling:
Case Study: Lloyds TSB General Insurance & US Bank
Uplift Modelling: You Should Not Only Measure But Model Incremental Response

Most marketing analysts understand that measuring the impact of a marketing campaign requires a valid control group so that uplift (incremental response) can be reported. However, it is much less widely understood that the targeting models used almost everywhere do not attempt to optimize that incremental measure. That requires an uplift model.

This session will explain why a switch to uplift modelling is needed, illustrate what can and does go wrong when they are not used and the hugely positive impact they can have when used effectively. It will also discuss a range of approaches to building and assessing uplift models, from simple basic adjustments to existing modelling processes through to full-blown uplift modelling.

The talk will use Lloyds TSB General Insurance & US Bank as a case study and also illustrate real-world results from other companies and sectors.

 

Speaker: Nicholas Radcliffe, Founder and Director, Stochastic Solutions

[ Top of this page ] [ Agenda overview ]

5:40pm-6:30pm

Consumer services
Case Study: Canadian Automobile Association and other B2C examples
The Diminishing Marginal Returns of Variable Creation in Predictive Analytics Solutions

 

Variable Creation is the key to success in any predictive analytics exercise. Many different approaches are adopted during this process, yet there are diminishing marginal returns as the number of variables increase. Our organization conducted a case study on four existing clients to explore this so-called diminishing impact of variable creation on predictive analytics solutions. Existing predictive analytics solutions were built using our traditional variable creation process. Yet, presuming that we could exponentially increase the number of variables, we wanted to determine if this added significant benefit to the existing solution.

Speaker: Richard Boire, BoireFillerGroup

[ Top of this page ] [ Agenda overview ]

6:30pm-7:30pm

Reception / Exhibits
Room: Albert Suites

Tuesday November 16, 2010
All conference sessions take place in Edward 5-7

8:00am-9:00am

Registration, Coffee and Danish
Room: Albert Suites

9:00am-9:55am
Keynote
Multiple Case Studies: Anheuser-Busch, Disney, HP, HSBC, Pfizer, and others
The High ROI of Data Mining for Innovative Organizations

Data mining and advanced analytics can enhance your bottom line in three basic ways, by 1) streamlining a process, 2) eliminating the bad, or 3) highlighting the good. In rare situations, a fourth way – creating something new – is possible. But modern organizations are so effective at their core tasks that data mining usually results in an iterative, rather than transformative, improvement. Still, the impact can be dramatic.

Dr. Elder will share the story (problem, solution, and effect) of nine projects conducted over the last decade for some of America’s most innovative agencies and corporations:

    Streamline:

  • Cross-selling for HSBC
  • Image recognition for Anheuser-Busch
  • Biometric identification for Lumidigm (for Disney)
  • Optimal decisioning for Peregrine Systems (now part of Hewlett-Packard)
  • Quick decisions for the Social Security Administration
    Eliminate Bad:

  • Tax fraud detection for the IRS
  • Warranty Fraud detection for Hewlett-Packard
    Highlight Good:

  • Sector trading for WestWind Foundation
  • Drug efficacy discovery for Pharmacia & UpJohn (now Pfizer)

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: John Elder, Ph.D., Elder Research, Inc.

Also see Dr. Elder’s full-day workshop

 

[ Top of this page ] [ Agenda overview ]

9:55am-10:30am

Break / Exhibits
Room: Albert Suites

10:30am-11:20am
Telecommunications
Case Study: Leading Telecommunications Operator
Predictive Analytics and Efficient Fact-based Marketing

The presentation describes what are the major topics and issues when you introduce predictive analytics and how to build a Fact-Based marketing environment. The introduced tools and methodologies proved to be highly efficient in terms of improving the overall direct marketing activity and customer contact operations for the involved companies. Generally, the introduced approaches have great potential for organizations with large customer bases like Mobile Operators, Internet Giants, Media Companies, or Retail Chains.

Main Introduced Solutions:-Automated Serial Production of Predictive Models for Campaign Targeting-Automated Campaign Measurements and Tracking Solutions-Precise Product Added Value Evaluation.

Speaker: Tamer Keshi, Ph.D., Long-term contractor, T-Mobile

Speaker: Beata Kovacs, International Head of CRM Solutions, Deutsche Telekom

[ Top of this page ] [ Agenda overview ]

11:20am-11:25am

Session Changeover

11:25am-12:15pm
Thought Leader
Nine Laws of Data Mining

Data mining is the predictive core of predictive analytics, a business process that finds useful patterns in data through the use of business knowledge. The industry standard CRISP-DM methodology describes the process, but does not explain why the process takes the form that it does. I present nine “laws of data mining”, useful maxims for data miners, with explanations that reveal the reasons behind the surface properties of the data mining process. The nine laws have implications for predictive analytics applications: how and why it works so well, which ambitions could succeed, and which must fail.

 

Speaker: Tom Khabaza, khabaza.com

 

[ Top of this page ] [ Agenda overview ]

12:15pm-1:30pm

Lunch / Exhibits
Room: Albert Suites

1:30pm-2:25pm
Expert Panel: Kaboom! Predictive Analytics Hits the Mainstream

Predictive analytics has taken off, across industry sectors and across applications in marketing, fraud detection, credit scoring and beyond. Where exactly are we in the process of crossing the chasm toward pervasive deployment, and how can we ensure progress keeps up the pace and stays on target?

This expert panel will address:

  • How much of predictive analytics’ potential has been fully realized?
  • Where are the outstanding opportunities with greatest potential?
  • What are the greatest challenges faced by the industry in achieving wide scale adoption?
  • How are these challenges best overcome?

 

Panelist: John Elder, Ph.D., Elder Research, Inc.

Panelist: Colin Shearer, WW Industry Solutions Leader, IBM UK Ltd

Panelist: Udo Sglavo, Global Analytic Solutions Manager, SAS

Panel moderator: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World

2:25pm-2:30pm

Session Changeover

2:30pm-3:20pm
Crowdsourcing Data Mining
Case Study: University of Melbourne, Chessmetrics
Prediction Competitions: Far More Than Just a Bit of Fun

Data modelling competitions allow companies and researchers to post a problem and have it scrutinised by the world’s best data scientists. There are an infinite number of techniques that can be applied to any modelling task but it is impossible to know at the outset which will be most effective. By exposing the problem to a wide audience, competitions are a cost effective way to reach the frontier of what is possible from a given dataset. The power of competitions is neatly illustrated by the results of a recent bioinformatics competition hosted by Kaggle. It required participants to pick markers in HIV’s genetic sequence that coincide with changes in the severity of infection. Within a week and a half, the best entry had already outdone the best methods in the scientific literature. This presentation will cover how competitions typically work, some case studies and the types of business modelling challenges that the Kaggle platform can address.

Speaker: Anthony Goldbloom, Kaggle Pty Ltd

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3:20pm-3:50pm

Breaks /Exhibits
Room: Albert Suites

3:50pm-4:40pm
Human Resources; e-Commerce
Case Study: Naukri.com, Jeevansathi.com
Increasing Marketing ROI and Efficiency of Candidate-Search with Predictive Analytics
InfoEdge, India’s largest and most profitable online firm with a bouquet of internet properties has been Google’s biggest customer in India. Our team used predictive modeling to double our profits across multiple fronts. For Naukri.com, India’s number 1 job portal, predictive models target jobseekers most relevant to the recruiter. Analytical insights provided a deeper understanding of recruiter behaviour and informed a redesign of this product’s recruiter search functionality. This session will describe how we did it, and also reveal how Jeevansathi.com, India’s 2nd-largest matrimony portal, targets the acquisition of consumers in the market for marriage.

 

Speaker: Suvomoy Sarkar, Chief Analytics Officer, HT Media & Info Edge India (parent company of the two companies above)

 

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4:40pm-5:00pm
Closing Remarks

Speaker: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World

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Wednesday November 17, 2010

Full-day Workshop
The Best and the Worst of Predictive Analytics:
Predictive Modeling Methods and Common Data Mining Mistakes
Click here for the detailed workshop description

  • Workshop starts at 9:00am
  • First AM Break from 10:00 – 10:15
  • Second AM Break from 11:15 – 11:30
  • Lunch from 12:30 – 1:15pm
  • First PM Break: 2:00 – 2:15
  • Second PM Break: 3:15 – 3:30
  • Workshop ends at 4:30pm

Speaker: John Elder, Ph.D., CEO and Founder, Elder Research, Inc.

 

Interview James Dixon Pentaho

Here is an interview with James Dixon the founder of Pentaho, self confessed Chief Geek and CTO. Pentaho has been growing very rapidly and it makes open source Business Intelligence solutions- basically the biggest chunk of enterprise software market currently.

Ajay-  How would you describe Pentaho as a BI product for someone who is completely used to traditional BI vendors (read non open source). Do the Oracle lawsuits over Java bother you from a business perspective?

James-

Pentaho has a full suite of BI software:

* ETL: Pentaho Data Integration

* Reporting: Pentaho Reporting for desktop and web-based reporting

* OLAP: Mondrian ROLAP engine, and Analyzer or Jpivot for web-based OLAP client

* Dashboards: CDF and Dashboard Designer

* Predictive Analytics: Weka

* Server: Pentaho BI Server, handles web-access, security, scheduling, sharing, report bursting etc

We have all of the standard BI functionality.

The Oracle/Java issue does not bother me much. There are a lot of software companies dependent on Java. If Oracle abandons Java a lot resources will suddenly focus on OpenJDK. It would be good for OpenJDK and might be the best thing for Java in the long term.

Ajay-  What parts of Pentaho’s technology do you personally like the best as having an advantage over other similar proprietary packages.

Describe the latest Pentaho for Hadoop offering and Hadoop/HIVE ‘s advantage over say Map Reduce and SQL.

James- The coolest thing is that everything is pluggable:

* ETL: New data transformation steps can be added. New orchestration controls (job entries) can be added. New perspectives can be added to the design UI. New data sources and destinations can be added.

* Reporting: New content types and report objects can be added. New data sources can be added.

* BI Server: Every factory, engine, and layer can be extended or swapped out via configuration. BI components can be added. New visualizations can be added.

This means it is very easy for Pentaho, partners, customers, and community member to extend our software to do new things.

In addition every engine and component can be fully embedded into a desktop or web-based application. I made a youtube video about our philosophy: http://www.youtube.com/watch?v=uMyR-In5nKE

Our Hadoop offerings allow ETL developers to work in a familiar graphical design environment, instead of having to code MapReduce jobs in Java or Python.

90% of the Hadoop use cases we hear about are transformation/reporting/analysis of structured/semi-structured data, so an ETL tool is perfect for these situations.

Using Pentaho Data Integration reduces implementation and maintenance costs significantly. The fact that our ETL engine is Java and is embeddable means that we can deploy the engine to the Hadoop data nodes and transform the data within the nodes.

Ajay-  Do you think the combination of recession, outsourcing,cost cutting, and unemployment are a suitable environment for companies to cut technology costs by going out of their usual vendor lists and try open source for a change /test projects.

Jamie- Absolutely. Pentaho grew (downloads, installations, revenue) throughout the recession. We are on target to do 250% of what we did last year, while the established vendors are flat in terms of new license revenue.

Ajay-  How would you compare the user interface of reports using Pentaho versus other reporting software. Please feel free to be as specific.

James- We have all of the everyday, standard reporting features covered.

Over the years the old tools, like Crystal Reports, have become bloated and complicated.

We don’t aim to have 100% of their features, because we’d end us just as complicated.

The 80:20 rule applies here. 80% of the time people only use 20% of their features.

We aim for 80% feature parity, which should cover 95-99% of typical use cases.

Ajay-  Could you describe the Pentaho integration with R as well as your relationship with Weka. Jaspersoft already has a partnership with Revolution Analytics for RevoDeployR (R on a web server)-

Any  R plans for Pentaho as well?

James- The feature set of R and Weka overlap to a small extent – both of them include basic statistical functions. Weka is focused on predictive models and machine learning, whereas R is focused on a full suite of statistical models. The creator and main Weka developer is a Pentaho employee. We have integrated R into our ETL tool. (makes me happy 🙂 )

(probably not a good time to ask if SAS integration is done as well for a big chunk of legacy base SAS/ WPS users)

About-

As “Chief Geek” (CTO) at Pentaho, James Dixon is responsible for Pentaho’s architecture and technology roadmap. James has over 15 years of professional experience in software architecture, development and systems consulting. Prior to Pentaho, James held key technical roles at AppSource Corporation (acquired by Arbor Software which later merged into Hyperion Solutions) and Keyola (acquired by Lawson Software). Earlier in his career, James was a technology consultant working with large and small firms to deliver the benefits of innovative technology in real-world environments.

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