Tom H. C. Anderson - Next Gen Market Research™Groupon Case Study Hbs Groupon A Case Study In How Not To Do Ipo Groupon Ipo Case Study Marketing Case Study Groupon Groupon Merchant Case Study Case Study On Groupon Groupon Case Study Pdf Groupon Restaurant Case Study Groupon Success Case Study Groupon Uk Case Study Graphic Design Business Case Study Graphic Design Ethics Case Study Environmental Graphic Design Case Study Case Study For Graphic Design Case Study In Graphic Design Case Study On Graphic Designs Graphic Design Portfolio Case Study Graphic Design Research Case Study Graphic Design Studio Case Study Sustainable Graphic Design Case Study How To Write A Case Study Graphic Design What Is A Case Study Graphic Design Mgt 448 Case Study Google In China Ethics Case Study Google Inc. In China Google In China Case Study Answers Google In China Case Study 2010 Google In China Case Study Essays Google In China A Case Study Google In China Case Study Business And Society Google In China Case Study International Business Case Study Google In China The Big Disconnect Google In China Case Study Ethics Google In China Harvard Case Study Google In China Hbs Case Study Google In China Case Study Ivey Case Study On Google Problem In China Summary Of Google In China Case Study Google In China Case Study Problem Google In China Case Study Ppt Google In China Case Study Pdf Google In China Case Study Summary Google In China Case Study Solution Case Study Going To The Dogs Case Study House 22 Case Study House 22-l.a. 1960 Case Study House 22 Architect Case Study House 22 For Sale Case Study House 22 Stahl House Case Study House #22 Los Angeles 1960 Shulman Portfolio #01 Case Study House #22 Case Study House #22 Los Angeles Photos Case Study House #22 By Pierre Koenig Case Study House 22 Cost Case Study House #22 (the Stahl House) Los Angeles Calif Case Study House 22 Cad Case Study House 22 Drawings Case Study House 22 Elevation Case Study House 22 Google Earth Case Study House 22 History Case Study House #22 Aka Stahl House Pierre Koenig Stahl House Case Study House 22 Case Study House 22 In Movies Case Study House 22 In Film Julius Shulman Case Study House 22 Print Julius Shulman Photography Case Study House 22 1959- Case Study House #22 - Pierre Koening - Architect Pierre Koenig Case Study House 22 Tour Case Study House No 22 Stahl House By Pierre Koenig Case Study House 22 Location Case Study House 22 La Case Study House #22 The Stahl House Los Angeles Case Study House 22 Materials Case Study House Number 22 Stahl House Case Study House No. 22 Stahl House Case Study House No. 22 By Pierre Koenig Pierre Koenigs Case Study House No 22 Stahl House Case Study House 22 Pierre Koenig Case Study House 22 Plans Case Study House 22 Pictures Case Study House 22 Photography Case Study House 22 Pdf Richard Neutra Case Study House 22 Case Study House 22 Photo Sharing Case Study House #22 Photo Julius Shulman Case Study House 22 Tour Case Study House #22 Tickets Case Study House #22 In The Movies The Case Study House 22 Case Study House 22 Visit Visit To Case Study House 22 Los Angeles Case Study House 21 Koenig Case Study House 21 Drawings Case Study House 21 Dimensions Case Study House 21 Bailey House Julius Shulman Case Study House 21 Case Study House 21 Los Angeles Bailey House Case Study House No. 21 Case Study House N 21 Case Study House 21 Pierre Koenig Pierre Koenig Case Study House 21 Address Case Study House 21 Planos Case Study House 21 Pdf Pierre Koenig Case Study House 21 Case Study House 21 Sketchup Case Study Houses Book Depository Case Study Houses Book Online The Case Study Houses Book Case Study House 8 Plans Case Study House 8 Built In 1949 Case Study House 8 Eames Eames Case Study House 8 Elevation He Eames Case Study House No 8 In Pacific Palisades Case Study House 8 Eames House Eames Case Study House No.8 In Pacific Palisades He Eames Case Study House No. 8 Case Study House Numero 8 He Eames Case Study House No. 8 In Pacific Palisades Ca Case Study House 8 Charles Ray Eames The Eames Case Study House No. 8 In Pacific Palisades Ca Case Study House 8 Visit Case Study How To Write Case Study How To Solve Case Study How To Avoid Complacency Case Study How To Present Case Study How To Motivate Employees Case Study How To Make Case Study 101 Case Study 1 The Complete Accounting Cycle Case Study 18 The Bruised Boy Case Study 15 Case Study 12 Celiac Disease Case Study 100 Case Study 22 Case Study 26 Tyco International Case Study 2011 Case Study 2004 Tsunami Case Study 2 Springfield Express Case Study 3 Case Study 3 On Cash Budgeting Case Study 3.1 Hy Dairies Inc Case Study 31 Lymphoma Case Study 3pl Case Study 30 Metabolic Stress And Trauma Case Study 39 Movie Case Study 4 A South African Investment Case Study 4 The Unsuspecting Honeymooners Case Study 4.2 Dispatches From The War On Stress Case Study 4 Bgp Case Study 4.1 Marsha Warren Case Study 4.4 A New Work Ethic Case Study 5.1 Hamilton High Case Study 5 General Electric Prices Case Study 6.1 The Regency Grand Hotel Case Study 6 Love Canal Case Study 7 Process Cast In Stone Case Study 8 Case Study 8.1 Amanda Jackson Case Study 8051 Microcontroller Case Study 8.2 Philanthropic Team Building Case Study 8d Problem Solving Case Study 9 Case Study 9 Spinal Cord Injury Case Study 9 House Case Study 9 Thl Case Study 9 Gastroesophageal Reflux Disease Case Study 9 Eames Case Study 9 Pulmonary Thromboembolism Case Study 04 Banyan Tree Case Study How To Apply Data Mining Techniques Case Study How To Answer Case Study How To Analyse Case Study How To Analyze Case Study How To Do A Business Process Improvement Case Study How To Beat A Bigger Rival Case Study How To Start A Business Case Study How To Plan For An Sap Bpc Implementation Case Study How Voip Was Beneficial To An Organization Case Study Barriers To Communication Case Study Books Case Study Bipolar Disorder Case Study Based Group Discussion Case Study Business Ethics Case Study Borderline Personality Disorder Case Study How To Conduct Case Study How To Create Case Study Coffee Case Study Conclusion Case Study Change Management Case Study Copd Case Study Customer Service Case Study How Kristin Died Case Study Definition Psychology Case Study Dissertation Case Study Database Case Study Depression Case Study For Mba Case Study For Mba Students Case Study Turks To Germany Case Study Gourmet To Go Case Study Snacks To Go Case Study Group Discussion Case Study Gd Case Study Road To Hell Case Study House 21 Case Study How To Do It Case Study Introduction How To Write How To Interpret Case Study How To Introduce Case Study How To Case Study Interview How To Improve Case Study How To Identify Case Study How To Solve Case Study In Mba Case Study Japan Tsunami 2011 Case Study Japan Earthquake 2011 Case Study Journal Article Case Study John Woodbury Case Study Kfc Case Study Kingfisher Airlines Case Study Kobe Earthquake Case Study On How To Launch A New Product Case Study How Long Case Study How Low Will You Go Case Study Related To Leadership Case Study Link Case Study Related To Management Case Study Mexico To Usa Migration Case Study Methodology Case Study Method Of Research Case Study Method Ppt Case Study New To The Touch Case Study Nike Case Study Nestle Case Study Nokia Case Study Netflix Case Study Negotiation Case Study How To Do Case Study On Marketing Case Study On Leadership Case Study On Motivation Case Study On Management Case Study On Communication Case Study On Child Labour Case Study On Depression Case Study How To Prepare Case Study Poland To Uk Migration Case Study Psychology Case Study Ppt Case Study Pdf Case Study Project Management Case Study Approach To Research Case Study Related To Hr Case Study Related To Finance Case Study Related To Marketing Case Study Related To Motivation Case Study Related To Strategic Management Case Study Synonym Case Study Starbucks Case Study Strategic Management Case Study To Theory Case Study Talk To Chuck Case Study How To Use Case Study Mexico To Usa Case Study Poland To Uk Case Study Rural To Urban Migration Case Study How A Ups Manager Cut Turnover Case Study Unit Of Analysis Case Study Unix Case Study Unix Operating System Case Study Video Case Study Vs Experiment Case Study Vs Survey Case Study Validity Case Study Vs Ethnography Case Study Vs Naturalistic Observation Case Study Volcanic Eruption Medical Case Study How To Write Management Case Study How To Write Marketing Case Study How To Write Case Study How Why Case Study Steps To Write Case Study When To Use Case Study X85 And X86 Machines Case Study Xenotransplantation Case Study X85 Case Study Xml Case Study Xian-janssen Pharmaceutical And The Euro Case Study X Ray Case Study Xbox Case Study Xenophobia Case Study Youtube Case Study Yin 2003 Case Study Youth Crime Yellowstone Super Volcano Case Study Case Study Zynga Case Study Zhou Bicycle Company Case Study House Tour Case Study House Tours Los Angeles Case Study House Tours California Eames Case Study House Tours Case Study House 8 Tour

More Than Market Research - Gain The Information Advantage

Tom H. C. Anderson - Next Gen Market Research™ header image 6

KDnuggets Data Mining Guru Gregory Piatetsky-Shapiro and Tom H. C. Anderson talk about Data Mining, Text Mining, Web 2.0, and Market Research

March 29th, 2008 · 6 Comments

KDnuggets Data Mining Guru Gregory Piatetsky-Shapiro and Tom H. C. Anderson
Business Guru Round Table Discussion #3 
(Discussion #1, #2)

Anderson Analytics KDnuggets 

Dr. Gregory Piatetsky-Shapiro
KDNuggets - Data Mining Guru

If you’ve been working with data mining, knowledge discovery, bioinformatics, or business analytics, for the past few years then you are also likely to be familiar with Gregory. The word Data Mining and the name Gregory Piatetsky-Shapiro are inextricably linked. Before staring KDnuggets he led data mining and consulting groups at GTE Laboratories, Knowledge Stream Partners, and Xchange. He has extensive experience developing CRM, customer attrition, cross-sell, segmentation and other models for some of the leading banks, insurance companies, and telcos. He has also worked on clinical trial, microarray, and proteomic data analysis for several leading biotech and pharmaceutical companies. He has served as an expert witness and provided expert opinions in several cases. He serves as the current Chair of ACM SIGKDD, the leading professional organization for Knowledge Discovery and Data Mining. He is also the founder of Knowledge Discovery in Database (KDD) conferences, having organized and chaired the first three Knowledge Discovery in Databases workshops in 1989, 1991, and 1993.

Gregory has over 60 publications, including 2 best-selling books and several edited collections on topics related to data mining and knowledge discovery, including SIGKDD Explorations Special Issue on Microarray Data Mining (Vol 5, Issue 2, Dec 2003). Gregory received ACM SIGKDD Service Award (2000) and IEEE ICDM Outstanding Service Award for contributions to data mining field and community. He is a true Data Mining Guru.

Gregory’s work has had an indelible influence on the scientific approach of Anderson Analytics. His noted influence came during founder Tom H. C. Anderson’s graduate school years, and later, during the development of Anderson Analytics, was a source of advice and scientific guidance. After meeting for the first time in person last year at the text analytics summit in Boston, Tom and Gregory have kept in touch. Tom’s most recent conversation with Gregory is presented as the third installment of the Anderson Analytics Guru Round-Table discussion. In their conversation, Tom and Gregory discuss the importance of data mining, including how it is unfortunately foreign to most market researchers.

On Data Mining

Tom H. C. Anderson: Gregory, how did you get into data mining?

Gregory Piatetsky-Shapiro: Since I was a child I’ve always been interested in AI [artificial intelligence]. There weren’t really any PhD programs specifically in data mining back in the early 80’s, so I got my M.S. and Ph.D. from NYU in Computer Science and my dissertation was on Self-Organizing Database Systems.

When I worked at GTE Labs in 1980-s, I was very interested in applying AI to databases, and data mining was a natural application. There were no meetings on data mining, so I decided to organize a workshop myself in 1989. I started Knowledge Discovery Nuggets newsletter in 1993 as a way for researchers in the area to stay connected and share ideas.When the first web browser Mosaic came along it seemed only natural to start a website (1994).

When I left GTE in 1997, I moved the Knowledge Discovery Mine website to .

In retrospect I should have chosen a name easier to spell than KDnuggets.

Tom H. C. Anderson: I think there’s nothing wrong with the branding there, it’s quite a memorable name I would think. How, about advice for those just starting to learn about data mining? Are there any tips you can give? Perhaps books you could recommend that you like?

Gregory Piatetsky-Shapiro: There are so many, it depends on whether you are interested in Data Mining /Knowledge Discovery technical aspects or in business aspects. One good less technical one is Michael Berry’s and Gordon Linoff’s “Mastering Data Mining”. I believe I have this one on my site. It’s a very good non technical introduction.

Tom H. C. Anderson: How about websites or blogs?

Gregory Piatetsky-Shapiro: I like Avinash Kaushilk’s blog ( and also “Juice analytics“. I enjoyed the Freakanomics book and read their blog as well. A good list of data mining related blogs is on

Tom H. C. Anderson: Gregory, is Data Mining an art, a science, or both?

Gregory Piatetsky-Shapiro: It has to be both. Otherwise someone will find a correlation between the S&P 500 index and the price of butter in Bangladesh. I’m not kidding; I’ve seen some crazy things. There has to be an art applied.

Tom H. C. Anderson: What do you feel is the current state of data mining, knowledge discovery, bioinformatics, and business analytics in companies today?

Gregory Piatetsky-Shapiro: Companies are certainly aware of data mining/KDD, but most companies are not making effective use of the data collected. They are good at collecting it. And also good at correcting the collected data. But they are not so good at analyzing it or applying these insights to the business.

Tom H. C. Anderson: Yes, that is what we have found as well. There is certainly a lot of data collection going on. It seems that’s where the focus is. There are exceptions of course, like Amazon for instance?

Gregory Piatetsky-Shapiro: Amazon definitely makes a good use of their customer data - their “customer who have seen X also bought Y” recommendations are very effective and from what I hear contribute a lot to their bottom line.

Speaking of the broader data mining and analytics industry, I have a unique indicator which is the number of job postings on KDnuggets . In 1999-2000 it was growing strongly, then dropped sharply after the dom com bubble burst in 2001, and then started growing again in 2004. 2007 was the best year so far,
I’m seeing a slowdown this quarter, but part of that is the business cycle. I’m very optimistic about the long term outlook for analytics.

Trends, CGM & SNS ~ Link & Text Analysis

Tom H. C. Anderson: What important trends, if any, have you seen emerging during the last year which will be important in 2008 and in the near future?

Gregory Piatetsky-Shapiro: Myspace, Facebook, Linkedin and other SNs are causing this growth. There is a lot technology that can be used to analyze links, but we must be careful with privacy and allow users to opt-in explicitly to the potential analytics. Facebook recent Beacon advertising program was a notable example of violating privacy and caused a big backlash.

Then of course there are video/movie and image databases, though these will take some time, but a lot of work is being done now.

Tom H. C. Anderson: Other than Link Analysis, it seems Text Analytics/Text mining seems to be one of our best hopes for understanding SNS, would you agree?

Gregory Piatetsky-Shapiro: Link Analysis can be valuable even without the text. For instance analyzing phone records, you can learn a lot by seeing who calls who. But yes, text analytics is also a very important trend and complimentary to link analysis.

Tom H. C. Anderson: I ask this of myself sometimes. How will SNS such as Linkedin and Facebook change the world?

Gregory Piatetsky-Shapiro: I think the great part is that these companies make a structure that already exists explicit and amenable to analysis. Some things like twitter perhaps seem a bit strange to our generation, but to younger generations it’s no stranger than driving and drinking coffee. So this next generation may totally accept a computer mediated network.

Tom H. C. Anderson: I like that, taking of something that tacit and making it explicit. Yes, I think we are already seeing a change in some of the work we do with GenX2Z, especially among younger girls; it may well change the future of how we network.

Outsourcing/Off-Shoring & KPO

Tom H. C. Anderson: I’ve been hearing more and more about KPO or Knowledge Process Outsourcing/Off-shoring from others in the field of market research as well as data mining, it seems to be a trend which has been growing as more and more companies seem to be trying to leverage skilled cheap labor, what are your thoughts on this if any in terms of data mining? Do you feel it will continue to grow?

Gregory Piatetsky-Shapiro: Clearly it will continue to grow; it’s like an economic force of gravity which drives outsourcing to Mumbai and similar areas. The US should develop skills that are harder to duplicate. Business skills are harder to offshore. Good Analytics are a combination of these skills.


Tom H. C. Anderson: In terms of Customer Segmentation 1-to1 Marketing has long been a buzz word in our industry. Do you feel this is still the direction we’re heading in (1-to-1 segmentation), or is this something that has been abandoned for more actionable, broader segmentation strategies?

Gregory Piatetsky-Shapiro: Segmentation sometimes can become even more specific than 1-to-1 because there may be multiple people on an account and each person may have different personas.
For example, there may be one Netflix account for a family, but can we figure out if the movie rating reflects the opinion of the husband, the wife, the children, or the dog?
Segmentation must be actionable; one must find the best way to segment.

Who is Leveraging Analytics for an Information Advantage?

Tom H. C. Anderson: In your experience what companies are leveraging analytics better/more?

Gregory Piatetsky-Shapiro:…To see which companies are leveraging analytics more as I mentioned before, you just have to look at who is recruiting online for what. Insurance companies, banks, Pharma (Microsoft, Travelers, etc.).

Tom H. C. Anderson: And how about by department (Market Research, Business Intelligence, Competitive Intelligence, Strategy etc.) who is making more/better use of KD/DM techniques currently? For Marketing, do you think CMO’s have a good enough understanding of these analytic techniques?

Gregory Piatetsky-Shapiro: In regard to CMO’s, I suspect they are aware of the buzz words. But probably not aware of potential. While analytics isn’t the ‘Golden Bullet’, it is very important. I see relatively few analytics/data mining job postings within the marketing department.

[Post to Twitter] 

Tags: Academia · Analytics · Anderson Analytics · Business Guru · CMO · Gregory Piatetsky-Shapiro · Interview · KDNuggets · Marketing · Marketing Guru · Methods · Networking · Off-Shoring · SNS · Segmentation · Strategy · Text Analytics · Tom H. C. Anderson

6 responses so far ↓

  • 1 gapInsedasids // Jan 13, 2009 at 11:49 am

    There are 5 houses in five different colors
    In each house lives a different nationality.
    These 5 owners drink a certain beverage, smoke a certain brand of cigar and keep a certain pet.
    No owners have the same pet, smoke the same brand of cigar, or drink the same beverage.

    The CLUES:

    The Brit lives in the Red house.
    The Swede keeps dogs as pets.
    The Dane Drinks tea.
    The Green House is on the left of the White House.
    The Green House’s owner drinks coffee.
    The person who smokes Pall Mall rears birds.
    The owner of the yellow house smokes Dunhill.
    The man in the center house drinks milk.
    The Norwegian lives in the first house.
    The man who smokes Blends lives next to the one who keeps cats
    The man who keeps horses lives next to the man who smokes Dunhill.
    The man who smokes Blue Master drinks beer.
    The German smokes Prince.
    The Norwegian lives next to the Blue House.
    The man who smokes Blends has a neighbor who drinks water.

    Who owns the fish?

  • 2 Tom H C Anderson // Jan 13, 2009 at 6:29 pm

    Thanks, that was fun. We took turns solving for it ;)

  • 3 Mark O'Hazo // Nov 6, 2009 at 8:39 pm

    The colors/nations are the pivot.

    The answer is the German owns the fish

  • 4 Educación 2.0 para formadores // Nov 20, 2009 at 9:15 am

    [...] [...]

  • 5 The Top Blog in Market Research! // Mar 29, 2011 at 9:35 am

    [...] inspired any other researchers to think about less traditional market research techniques such as data mining or text analytics, and to take social media more [...]

  • 6 Text Analytics News Interviews // Sep 7, 2011 at 2:55 pm

    [...] interview with myself and three analytics professionals who many of you may know (KDnugget’s Gregory Piatetsky-Shapiro, Social Media Today’s Cliff Figallo, and Vincent Granville of AnalyticBridge). We were asked [...]

Leave a Comment