Proliferation of IoT and its implication

Proliferation of Internet of Things - what does this really mean to us?

It was my pleasure and honor to present "IoT & its Business Development" to one of Korea major Chaebol's CEO Breakfast session in May. I felt awe when I saw the number of standing signboard at the entrance. It was 195th forum. Wow, 195 consecutive breakfast meeting with more than 50 CEOs together? Impressive.

Anyway, I kicked off by saying some examples unveiled during CES and MWC such as connected crockpot, toothbrush(?), Belkin's Wemo switch, wash machine, car etc and then, explained the traits of IoT communication protocol. I explained MQTT as one of publish-subscribe based messaging protocol example for use of IoT. MQTT is light-weight protocol to fit into Internet of Things that is designed for remote location and characterized as, small code foot print, limited network bandwidth, high velocity, low battery consumption and "do not get "lost" in the event of a system failure" etc,.

And then illustrated some use cases such as connected car, condition based maintenance, smart city demo - Geo-spatial streaming data based integrated operation control and connected troops etc.

Merging of physical and digital world

As 1 trillion devices in the world are expected to start dialogue with each other, or 2 billion internet of peoples, it naturally lead us to imply the next question, " OK, what does this really mean to us?". Although some of survey unveiled the fact that explosion of data is the area where most of C-level executives were under-prepared, paradoxically it also implies the opportunity that we can extract the insights from these data explosions.

Way to plug our hands into the digital world

One symbolic example of merging physical and digital world is Myo from Thalmic Labs based in Waterloo, Canada. The technology that makes Myo possible — electromyography (EMG), which doctors and scientists use to record electrical activity from muscles —  is something Thalmic Labs have been experimenting. Co-founder Lake and Matt Bailey worked on a wearable device that could help blind people navigate using sensors. 

Connected Thermostat as 'thing' of a smart home.

Another example is certainly Nest learning thermostat and protect smoke detector.
As of today, the Jawbone UP24 band will have a setting that turns on the Nest thermostat when it senses its wearer has woken up from a night’s sleep. Mercedes-Benz’s cars will be able to tell Nest when a driver is expected home, so it can set the temperature ahead of time. Smart lights made by LIFX can also be programmed to flash red when the Nest Protect detects smoke, or randomly turn off and on to make it look like someone is home when Nest’s thermostat is in “away” mode.




Reinforce the firm's competitiveness within incumbent business boundary.

As world is getting more instrumented, interconnected and intelligent, firm needs to think about the way to gain competitive advantage from existing business areas.
First, instrumented means, data is existed as form of digital as we observed from above Myo or Nest case. Physical human motion and temperature can be converted into digital data through instrumented.  We should examine whether we can obtain the digital data from major touch points, and if yes, then map of collection.

Second,  interconnect the existing internal data first, and then beyond internal structured data, such as external unstructured data, social, streaming etc, so need to define the area to interconnect. Why? Because these efforts would eventually help the firm to understand 360 degree of customer profiling so understand the preference.

Third, intelligent. Ultimate goal of 'instrumented' and 'interconnected' is for intelligence. We need to study many use cases to learn how optimized data analysis facilitate the supreme services so eventually gain firm's competitive advantages. For example, as soon as you recognize the customer's event at any touch point (Nest recognize car is approaching near to house - in this case), you can analyze customer's preference from internal or external big data, so you can activate the very personalized recommended next best action (Pop up cool bear Ad at Next screen) to his/her instruments (in this case Nest or perhaps car navigator?).

Make new market with IoT

Inspire new customer experience engagement model:
With digital delivery capabilities, firm can internalize and enhance the customer experiences of every touch points through pre-designed system of engagement model. And then, firm may seeking the new business from adjacent area. In case of Amazon, once they conquered the book area then moved to adjacent spaces, such as CDs, groceries. In this case, it is essential to understand the emerging new technologies such as Big Data, Social, Mobile, Cloud and IoT.

Examine the quest where innovation capability meets opportunity:
Diagnose overall organizational capability to the point where data can be augmented as major resources. Explore the area where firm can advance to new business model innovation from the combination of firm's core competencies and real-time predictable insights.

Epilogue

Explosion of data incurred by Internet of Thing is, not a threat. But this statement is probably only true to the firm who understand the implication of data explosion. People only see this as an opportunity when they can extract the insights from these data explosions. In order to this, overall organizational notion such as "data is firm's strategical major resources" is required. Also organizational obsession on customer experience is essential part of ingredient.

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

Internet of Things (IoT) presentation to CIO executives meeting

IoT and CES 2014

I presented “Foresee the future of IT through the lens of CES 2014” to the one of executive session at Plaza Hotel, Seoul. The object was to share my perspective on future trends from the observation of CES 2014. It naturally progressed to the topic around IoT.

CES 2014 is famous consumer electronic show and it is somewhat meaningful because this exhibition has usually taken placed during early January so it is regarded as precursor to anticipate IT trend that will shape the future.

I kicked off by saying some noticeable solutions from the show. LG showed the washing machine that is connected to smart phone to chat, and MakerBot for 3D printer. But I highlighted the Belkin Wemo Switch.

When IFTTT meets Belkin Switch.

IFTTT stands for “If This Then That”. It is a service that enables users to connect different web applications (e.g., Facebook, Evernote, Weather, Dropbox, etc.) together through simple conditional statements known as "Recipes". For example, If ‘sunset’ then “turn on the living room ramp”. This logic can be applied to web applications. But Belkin augmented this way into HW switch. The WeMo Switch can be plugged into any home outlet, which can then be controlled from an iOS or Android smartphone running the WeMo App, via home WiFi or mobile phone network. So in other words, you can control Wemo physical switch by Wemo App which can be applied IFTTT logic, so you can simply turn on or off the switch by clicking the button on the SmartPhone.

Facebook Messenger

Then I introduced Facebook Messenger. Well, interestingly enough, very next day of my presentation, FB acquired WhatsApp and paid 19B$ - 2^nd biggest tech acquisition of all time. Anyway, When Facebook faced the situation that its application should be continued on the Smart Phone devices, they considered the performance the most. FB acquired group messaging company named Beluga in March 2011 and introduced Facebook Messenger in August same year. It has known that Beluga was based on MQTT communication protocol.

Why Facebook chose MQTT for its messenger?

Message Queue Telemetry Transport (MQTT) was originally developed from Hursley IBM Lab for the purpose of Telemetry communication protocol to monitor and gauge the telemetry object. Characteristics of telemetry is low power & bandwidth consumption, very limited memory, and server capability, very long lifespan etc…, interestingly this is similar traits of Smart Phone. Given the consideration of HTTP’s communication through polling, MQTT’s heartbeat is originated from the requirements of low bandwidth and CPU usage (publish/subscribe protocol is under 5% CPU usage) in telemetry. MQTT communication protocol is conducted by publishing and subscribing. Subscribers nominate which types of information they want to receive by subscribing to specific topics. From publisher’s point of view, they publish in the event and forget. Rest part of communication governed by so called “message broker”. Message broker take care of session establishment and deliver so when the telemetry communication disconnected, heartbeat just checking the availability of counterpart, if disconnected, nothing happen, and once connection has established, broker resend the signal. So simply put, MQTT is lightweight, guaranteed service quality, based on publish/subscribe message protocol. MQTT is Open Standard base. OASIS (Advanced Open Standards for Information Society) adopted MQTT which stemmed from sub project of Eclipse Open Source Integrated Development Environment. MQTT boasts 93X more throughput than HTTP, 12 times less power consumption in the event of publishing, 120 times less power consumption in subscribing and 8 times less bandwidth workload than HTTP protocol.  MQTT boasts 50 µs app to app network speed where as HTTP takes few second level.

MQTT’s Publish and Subscribe protocol

In the WebSphere MQ Publish/Subscribe model the only thing which connects publishing and subscribing applications is the topic or subject which the publisher associates with his information. Publishers and subscribers need only agree on the topic to become connected to one another. Each different piece of information has its own topic associated with it. Subscribers nominate which types of information they want to receive by subscribing to specific topics.

Publishers of information are unaware of subscribers to the extent that they may publish information even if there are no subscribing applications requiring it. Publishing and subscribing are completely dynamic processes. New subscribers and new publishers can be added to the system without disruption.

With respect to a given topic, or piece of information, all possible combinations of publishers/subscribers are possible, that is; information about each topic may be provided by a single or multiple publishing applications the information may be received and processed by one or more subscribing applications.

The number of publishers and subscribers connected by a single topic depends upon the type of information which is flowing between them. As we will see later, WebSphere MQ supports both state and event based information, or topics.

Substantial numbers of use cases are possible in IoT

I shared “connected car” example as first popular Internet of Things use case. By connecting the Infotainment, Telemetics and Smart Phone, car owner unlock the car and find car when he/she lost, and when vibration in left front detected, sends data to car company who schedules appointment with dealer and sends invite.  Another use case is, condition based maintenance. Think about the petroleum pipeline network such as 17,000 km pipeline network.., how can we monitor the breaches or abnormal situations? By using 30,000 pieces low power, low bandwidth battery based M2M sensors in storage, temperature, pressure, refineries, spill location and pumps, operation center can detect, predict and execute based on condition based maintenance.

M2M Smart City demo in Barcelona Mobile World Congress.

Impressive demo was “connected car” demo in MWC this year. Solution that were comprised of IBM Infosphere Stream (SW that can process the unstructured stream data almost real-time), worklight and m2m message appliance “MessageSight”, underscored the importance of real-time monitor and control of road traffic and emergency control.  If I make long story to short, we could control the messages among the vehicle running in the Barcelona road from the control center. For example, demo displayed the automobiles in Barcelona city and let’s suppose one hydrant has broken and certain road was flooded. In this case, control center can define the zone and published alarm signal to specific zone, then the cars who have equipped with MQTT agent could subscribe that message and send messages to the adjacent cars, so ultimately all the moving cars in the zone could sense this alarm. And one car damaged and this would publish the fatal signal so emergence rescue team has arrived within short period of time….

Implication: OK, then what all of these imply to us?

Report articulates that 18B$ IoT market is estimated by 2020 and 30B IoT devices in 2015. OK good, then what this IoT proliferation to be meant to all of us?  

Proliferation of IoT devices and huge amount of market opportunity around IoT is somewhat threats and at the time, opportunities to us. We may think about the two approaches; How and What.

From the point of ‘how’, it is calling for new operation model. Firstly, it requires new capability.. for example, capability to take care of the consistent relationship around every touch points in omni-channels and deliver enhanced digital customer experiences. Second, capability across total organization is needed to expand among different channels and processes, and at the same time, it is also true to optimize every element’s capabilities. Third, it requires also integrate every element of value deliveries and optimize total organization centered on client touch points and effectiveness/efficiency point of view.

From the point of ‘what, it is calling for new customer experience which could be essentially achieved through enhanced digital contents…, for example, firstly, enhance customer experience improvements through digital contents of product & services, information, insights and relationship that could be captured across omni-channel touch points. Second, expand physical product and services to digital contents for new revenue growth. Third, redefine the system towards customer value oriented digital front office system so ultimately deliver customer’s differentiated unique value through digital channels.

Epilogue

Proliferation of IoT devices and consequent abundant data information lead us to imply the causality of businesses. This internet of things proliferation itself uncover the different control equations, it means we are literally exposed by exploded data information surrounded us and it requires new operation model which embraces consolidated view of information collected from every touch points of our clients relationship. Data is data but firm need to reorient its approaches more customer value orientation. If we are able to figure out the individual clients’ profile and persona for example, we may able to more proactively offer the value proposition, or perhaps predict the next best action based on our understanding of 360 degree client’s profile which have acquired from every touch points of engagements.

So it naturally leads us to conclude that understanding the customer is important, particularly true under exploding information era. Customer journey on product and services consumption could be also collected from proliferated M2M or IoT information. It means if we do the right job in maintaining customer lifetime value through digital front office transformation, it promises customer’s lifetime value to us. Because customer advocacy and intimacy stems from experience lock-in could be a critical weapon to win in the market place. 

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

In-Store Experience Technology

IBM's new technology "Presence Zones" will help retailers transform in-store experience

Forbes online Jan 9th edition covers the intriguing story on In-Store Experience management from IBM. Below is the summary of article.

IBM will showcase a new technology that merges in-store, online, and mobile shopping into a single customer experience.  The technology helps retailers give shoppers a 360 degree experience that connects every touch point to deliver smarter, more personalized promotions.

What are the "Presence Zones"?

Presence Zones transform the in-store experience by using intelligent location-based sensors that help retailers engage shoppers with real-time promotions as they move through the store.  The goal is to integrate the physical and digital experience and create a seamless journey that benefits both the customer and the retailer.  As the customer moves through the store, IBM’s new technology allows the retailer to extend relevant and timely offers based on their time spent browsing different aisles and products.

Possible example of this story

As an example consider a new mom shopping specifically in the newborn clothing section at a major retail chain or department store. The retailer could craft specific messages ? whether it’s a promotion, an advertisement, or education ? tailored to the new mom’s needs. They could provide contextual information regarding the top pieces of clothing every newborn needs, or provide a coupon to drive repeat visits as the child grows.  As the mom moves to the grocery area, that message could then be tailored in real-time to focus on a newborn’s dietary needs, again based on the knowledge that the woman is a new mom.

Taking that example one step further, let’s say the father is about to leave his favorite home goods store after comparison shopping on his mobile device for a new baby bed (something that happens more frequently according to IBM, which saw mobile traffic soar by 40 percent over the recent holiday season). Assuming he has opted to participate, Presence Zones knows both the aisle and products he’s been browsing based on his physical location.  It also knows that he participates in the rewards program and immediately delivers a 15 percent discount to his smartphone, winning the in-store sale while rewarding his brand loyalty.

How is customer privacy handled?

This was one of the most important issues when the technology was first developed in IBM labs. It is important to know that the entire experience begins and ends with the customer’s decision to participate in the service. Every customer has to opt in, and further, IBM doesn’t receive, store, or share any personal data, which is considered proprietary between the retailer and the customer.   The customer can opt-in or opt-out of the service in a few ways, including through the retailer’s mobile app or website. Alternatively, they can just shut off the WiFi signal on their mobile device (simple way ha?).

Preference that matters

Using the above examples, it outlined a few important goals during the shoppers’ journey.  One is ensuring the customer is willing to participate.  Without consent, no personal data is exchanged and the customer goes about their day.  Another is the ability to reward loyalty by delivering a tailored and connected shopping experience through a mutual exchange of information.  The retailer understands more about the customer, and in turn, the customer receives a much more personalized and targeted promotion.  In addition, and enabled by that deeper understanding of in-store traffic, the retailer is able to direct sales associates for better face-to-face service or optimize the physical location of new in-store promotions.

Through significant analysis, IBM has learned that people prefer very different levels of engagement. Some might want notifications, others might want email, and others might prefer in-depth and engaging discussions with a particular retailer. IBM’s analytics enable retailers to help discover customer preferences so that they can approach customers in a customized and appropriate manner. Likewise, IBM’s technology enables the retailer to integrate different facets of the customer’s behavior to better predict their engagement preferences. In the long-run, this should lead to a better customer experience that translates into greater loyalty.

Epilogue

It is generally known that great deal of RFIDs in the store is not easy job to control. But this technology utilizes the retailer’s WiFi network in concert with the customer’s mobile device to deliver the location-based service.  Once a customer opts in to participate, their location and patterns of travel are tracked in the store with targeted real-time information – such as a new promotion or more detailed product information – delivered at various points based on the products they are browsing.

Utilizing information from social or public data source always entails privacy issues. This story suggests fundamental but important prescription - customer's decision to participate. This article shed a light on the way of sharing private information without any one-side harm, in other words, mutual benefit: retailer understand more on customer, in turn, customer receives much more personalized and targeted promotion.

However, unless customer are experienced the tangible benefit, to me, it seemed not easy to overcome the customer's preconceived notion of terrifying from the outset.

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

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.

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

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!

  

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

Posted on May 6, 2013 by Bob Sutor

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.

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.

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.

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.

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.

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.

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