A Dare for Analytics Bloggers in 2011

A new challenge for R , SAS and all techie bloggers-

http://en.blog.wordpress.com/2010/12/30/challenge-for-2011-want-to-blog-more-often/

As part of the DailyPost, we’re launching two campaigns:

  • Post a Day 2011: Post something to your blog every single day through 2011
  • Post a Week 2011: Post to your blog at least once a week through 2011

Signing up is simple – do the following:

  1. Post on your blog, right now, that you’re participating
  2. (You can grab a sample post from dailypost.wordpress.com)
  3. Use the tag postaday2011 or postaweek2011 in your posts (tips on tagging here)
  4. Go to dailypost.wordpress.com
  5. Subscribe to dailypost.wordpress.com– you’ll get reminders and inspirations every day to help you bring your full potential to your WordPress blog!

 

Do you write a blog or own a website?

Well how about taking up this challenge?

Who Dares-    Wins

Game On!!!

Ways to use both Windows and Linux together

Tux, as originally drawn by Larry Ewing
Image via Wikipedia

Some programming ways to use both Windows and Linux

1) Wubi

http://wubi.sourceforge.net/

Wubi only adds an extra option to boot into Ubuntu. Wubi does not require you to modify the partitions of your PC, or to use a different bootloader, and does not install special drivers.

2) Wine

Wine lets you run Windows software on other operating systems. With Wine, you can install and run these applications just like you would in Windows. Read more at http://wiki.winehq.org/Debunking_Wine_Myths

http://www.winehq.org/about/

3) Cygwin

http://www.cygwin.com/

Cygwin is a Linux-like environment for Windows. It consists of two parts:

  • A DLL (cygwin1.dll) which acts as a Linux API emulation layer providing substantial Linux API functionality.
  • A collection of tools which provide Linux look and feel
  • What Isn’t Cygwin?

  • Cygwin is not a way to run native linux apps on Windows. You have to rebuild your application from source if you want it to run on Windows.
  • Cygwin is not a way to magically make native Windows apps aware of UNIX ® functionality, like signals, ptys, etc. Again, you need to build your apps from source if you want to take advantage of Cygwin functionality.
  • 4) Vmplayer

    https://www.vmware.com/products/player/

    VMware Player is the easiest way to run multiple operating systems at the same time on your PC. With its user-friendly interface, VMware Player makes it effortless for anyone to try out Windows 7, Chrome OS or the latest Linux releases, or create isolated virtual machines to safely test new software and surf the Web

    Choosing R for business – What to consider?

    A composite of the GNU logo and the OSI logo, ...
    Image via Wikipedia

    Additional features in R over other analytical packages-

    1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

    2) Wide literature of training material in the form of books is available for the R analytical platform.

    3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

    4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

    5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.

     

    6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

    Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

    Specific to the following domains R has the following costs and benefits

    • Business Analytics
      • R is free per license and for download
      • It is one of the few analytical platforms that work on Mac OS
      • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
      • It has open source code for customization as per GPL
      • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
      • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
      • Huge library of packages for regression, time series, finance and modeling
      • High quality data visualization packages
      • Data Mining
        • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
        • Flexibility in tweaking a standard algorithm by seeing the source code
        • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
        • Business Dashboards and Reporting
        • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
          • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
          • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

    Additional factors to consider in your R installation-

    There are some more choices awaiting you now-
    1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

    2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

    3) Operating system sub choice- 32- bit or 64 bit.

    4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

    5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

    6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

    7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

    1) Licensing Choices-
    You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

    Commercial Vendors of R Language Products-
    1) Revolution Analytics http://www.revolutionanalytics.com/
    2) XL Solutions- http://www.experience-rplus.com/
    3) Information Builder – Webfocus RStat -Rattle GUI http://www.informationbuilders.com/products/webfocus/PredictiveModeling.html
    4) Blue Reference- Inference for R http://inferenceforr.com/default.aspx

    1. Choosing Operating System
        1. Windows

     

    Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

          1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
        1. MacOS

     

    Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

        1. Linux

     

          1. Ubuntu
          2. Red Hat Enterprise Linux
          3. Other versions of Linux

     

    Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

    Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

        1. Multiple operating systems-
          1. Virtualization vs Dual Boot-

     

    You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

    1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.

     

    1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

    Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
    Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

      1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
        1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
        2. Workstation
      2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
        1. Amazon
        2. Google
        3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
      3. How much resources
        1. RAM-Hard Disk-Processors- for workstation computing
        2. Instances or API calls for cloud computing
    1. Interface Choices
      1. Command Line
      2. GUI
      3. Web Interfaces
    2. Software Component Choices
      1. R dependencies
      2. Packages to install
      3. Recommended Packages
    3. Additional software choices
      1. Additional legacy software
      2. Optimizing your R based computing
      3. Code Editors
        1. Code Analyzers
        2. Libraries to speed up R

    citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

    (Note- this is a draft in progress)

    Sugar CRM: Forrester Webinar

    Analytické CRM
    Image via Wikipedia

    https://sugarcrmevents.webex.com/mw0306lb/mywebex/default.do?nomenu=true&siteurl=sugarcrmevents&service=6&main_url=https://sugarcrmevents.webex.com/ec0605lb/eventcenter/event/eventAction.do%3FtheAction%3Ddetail%26confViewID%3D279191911%26siteurl%3Dsugarcrmevents%26%26%26

    Date and time:

    Thursday, December 2, 2010 11:00 am 
    Pacific Standard Time (San Francisco, GMT-08:00) 
    Change time zone

    Thursday, December 2, 2010 2:00 pm 
    Eastern Standard Time (New York, GMT-05:00)
    Thursday, December 2, 2010 7:00 pm
    Western European Time (London, GMT)
    Thursday, December 2, 2010 8:00 pm
    Europe Time (Berlin, GMT+01:00)
    Duration: 1 hour
    Description:
    Every organization wants to improve the way they manage their customer relationships. But until recently, adding robust CRM tools to your organization was a time consuming and cost prohibitive endeavor for many resources-constrained organizations. Until Now. On December 2 join us to learn how new developments in technology like open source, cloud computing and web 2.0 – are making it easier than ever to add a top notch CRM system to your operations. 

     

    This live webinar hosted by SugarCRM will feature Forrester Research, Inc. Vice President William Band, named one CRM Magazine’s 2007 Influential Leaders. Mr. Band will discuss the current state of the market, review the major trends affecting the CRM landscape, and discuss some criteria you can use to ensure your next CRM decision is the right one.

    In addition, all attendees of the live webinar will receive a complimentary download a recent Forrester Wave™ Report! Register today!

    Speakers:

    William Band, Vice President, Forrester Research
    Martin Schneider, Sr. Director Communications, SugarCRM

    Who Should Attend:
    VP Sales, VP Marketing, CIO’s, Head of Customer Support and other technical decision makers

    Brief Interview Timo Elliott

    Here is a brief interview with Timo Elliott.Timo Elliott is a 19-year veteran of SAP Business Objects.

    Ajay- What are the top 5 events in Business Integration and Data Visualization services you saw in 2010 and what are the top three trends you see in these in 2011.


    Timo-

    Top five events in 2010:

    (1) Back to strong market growth. IT spending plummeted last year (BI continued to grow, but more slowly than previous years). This year, organizations reopened their wallets and funded new analytics initiatives — all the signs indicate that BI market growth will be double that of 2009.

    (2) The launch of the iPad. Mobile BI has been around for years, but the iPad opened the floodgates of organizations taking a serious look at mobile analytics — and the easy-to-use, executive-friendly iPad dashboards have considerably raised the profile of analytics projects inside organizations.

    (3) Data warehousing got exciting again. Decades of incremental improvements (column databases, massively parallel processing, appliances, in-memory processing…) all came together with robust commercial offers that challenged existing data storage and calculation methods. And new “NoSQL” approaches, designed for the new problems of massive amounts of less-structured web data, started moving into the mainstream.

    (4) The end of Google Wave, the start of social BI.Google Wave was launched as a rethink of how we could bring together email, instant messaging, and social networks. While Google decided to close down the technology this year, it has left its mark, notably by influencing the future of “social BI”, with several major vendors bringing out commercial products this year.

    (5) The start of the big BI merge. While several small independent BI vendors reported strong growth, the major trend of the year was consolidation and integration: the BI megavendors (SAP, Oracle, IBM, Microsoft) increased their market share (sometimes by acquiring smaller vendors, e.g. IBM/SPSS and SAP/Sybase) and integrated analytics with their existing products, blurring the line between BI and other technology areas.

    Top three trends next year:

    (1) Analytics, reinvented. New DW techniques make it possible to do sub-second, interactive analytics directly against row-level operational data. Now BI processes and interfaces need to be rethought and redesigned to make best use of this — notably by blurring the distinctions between the “design” and “consumption” phases of BI.

    (2) Corporate and personal BI come together. The ability to mix corporate and personal data for quick, pragmatic analysis is a common business need. The typical solution to the problem — extracting and combining the data into a local data store (either Excel or a departmental data mart) — pleases users, but introduces duplication and extra costs and makes a mockery of information governance. 2011 will see the rise of systems that let individuals and departments load their data into personal spaces in the corporate environment, allowing pragmatic analytic flexibility without compromising security and governance.

    (3) The next generation of business applications. Where are the business applications designed to support what people really do all day, such as implementing this year’s strategy, launching new products, or acquiring another company? 2011 will see the first prototypes of people-focused, flexible, information-centric, and collaborative applications, bringing together the best of business intelligence, “enterprise 2.0”, and existing operational applications.

    And one that should happen, but probably won’t:

    (4) Intelligence = Information + PEOPLE. Successful analytics isn’t about technology — it’s about people, process, and culture. The biggest trend in 2011 should be organizations spending the majority of their efforts on user adoption rather than technical implementation.                 About- http://timoelliott.com/blog/about

    Timo Elliott is a 19-year veteran of SAP BusinessObjects, and has spent the last twenty years working with customers around the world on information strategy.

    He works closely with SAP research and innovation centers around the world to evangelize new technology prototypes.

    His popular Business Analytics and SAPWeb20 blogs track innovation in analytics and social media, including topics such as augmented corporate reality, collaborative decision-making, and social network analysis.

    His PowerPoint Twitter Tools lets presenters see and react to tweets in real time, embedded directly within their slides.

    A popular and engaging speaker, Elliott presents regularly to IT and business audiences at international conferences, on subjects such as why BI projects fail and what to do about it, and the intersection of BI and enterprise 2.0.

    Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England. He blogs on http://timoelliott.com/blog/ (one of the best designed blogs in BI) . You can see more about him personal web site here and photo/sketch blog here. You should follow Timo at http://twitter.com/timoelliott

    Art Credit- Timo Elliott

    Related Articles

    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

    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

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    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

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    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

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    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

     

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    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

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    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

     

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    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.