페이지

2013년 11월 27일 수요일

Multi-dimensional Analytics of Individuals

When social meets psychology

In 2010 Tal Yarkoni, an academic at the university of Colorado, Boulder, suggested in an 
article that it might be possible to gauge a person's personality through their writing
by tracking the 'Big Five' personality traits - openness, conscientiousness, extraversion,
agreeableness and neuroticism. Mr Yarkoni argued that extroversion correlated with "bar', while neurotics were found to use the words "awful", "lazy" and, "depressing". But other
findings were more remarkable. Trusting types were more likely to use the word "summer", while more co-operative beings favoured the word "unusual".

A group of researchers at IBM's Almaden Research Centre in San Jose, California, had picked up on Mr. Yarkoni's idea and applied it to Twitter. The team, lead by Eben Haber, 
hope to discover the "deep psychological profiles" of tweeters. Analysing three months'
worth of data from 90m users, they argue that so far they have been able to gauge 
someone's personality reasonably well from 50 tweets, and even better from 200. 

Multi-dimensional Analytics of Individuals

Why they are doing this? Well, all of these research aim to derive indivisual psycho-profile
out of social media sources. What if we are able to augment to more advanced dimensions such as human's value, needs analysis, social behavior as base foundational elements of analytical framework. Value measurements represented by individual's intrinsic value such as self-transcend, hedonism, conservation etc., and Needs might be mapped with "curiosity",
"self-express", "harmony" etc. Social behavior may reveal each individual's bevavior such
as "morning tweeter" etc. 



By doing this, current technology may represent individual psycho-profile through the lens of these four elements - personality, needs, values and social behavior. We may bring the different insights and consequent dimension to this equation but nontheless, ultimate goal would be identify each individual's unique psychological traits.

From firm's point of view, these insights could be regarded as different lever to drive different offer target to different response. Further more, if we are able to put another insight such as "network potential" into each customer data, we may offer tailored messages as timely basis (since we know the social behavior) via preferred channel from these enhanced digital profiles of individuals.

**The postings on this site are my own and don't necessarily represent IBM's positions, strategies or opinions.

2013년 11월 11일 월요일

Big Data Workshop for Insurance Executives


Insurers as experienced risk planners

I had an opportunity to lead the interesting idea exploration workshop with South Korea leading insurance executives on Big Data and its implication to Life Insurance Industry in September.

Session kicked off by sharing the case where South Africa’s largest short-term insurance company uses predictive analytics to uncover a major insurance fraud syndicate, saving millions on fraudulent claims and resolve legitimate claims 70 times faster than before.

Like most insurers around the world, this company was losing millions of dollars paying out fraudulent claims every year. To improve its bottom line and enhance customer satisfaction, the company needed to detect and stop insurance fraud early in the claims process.

Solution gained the ability to spot fraud early with an advanced analytics solution that detects patterns in near real-time and captures data from incoming claims, assesses each claim against identified risk factors and segments claims to five risk categories, separating higher-risk cases from low-risk claims.


Results was stunning. Identified a major fraud ring less than 30 days after implementation and saved more than $2.5M in payouts to fraudulent customers, and nearly $5M in total repudiations. Further more, reduced claims processing time on low-risk claims by nearly 90%, and resolves legitimate claims 70 times faster than before.


Effective Channel Management


Next question was, how can I make my call centre more productive, while providing better customer service?

If we are able to combines data about the individual customer with each contact center agent’s specific skills, expertise and past performance to optimize the routing of calls. Current technologies and consulting capabilities designed a “matching-engine” which leverages this combination of customer insight, agent profiles and real-time analytics to provide “individual-level” decisioning and assignment of calls not available in most contact centers applications. By doing this, this company provided more personalized customer/agent interaction so sales yields increased by 29%.

Insurance Customer Journey Map

The argument was, insurers should shift their gravity to the role as experienced risk planner who may design the customer's journey map centered around customer's life event and every action customer should focused on locking in a profitable customer for life. Journey map could be explored with customer based on customer experiences and life event and examined and explored from the context of each age period such as twenties, thirties etc... and equation between expenses and residual incomes.

So this journey map possibly illustrate the future key event such as Auto purchasing, wedding, first long-term saving discussion, retirement planning, realtor, home purchasing and wealth transfer etc... through out the customer's life event.

Cause a fundamental shift in the Insurance business model

This can be realized by leveraging Big Data to model future risk development for customers based on experience of similar people. For example, non-discrete data such as video of home, audio of agent conversation may be used to complete asset inventory and propose coverage. Probably, real time predictive operational analytics to empower better business decisions regarding customer service, marketing, and infrastructure deployment.

Or group insurers assess the risk of individual insureds based on actual behaviors and score them for rating, behavior coaching, and up-sell. And not only build capital advantage through rapid compliance by predicting/planning for future regulation changes, but also process the advanced case management for clinical decision support, underwriting, and claims investigation.

These observations lead us to imply that it will cause a fundamental shift in the Insurance business model : Predict customer life stage evolution before it happens and proactively market.

** 여기에 포스팅한 내용은 개인 차원의 것이며, IBM의 공식적인 입장, 전략, 의견을 반드시 대표하는 것은 아닙니다


2013년 6월 28일 금요일

Lecture Experience

Innovation Management Class in Spring 2013

It was my personal privilege and honor to have an opportunity to teach Innovation Management topic in SungKyunKwan University as adjunct professor of MOT, during March, and June 2013 as 3 credit elective course of Management of Technology (MOT) at Engineering Graduate School.

80% of students are MOT Ph.D course students and one student from Industrial Engineering and two peoples from Electronics & Electrical Engineering.

Class was quite encouraging in a sense that most of students are expected to digest  at least two 30 to 40 pages volume of english articles or essay before the class in each week. 1 page summary, group presentation and active participation of class discussion was bonus. Given the fact that most of Ph.D students are part-time, it was quite ambitious desire that required great deal of students' endurance and efforts but I was so pleased most of them accomplished the task and enjoyed.

Course Objective is like following:

Introduction to the Innovation Management is an elective course that provides a gateway into the successful operation of Management of Technology program. This course involves the certificate of MOT course completion for graduate students. It is highly recommended to be enrolled mixed engineering, management and non-engineering students to this course. This course will provide the distinct opportunity to experience the intersection between technology and business worlds and it represents a unique opportunity for graduate students in business and engineering disciplines to work together in a highly collaborative and interactive environment. For MBA students, understanding the challenges and opportunities associated with innovation in technology companies can unlock tremendous value. For Engineering and i-School students, learning about the business side of technology will help you communicate with non-technical people critical to maximizing the impact of your ideas.


The Innovation Management course examines how companies succeed or fail to build competitive differentiation through innovation in products, processes and business models. The goal of course is to build a library of innovation pattern or framework for evaluating how different form of innovation can be applied to most of enterprise’s latent ability, so ultimately train more broad “T-shaped” business and engineering leaders. Each class meeting will consist of a lecture and a discussion based on an assigned reading or case on the topic of the week. We also explore the intriguing hidden story of people or organization that inspire the new model of innovation and also attempt to probe the different domain of innovation.

Course Schedule is like following:

Class
Lecture
Assignment B
Assignment A
Class 1
March 9th
1. Introduction


Part 1. Source of Innovation
Class 2
March 16th
2. Being a Innovation Part 1
- R 2-2 Design Thinking, HBR June 2008, Tim Brown
-Group 7
R 2-1 Ch1. Anthropologist, Ten Faces of Innovation by Thomas Kelly      -Group 1

Class 3
March 23th
3. Being a Innovation Part II
R 3-2. Malcolm Gladwell, Connecting the dots, The New Yorker, March 2003.
-           Group 1
R 3-1 Ch3. Cross-Pollinator, Ten Faces of Innovation by Thomas Kelly   - Group 2

Class 4
March 30th
4. Sources of Innovation
R 4-2. “The U.S Intelligence Community”, Enterprise 2.0 (page 29-35), HBP, McAfee 2
R 4-1. Ch 3. The Slow Hunch, Where Good Ideas Come From by Steven Johnson.  – 3
Class 5
April 6th
5. Innovative Culture
- Case 5-1. Matsushida and Japan’s changing culture 3
- R 5-2. The Paradox of Samsung’s Rise, HBR 2011 5
R 5-1 Geert Hofstede, The Cultural Relativity of organizational practices and theories   - 4
Part 2. Exploiting Innovation
Class 6
April 13th
6. Innovation in the organization context
                                          
- R 6-2. Collaboration Ch2, Opportunity and Barriers, HBP, Morten Hansen pp44-55. 4
R 6-1. Collaboratio Ch3, Spot the Four Barriers to Collaborate, HBP, Morten Hansen pp 45-66.  – 5
Class 7
April 20th
7. Path of Innovation
- R 7-2. Malcolm Gladwell, Televisonary, New Yorker May 2002
- 7
- R 7-1 Malcolm Gladwell, Ch 2. 10,000 hours rule, Outliers
  - 6

Class 8
April 27th
Review and Midterm Exam


Class 9
May 4th

9. Product Innovation-1

-R9-2. Introduction – Part One. Vision, The Lean StartUp, by Eric Ries. – 6
-R9-1. Ch1. The Path to Disaster, The Four Steps to the Epiphany, Steven Blank – 7
Class 10
May 11th
10. Product Innovation-2
-R10-2. Define – Learn, The Lean Startup by Eric Ries – 2
-R10-1. Ch2. The Path to Epiphany: The Customer Development Model, Steven Blank  -1
Class 11
May 18th
11. Innovation from Big Data
-R11-2. . Data Scientist: The Sexiest job of the 21st Century, HBR, 2012, Thomas H. Davenport & D. J. Patil. 1
-R 11-1. Big Data: The Management Revolution, Andrew McAfee & Erik Brynjolfsson,  2012, HBR 2
Part 3. Emerging Innovation Trends
Class 12
May 25th
12. Open Innovation

R 12-2. Inside P&G’s new model for Innovation, HBR, 2006, L. Houston & N. Sakkab 4
-R 12-1. Open Innovation & Strategy, California Mgmt Review, 2007, H C. & Melissa A
- 3
Class 13
June 1th
13. User Innovation
-R 13-2. Sources and Patterns of Innovation in a consumer product field, Sloan Working Paper, 2000,  Sonali Shah 3
-R13-3. Geeks in Toyland, Wired, 2006, Brendan Koerner 5
-R 13-1. Open Innovation and Organizational Boundaries, HBS Working paper, 2012, K Lakhani & M. Tushman
 - 4
Class 14
June 8st
14. Business Model Innovation
-R 14-2 The role of business model, H Chesbrough, 6
-R 14-1. Business Model Generation, Alexander Oswwalder, Biz Model Canvas
- 5
Class 15
June 15th
15. Service Innovation

-R 15-2. Open Service Innovation, H. Chesbrough
- 7
-R 15-1. Go downstream: The new profit imperative in Mgf, HBR, R Wise & P Baumgartner
- 6
Class 16 June 22th
Review and Final Report



Required Text lists were following:

 Kelley, Thomas:The Ten Faces of Innovation: IDEO’s Strategies for Defeating the Devil’s Advocate and Driving Creativity throughout Your Organization
JohnSon, Steven: Where Good Ideas Come From: The Natural History of Innovation
Steven Gary Blank: Four Steps of the Epiphany : Successful Strategies for Products that Win
Ries, Eric: The Lean Startup:
Gladwell, Malcolm: Outlier
McAffee, Andrew: Enterprise 2.0
Hansen, Morten: Collaboration: How Leaders Avoid the Traps, Create Unity, and Reap Big Results
Oswalder, Alexander: Business Model Generation

Additional Texts & Books:
Chesbrough, Henry: Open Innovation (Business Model Innovation)
Chesbrough, Henry: Open Services Innovation: Rethinking Your Business to Grow and Compete in a New Era (Business Model Innovation)
Gardner, Howard: Five Minds for the Future
Grove, Andrew: Only the Paranoid Survive (Product Innovation)
Moore, Geoffrey: Inside the Tornado (Product Innovation)

Feedback

Couple of comments from students were, he actually enjoyed and loved this course from the sense that it attempted to intersect the juncture between Technology and Arts and evaluate different form of innovation.

To me, it was also great learning from my end and more interestingly, feedback was more than I expected since this is somewhat cross-disciplinary approaches so I'm little bit worried whether students could follow the course from the outset, but it turned out as groundless worry, and they did an excellent job!
  

2013년 5월 15일 수요일

Math and Analytics at IBM Research 50+ years

IBM 회사 생활을 하면서 가끔 도도한 역사의 흐름속에  경외감을 느끼게하는 기업이라는 생각을 들게하는 순간들이 있는 것 같다. 아래에 Mathmatics 와 Analytics 부서가 출범한지 50 주년을 기념하는 세션을 IBM Watson Research Center 에서 가진 Bob Sutor 박사의 blog 를 소개한다.

Math and Analytics at IBM Research: 50+ Years


Soon after I arrived back in IBM Research last July after 13 years away in the Software Group and Corporate, I was shown a 2003 edition of the IBM Journal of Research and Development that was dedicated to the Mathematical Sciences group at 40. From that, I and others assumed that this year, 2013, was the 50th anniversary of the department.
Herman Goldstine at IBM Research
I set about lining up volunteers to organize the anniversary events for the year and sent an email to our 300 worldwide members of what is now called the Business Analytics and Mathematical Sciences strategy area. Not long afterwards, I received a note from Alan Hoffman, a former director of the department, saying that he was pretty sure that the department had been around since 1958 or 59. So our 50th Anniversary became the 50+ Anniversary. Evidently mathematicians know the theory of arithmetic but don’t always practice it correctly
The first director of the department was Herman Goldstine who joined after working on the ENIAC computer and a stint at the Institute for Advanced Study in Princeton. Goldstine is pictured in the first photo on the right at a reception at the T.J. Watson Research Center in the early 1960s. Goldstine died in 2004, but all other directors of the department are still alive.
Directors of the Mathematical Sciences Department at IBM Research
We decided that the first event of the year celebrating the (more than) half century of the department would be a reunion of the directors for a morning of panel discussions. This took place this last Wednesday, May 1, 2013.
Reunion of the directors of the Math Sciences Department at IBM Research
Photo credit: Mary Beth Miller
I started the day by giving a glimpse of what the department looks like today: the above-mentioned 300 Ph.D.s, software engineers, postdocs, and other staff distributed over the areas of optimization, analytics, visual analytics, and social business in 10 of IBM’s 12 global labs.
I then introduced our panel pictured in the photo above. From left to right we have me, Brenda Dietrich, Bill Pulleyblank, Shmuel Winograd, Roy Adler (a mathematician who was in the department during the tenures of all the other directors except me), Alan Hoffman, Dick Toupin, Hirsh Cohen, and Ralph Gomory.
Ralph Gomory, Benoit Mandelbrot, and other IBM researchers pondering a math problem
My goal for the discussion was to go back and look at some of the history and culture of the math department over the last five decades. I was hoping we would hear anecdotes and stories of what life was like, the challenges they faced, and the major successes and disappointments.
Other than a few questions I had prepared, I wasn’t sure where our conversation would go. The many researchers who joined us in the auditorium at the T. J. Watson Research Center in Yorktown Heights, NY, or via the video feed going out to the other worldwide labs would have a chance to ask questions near the end of the morning.
I’m not going to go over every question and answer but rather give you the gist of what we spoke about.
  • Ralph Gomory reminded us that the department was started in a much different time, during the Cold War. The problems they were trying to solve using the hardware and the software of the day were often related highly confidential. However, every era of the department has had its own focus, burning problems to be solved, and operational environment.
  • Hirsh Cohen got his inspiration for the mathematics he did by solving practical problems such as those related to the large mainframe-connected printers. Many people feel that mathematics shouldn’t stray too far from the concrete, but it is not that simple. This isn’t just applied mathematics, it is a way of looking for inspiration that may express itself in more theoretical ways. The panelists mentioned more than once that the original posers of business or engineering problems might not recognize the mathematics that was developed in response. (I think there is nothing wrong with theoretical mathematics with no direct connection to the physical world, but there are some areas of mathematical pursuit that I think are just silly and of marginal pure or applied interest.)
  • In response to my question about balancing business needs with the desire to advance basic science, Shmuel Winograd told me I had asked the wrong question: it was about the integration of business with basic science, not a partitioning of time or resources between them. This very much sets the tone of how you manage such a science organization in a commercial company. The successful integration of these concerns may also be why IBM Research is pretty much the sole survivor of the industrial research labs from the 1950s and 1960s.
  • There was general consensus that it is difficult to get a researcher to do science in an area that he or she fundamentally does not want to work. This was redirected to the audience members who were reminded to understand what they loved to do and then find a way to do it. (This sounded like a bit of a management challenge to me, and I suspect I’ll hear about it again.)
  • Time gives a great perspective on the quality and significance of scientific work that is just not obvious while you are the middle of it. This is one of the reasons why retrospectives such as this can be so satisfying.
Discussing the future of BAMS
Photo credit: Mary Beth Miller
After the first panel and coffee break, we came back and I started the session looking at the future of the department instead of the history. We have an internal department social network community in IBM Connections and I started by summarizing some of the suggestions people came up with about what we’ll be doing in the department in five, ten, and twenty years.
Sustainability, robotic applications of cognitive computing, and mathematical algorithms for quantum computing were all suggested. Note that his was all fun speculation, not strategy development!
Eleni Pratsini, Director of Optimization Research, and Chid Apte, Director of Analytics Research, then each discussed technical topics that could be future areas for scientific research as well as having significant business use.
After the final Q&A session, we got everyone on stage for a group photo.
BAMS group photo
Photo credit: Steve Hamm
One thing that struck me when we were doing the research through the archives was how much more of a record we have of the first decade of the department than we do of the 40+ years afterwards. In those early days, each department did a typed report of its activities which was then sent to management and archived.
With the increasing use of email and, much later, digital photos, we just don’t have easy if any access to what happened month by month. As part of this 50+ Anniversary, I’m going to organize an effort to do a better job of finding and cataloging the documents, photos, and video of the department.
This should make it easier for future celebrations of the department’s history. I suspect I’m not going to make it to the 100th anniversary, but I just might get to the 75th. For the record for those who come after me, that will be in 2034.

** 여기에 포스팅한 내용은 개인 차원의 것이며, IBM의 공식적인 입장, 전략, 의견을 반드시 대표하는 것은 아닙니다