Breath for the Week (3)

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I take a break from blogging this week, in order to catch my breath and focus more on other work: Theory of Algorithms and The Core Algorithm.

How often do you step back from your work, and what do you do to catch your breath?

Breath for the Week (2)

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I take a break from blogging this week, in order to catch my breath and focus more on other work: Theory of Algorithms and The Core Algorithm.

How often do you step back from your work, and what do you do to catch your breath?

Breath for the Week (1)

(image credit)

I take a break from blogging this week, in order to catch my breath and focus more on other work: Theory of Algorithms and The Core Algorithm.

How often do you step back from your work, and what do you do to catch your breath?

Chevron and GE Partner on Technology Innovation

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‘GE brings its leading manufacturing capabilities, worldwide marketing, distribution, and extensive R&D capabilities not only for oil and gas, but also other business sectors to this alliance,’ said Paul Siegele, president of Chevron Energy Technology Company and chief technology officer. ‘Together, we hope to bring impactful new technologies to the industry.’

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‘Chevron’s deep understanding of the oil and gas industry, combined with GE’s long tradition of technology development and close collaboration with strategic partners, will uniquely position this new alliance to address the industry’s technology needs,’ said Lorenzo Simonelli, president and CEO, GE Oil & Gas. ‘The solutions developed by this alliance will take on even more industry significance given Chevron’s proven leadership in being first to field-test and deploy new technology breakthroughs.

Reference: CHEVRON ENERGY TECHNOLOGY COMPANY AND GE OIL & GAS ANNOUNCE THE CREATION OF THE CHEVRON GE TECHNOLOGY ALLIANCE.

George Reed posted this article in Chevron's group on LinkedIn, and I commented:

Thank you for this article, George. I just watched a terrific talk that Jeff Immelt gave at Stanford a couple of years ago. Because we live in a highly networked world, he said, it is important to be open to relationships and be willing to forge partnerships. This alliance between two giant companies could really move the needle on technology innovation, especially in an era of abundance for the oil and gas industry!

Thank you for reading, and let me know what you think!

Ron Villejo, PhD

GE CEO Jeff Immelt and Theory of Algorithms

Jeff Immelt

In his talk at Stanford Graduate School of Business - Leaders Must Drive Change - GE CEO Jeff Immelt spoke to several things that resonate with Theory of Algorithms and The Core Algorithm.

Theory of Algorithms as a conceptual framework improves our efforts to solve all sorts of problems and sharpens our ability to know what we need to know.  Besides being a meta-model for problem-solving, it also aspires to be a complete epistemology.  The Core Algorithm is its practical applications model, and therefore lays out the meta-methods and meta-processes for actually solving problems and knowing things.

The following references what Immelt said and how it resonates thus with ToA and TCA:

GE does a lot of things in a lot of places, and consequently optimize its technology and costs.

It is important for GE leaders to truly understand how things work.  This is the seminal precept of ToA, when I first crystallized what I needed to do and could do: that is, to delve into things and truly grasp how they work.  It is a process I've come to call extracting the algorithms, because once this is done, then these algorithms are applicable across situations, purpose and organizations.  

For example, consider the engine of a motor vehicle.  Of course, over the past century, automobile companies have evolved the engine into something far more sophisticated than Henry Ford could've imagined.  Yet, its essential operation remains the combustion of gasoline, which produces energy that powers diverse models and types of vehicles.  That engine is an example of TCA and is thus a practical application of ToA.

That applicability is what can help companies operate more time-efficiently and cost-effectively.  In American parlance, We have invented the wheel, and we don't need to re-invent it.  

Immelt commends Stanford students as smart enough to do his job.  The question was, however, Do they want to?

Step 1 of TCA is `Begin with the end in mind.  It is important to clarify, as best as possible, what you're trying to accomplish, what problem you're working to solve, or what opportunities you aim to capture.  So in developing the corporate algorithms that are grounded on TCA, I work closely with a CEO on this first step, and ask the question that Immelt asks: How badly do you want this?  That is, How important is it for you to accomplish this goal?  We stay on Step 1, until we arrive at clear enough, satisfactory answers to this question.  

Many people set resolutions at the beginning of the New Year - such as losing weight, quitting smoking, or eating more healthy foods.  Those for whom these resolutions fall by the wayside may not truly want to work at these things.  So they, like a CEO, have to openly and honestly reflect on what they're trying to accomplish and ensure they have the requisite reasons and motivation.  

If not, then it simply doesn't make sense to whatever those aims.        

You can read 30 books on China, or you can spend 100s of days in China.

Immelt's point is that you have to be there to understand China, and suggests that you cannot learn by proxy.  I don't argue with this at all.  Rather, I offer two perspectives for CEOs to reflect on.  One, doing research on a country goes hand-in-hand with experiencing that country directly.  We can extract algorithms about any situation, yes, even from a distance.  But the integrity, strength and utility of these algorithms depend on multiple situations.  So the more varied we go at understanding China, for example, the better our efforts will ultimately be.  

Two, how you go about understanding China really depends on your situation, context and purpose.  For example, as a startup management consultancy, I have set China as a target market for way down the road.  I don't yet have the resources and funds to travel there, just as Immelt can, but I definitely can learn a lot about the country just from the means I have at my disposal, mostly free, such as business publications, videos and sites online.

Be able to see things from others' point of view.

I read recently that about 25% of the world population still believe that the sun revolves around the earth, that is, a geocentric universe.  I have argued in TCA that we as humankind are wired to be self-centered, as if the world did revolve around us.  But successfully hitting targets requires us, first of all, to step outside of ourselves and, second of all, to go into the future.  

In serving customers, forging partnerships, and engaging staff, the CEO must do both quite well.  Seeing things from others' point of view is the stuff of Emotional Intelligence, which comprises of both self awareness and social awareness.  

If you're going to do something different, people are going to hate you.

Social media has given us avenues to marvel at what some people do and to praise them accordingly.  It has also given license to would-be haters to do the very opposite.  I am very much in that boat of doing something extraordinarily different and complex with ToA and TCA.  I am very fortunate that the hateful, ignorant people have been far and few between, but they're certainly there.  While a growing few have been really interested, and worked with me on grasping this concept and this model, a good majority dismiss them. 

Understandably so.  For now, anyway.  

As I told one colleague a year ago, I'm doing unconventional stuff, so how I go about presenting it cannot be conventional.  Because it is not conventional, people are simply not going to like it all at first.   

          

Thank you for reading, and let me know what you think!

Ron Villejo, PhD

GE CEO Jeff Immelt on Leadership Competencies

Jeff Immelt, chairman and CEO of GE, gave the first annual Roanak Desai Memorial View From The Top at the Stanford Graduate School of Business. He discussed how business has changed in his nearly three decades at GE and stressed to the student audience to be prepared for a turbulent business world.

There are four broad categories of leadership, that I have drawn on as a framework for weighing competencies: Thought, People, Results and Personal.  Different companies and consultancies deploy various competencies, but I have found these categories to be enduring ones and applicable across contexts.

1. Thought Leadership is about how much and how well one knows such areas as strategy, finance and technology.  In particular, too, it has to do with critical thinking, that is, grasping new, complex information, solving problems effectively, and making sound judgments and decisions.  
2. People Leadership is about the constellation of working with others and leading them effectively: from engaging and motivating, to building relationships, resolving conflict, and coaching and developing.
3. Results Leadership is of course about driving oneself and others to achieve and executing not for the sake of executing but for the purpose of hitting targets, fulfilling priorities, and reaching aims.
4. Personal Leadership is about how one manages oneself to take on the exciting but complex responsibilities of leadership:  adapting oneself as necessary, holding to ethics of conduct, managing stress and leading under pressure, challenges and ambiguity.

Interestingly, the 10 competencies that Immelt emphasizes for his leaders at GE are evenly divided among these four categories.

Thought Leadership

• Listening analytically
• Thinking systemically
• Understanding how things work

People Leadership

• Managing relationships, forging partnerships
• Managing diverse people
• Actually liking people

Results

• Simplifying everything
• Holding oneself accountable

Personal

• Adapting and persevering, dealing with volatility
• Being courageous and seeing solutions through

Both these GE competencies and the four categories are simply frames of reference.  You as CEO must (a) weigh the fundamental purpose of your leadership vis-a-vis your company or organization, (b) acknowledge the context or landscape that you're in, and (c) decide what you and your leadership staff must be able to do effectively.  More than likely, then, you adopt some of these competencies, and discard others, and perhaps draw on these categories or create your own unique set.  The end in mind is serving that fundamental purpose.

Thank you for reading, and let me know what you think!

Ron Villejo, PhD

What They Don't Teach in Business School

Part of 2010 Conference on Entrepreneurship [at Stanford University] 

Description: A group of entrepreneurs talk about what they learned in the trenches that they never could have learned in a classroom. The panelists will also share the courses that were most helpful to them in their entrepreneurial ventures, the courses that they wished they had taken, and the topics that business schools should be teaching to aspiring entrepreneurs.

My notes

Chuck Holloway moderates the panel discussion.

Mike Cassidy (Harvard) has started up four businesses, and has had three successes so far.  He's still working to see how his fourth can succeed.

The morale of his team is a key factor.

Speed is the ultimate weapon, for example, in launching products.

I have this fantasy: I wish I had the power to control the brain of someone I'm trying to get a business development deal with.  You can try different sales techniques, and find out which one works.  Ask, even if you think it's too far-fetched or too much.

My first one, we (three partners) put in $500, so we started with $1500.  Even small amounts can feel like a million dollars.

We keep meetings sparing in a streamlined schedule.

There's benefit to exponential growth, such as investor confidence and staff morale.  But it's hard to sustain drive and motivation over long periods of time.  That's why I like to sell my company after two years.

Nazila Alasti (Stanford) debunks the notion that mothers cannot be entrepreneurs.

She didn't have a line-of-sight on her contributions at a juggernaut of a company like Apple, so this was one motivation to go into her own business.

Small sailboat analogy speaks to our journey.  You shouldn't give up.  If you're still coming up with good ideas, keep at it.  Persistence always pays.  Stay on the process.  Don't let people derail you, even if you're six months pregnant.  The alpha male is a lion, but I'm an ant.  I just work in a small, persistent and methodical way.  The lion sits in the shade, then chooses which prey to go after.

It's hard to get $10 out of a person.  Maximizing profit vs maximizing value.  I have to ask, What am I maximizing?  We always think bigger is better, but I'm not sure that applies all the time.  If you work at an organization, pay attention and learn about scale.  Learn where you're weak, if you want to be more of a holistic entrepreneur.

If you don't believe it, nobody else will believe it.  At the end of the day, I just sit for 10 minutes.

Watch executives: How they run meetings, what they do.  Get to know their administrative assistant.  Take advantage of learning and development offerings at your company, because at a startup there aren't any.

From e-mail, shift to actual customer service and customer support.  It'll be a renaissance.

Will Price (Northwestern) abides by the notion of: How could you not do it?  If not now, when?

The culture is about risk-taking.  Entrepreneurship is a full-wafer test (rf. semi-conductors): It tests your passion, your endurance, your perseverance, your leadership, your sales ability.  Trusting in process that you will get to the answer.  Investing is hypothesis-driven thinking, and he had to switch process-driving thinking.

A team that recognizes, and grasps, the market quickly is important.  Knowing how to work with investors is also crucial, given the inherent differences in thinking between them and entrepreneurs.  They're more likely to hold to an original idea and plan, and won't be pleased to hear that it's not working.  

What I didn't learn?  Risk is relative.  There is no such thing as a safe place.  Business schools over-teach how rational organizations are.  Sometimes I don't understand how some decisions are made.  Idea-driven, logical and rational vs people- and politics-driven.  For some reason, business schools are prejudiced against sales.  But it is the sales people who run the world.  If you're good, and you can sell, go sell.    

Be authentic with people.  Conversations with the right people can recharge you.

I worked at Morgan Stanley.  Learn how stuff gets done, learn how you work.  The difference between good and great is 10 minutes.  Before you submit something, walk around for 10 minutes and review it.  You'll find mistakes that you can correct, before you actually submit it.

A CEO has two jobs: to sell and to raise money.  Crack the formula the code for sales, hire renaissance people who can do everything.

Be capital efficient.    

Thank you for reading, and let me know what you think!

Ron Villejo, PhD

Mark Cuban and Guy Kawasaki on Entrepreneurship

Entrepreneur Mark Cuban discusses the U.S. Economy and starting a business with Trish Regan at the Clinton Global Initiative in Chicago on Bloomberg Television's "Street Smart." 

I'm not big on excuses, just do it. It's more about effort and brains, not so much about capital. You better know about your industry, because you're competing. The new normal is 2 - 3% growth, and it's not necessarily we're doing something wrong. Watching something live creates a unique experience.

The UC Berkeley Startup Competition (Bplan) proudly welcomed Guy Kawasaki to the Haas School of Business. Kawasaki, former chief evangelist of Apple and co-founder of Garage Technology Ventures, explained the top ten mistakes that entrepreneurs make. His talk covered all stages of a startup from inception to exit.

Top 10 Mistakes

1. Multiplying big numbers by 1%.  Getting 1% of any market is not as easy as it sounds.  Yet, no investor will support a business that taps only 1% of any market.
2. Scaling too soon.  I've never seen a company die, because it didn't scale fast enough.  Instead, scaling too fast risks you being left with big overhead, no money, and your venture doesn't work.
3. Partnering.  Partnerships don't mean anything.  What entrepreneurs need to focus on is sales.  Meeting your numbers is what investors look for.  
4. Pitching instead of prototyping.  Marketing is free or cheap via social media, and your team can be virtual.  Show up with a prototype, and thus lend confidence that you can deliver.  
5. Using too many slides and too small a font.  Present with 10 slides, in 20 minutes, using 30-point font.   
6. Doing things serially.  The linear sequence doesn't exist in entrepreneurship.  Instead, you're doing several things in a parallel process, moving multiple things down the road.  
7. Believing 51% equals control.  51% offers merely the illusion of control.  
8. Believing patents equals defensibility.  It takes five to six years to secure a patent, and even then a major company can still take your idea.  The exception perhaps is a biotech product.
9. Hiring in your own image.  Many companies like the hire the same kind of people.  Instead, hire people who complement your skills.  You need people who can make it, sell it, and collect it.  
10. Befriending your venture capitalist.  Venture capitalists aren't in the business to make friends, but to make money.  So make your forecasts.  Under promise and over deliver.
11. Thinking venture capitalists can add value.  They have a hard time separating causation vs correlation.  Simply seek money, and maybe two to three hours of their time.

That's 11 mistakes, because I believe in over delivering.

Having too much money is worse that not having enough money.  So I love the concept of Lean Startup.  I believe the organic, lean way is the way to go.  You have to start with a prototype.  Just because you have shared space, that is, at incubators and accelerators, it doesn't mean your chances of success are greater.  Again, do what you need to do to create a prototype, something tangible.  There is no hard-and-fast rule about whether it's better to keep going or give up.  Don't sweat it.  

Thank you for reading, and let me know what you think!

Ron Villejo, PhD

On Being a Startup Entrepreneur

Get out of your comfort zone.  A lot of people have a lot of ideas, but it's what you make happen that makes a difference.  If you're not uncomfortable enough, then you're not being innovative enough.  Fight inertia.  Imagine a toddler just learning how walk, that's how it is to be an entrepreneur.  Writing is great background for being entrepreneur, because it helps you think about how people are going to react emotionally.  We're all insane.  We're fed up.  Entrepreneurial mindset is a product of how you were brought up.

Everyone should take improvisational (acting) classes.  It helps to prepare you for meeting up with investors and presenting your ideas and plans.  Such classes may be better than formal public speaking programs.  Scopely teaches them.

When does a startup stop being a startup, and start being a 'real' company?  It depends on your mentality.  Google wants to be known as the startup, that is, for its culture, mentality and innovation.  It's about listening to what people want, not so much about what you want.  Startups are a matter of inevitability, that is, we're doing it.  Spread the belief that this thing is going to happen.  You need to get people to believe in it.  Imagine ducks gliding smoothly and easily across the pond, but below the waterline they may be paddling like crazy.  That's a way to get people to believe.

Whom do you look to for inspiration?  Young people and various communities can be inspiring.  Social media helps you reach your youthful audience, and vice versa, and the ideas they come up can be inspiring.

Where do the best ideas come from?  Put yourself in an area of overlapping circles, as in Venn Diagrams, where there is the possibility of combustion.  Watch out for Kickstarter, as others may copy your idea.  Listening and watching people, it feels like being a voyeur.  Keep things in a scrapbook.    

Thank you for reading, and let me know what you think!

Ron Villejo, PhD

Infostorms, Epistemology, Theory of Algorithms

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Kenneth Mikkelsen posted about this upcoming book on Google+:

Sat down with Vincent F. Hendricks in the beginning of January to discuss his new book; Infostorms: How to Take Information Punches and Save Democracy.

I highly recommend reading it in order to understand how new information technologies challenge the way we process information and make decisions.

Based on new insights from philosophy, logic, social psychology, behavioral science and economics the book explains how to navigate in the information age and shows how information is used to enlighten but also manipulate people, opinions and markets.

The website for the book offers more information, including the Table of Contents: Infostorms.  It was interesting to note Hendricks is a philosophy professor and also the editor-in-chief of Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science.

Interesting, for this particular reason: I just had an exchange of messages on LinkedIn with a old friend from Northwestern University and T'ai Chi classes in Chicago.

Thanks very kindly for watching my videos and sharing your notes! For years, I had heard of physicists' efforts to arrive at Theory of Everything, and thought it was bound to fall terribly short. For it to be true Te, as you saw, it has to account for absolutely all theories. Also, because we simply don't know everything, Te also had to account somehow for theories we have yet to formulate.

Beyond that linear equation, I've come up with another formulation that better accounts for that infinity of infinity and also accounts for a universe that is possibly (or probably) more than three dimensions (rf. String Theory). But that formulation is very crude, for now.

The conceptual aim of Theory of Algorithms is to be a complete epistemology, and its practical purpose, once completed, will be to allow us to solve absolutely any, and all, problems we can ever face... more effectively. The practical applications model is The Core Algorithm, and I've completed three algorithms that are for top leaders and their organizations.

I'm drawing on science, art and religion in this effort, and this comprises my Tripartite Model. Over the next several weeks, I'll resume writing articles for my ToA and TCA blogs and advancing both. This is part of getting back on my feet, and I'm thankful for it.

I had to look up epistemology :-) , as I always get a little fuzzy about meta-knowledge concepts. What helped me understand it is Wikipedia's statement: "In epistemology in general, the kind of knowledge usually discussed is propositional knowledge, also known as 'knowledge that.' This is distinguished from 'knowledge how' and 'acquaintance-knowledge.'" 

Is that a fair description of your work? If so, it might be helpful to clarify this in your next output, as I was thinking that your model was about "knowledge how" because you say that it can be used to help solve problems.

I remember epistemology in philosophy class in our NU days, and I use it to refer to how we come to know what we know. So, yes, in effect, meta-knowledge and Theory of Algorithms is, in part, a theory of knowledge.

In corporate language, it's about methods, process and systems. But more accurately The Core Algorithm is a meta-method. It's a method that doesn't prescribe solutions a priori, but instead offers a way of grasping problems as well and as fully as needed and then determining which methods will actually help solve them. Many consultants have lead off with their database, solutions etc. I am challenging that.

Besides creating a conceptual framework and its practical applications model, every algorithm I devise will be wrapped around a business model. So it's a bit of daunting effort not just to "practicalize" a theory but also to monetize it.

Mikkelsen's post helped me to crystallize the link between epistemology and Big Data and Analytics and the reasons why the latter is pivotal vis-a-vis Theory of Algorithms and The Core Algorithm.  In any event, Infostorms looks to be a very thoughtful book, indeed.

Thank you for reading, and let me know what you think!

Ron Villejo, PhD

Innate Complexity of Trust vis-a-vis Big Data

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"Garbage in, garbage out," one colleague used to say periodically, when cautioning and advising a client on making decisions.  Basically he means that the integrity - validity and reliability - of information you draw on must be sound, or else any output from that information (i.e., decisions) is immediately suspect.  

That said, consider the following from an article that Don Peppers posted on LinkedIn - How to Know When Data Can Be Trusted:

A recent Forrester survey of some 58,000 consumers shows that while 70% of us trust the opinions and product reviews of our friends, just 10% of us trust advertising messages. This doesn’t mean we think the ads are lying, but that they are biased. By definition, advertising is not objective. How could it be? The only reason a company invests in an advertising campaign in the first place is to recoup that investment and more in the form of product sales. Ads are designed to persuade. They are biased toward getting you to buy. 

On the other hand, when you ask a friend’s opinion of a product, your friend is unlikely to have such a bias. His opinion will therefore be much more objective. 

This is one reason why, as information continues to inundate all of us more and more, we will begin to rely on “social filtering” to make sense of things. What do our friends think of this, or the friends of our friends?

Social filtering allows you to enhance the objectivity of the information you encounter largely because it helps you rely on your friends’ opinions, which are less likely to be biased.

It is one thing for me to have had a friend for a long while, and I know how he or she thinks, and another thing for me to have a friend on Facebook, whom I may know only circumstantially, if at all.  

Also, Facebook as a social platform is an advertising platform as well.  More specifically, it is the curator of social media and is the liaison for advertisers.  In this respect, I simply do not trust what they say my friends like.  Again, I don't necessarily know who those friends are - in fact, I wonder how many of us actually know our friends on Facebook - and clicking Like is such an easy, convenient thing to do, that I wonder how much of us actually even read, viewed, or otherwise considered a particular post.   

I may be wrong about this, but I believe that Facebook has exploited that ease and convenience to sell its social platform to advertisers.  So that social filtering that Peppers hangs his trust-hat on is in itself suspect.  

So ask that very question he asks, How do we know when we can trust data?  

For one, trust is a very complicated thing, and it defies simplistic reasoning, such as Peppers'.  Trust in itself is a human phenomenon, that is, subject to the best and the worse of what makes all of us human.  To parcel it as an issue of objective versus subjective is naive, because in its essence it is some balance of both: We simply cannot know everything, about anything, at any given time.  

So we must gather evidence and observations, feedback and facts, and then use our knowledge and experience to render a judgment about something.  This, in brief, is an essential process of trust.  

But even then it's still an imperfect process.  How many times has a spouse trusted his or her partner, only to have that trust ruptured with some betrayal or another?  A married couple may have been together for years, perhaps carefully nurturing a trusting relationship, but one incident can undo it in a heartbeat.  

So when it comes to answering that question, I argue first that we must understand as best as we can the nature and process of trust.  

Second, the issue of data itself is a very distinct one.  What does it speak to, and how was it gathered?  How does it relate to other data that's been gathered, and what other data needs to be gathered to address particular business matters?  How much diversity is there among the types of data gathered, and how well can they be synthesized or integrated for analysis?

The question that Peppers asks is a crucial one, indeed.  The correct answers are described and argued than actually obtained and executed.

Thank you for reading, and let me know what you think!

Ron Villejo, PhD

Moral Imperative of Big Data and Analytics

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Big Data and Analytics are about discrimination or, in business parlance, segmentation.  The idea with any marketing effort is to slice a universe of customers into discrete groupings, thereby allowing sales people to differentiate and optimize their efforts accordingly.  

But isn't discrimination the scourge of civilized communities?  

In Big Data's Dangerous New Era of Discrimination, Michael Schrage aptly raises a moral question about what a purely analytic approach may do and he offers several examples of fine-tooth comb, data-driven segmentation.   

It's an irony for social activists, I think.  The stuff of discrimination goes like this: You may have had some experience, positive or negative, with a handful of people, and you generalize your experiences into a conclusion about the particular group to which this handful belong.  Going forward, any other person you meet, who belongs to this group, immediately triggers your conclusions about what they are like and what they are not.  

The essence of prejudice is making a prejudgment about people, well before you've met them.  Discrimination is differential treatment, response or inclusion of certain people, based on that prejudgment.   Regardless, it is a flaw in inductive reasoning, the hallmark of which is unsystematic thinking and little supporting evidence.

The irony for social activists is that companies have data to back up what may frankly end up being discriminatory business practices.  For better or for worse, gay men may get preferentially better customer service, while African American women the opposite, based on how profitable their respective segments actually are.  

I see a missing piece in Schrage's otherwise very compelling argument.  The technology behind Big Data and Analytics can often pinpoint details about particular individuals, that is, current or prospective customers.  So, depending on such details, then, a company may, and should, serve an African American woman and sidestep a gay man.  In other words, technology can do a segmentation of one, even in real time, as, say, you navigate an e-commerce site.  

Nevertheless, Schrage duly prompts companies to reflect on its practices.  How they navigate among business priorities, on the one hand, and a moral imperative, on the other hand, goes beyond Big Data and Analytics.  Sure, they can formulate algorithms based on a more individualized segmentation, but how much will they account for the fact that people have needs and people deserve respect?  

Even if a particular individual is of a particular segment, which is deemed unprofitable by Big Data and Analytics, should he or she be served anyway?

Thank you for reading, and let me know what you think!

Ron Villejo, PhD