When the threat is real

How would you know when the new innovative, disruptive and unthinkable invention, concept/idea becomes a threat to us (or rather our business) if you ignore the possibilities and opportunities? How would you know when it becomes real that it is set to revolutionize your world that if you don’t take interest in learning about it, you will be left behind?

Be aware. The trend goes like this:

  1. There will be a few mentions via media such as news portal, social media like Twitter, Facebook but the mentions are not large enough to create a network effect for it to become viral or stand among the most viewed post
  2. Consulting firms, researchers start publishing reports about the current and future trends of the new invention/idea
  3. Some leading practitioner/business leader/professor starts re-posting/re-tweeting the post that it starts to gain traction
  4. Consulting firms, researchers and now leading firms in the relevant industry start publishing about the practical applications
  5. Leading firms/players start pouring investments to experiment the idea

At this point, it may just be a hype, because there isn’t enough track record to show the success of the applications. But the hype is scary enough that the big players start to jump on the bandwagon and startups utilizing this application are on the rise because they believe that it is real, it will be the future and it will revolutionize the way we do things. Even more so, many investors (private equities and VCs) are betting on who will be the winners.

Alternative, you can just search for the term on Google Trends and you will see an exponential growth chart as below:


If you still don’t believe that it is real, then you will surely be left behind. Imagine this, back then when you don’t believe that cars will takeover horses as a means of transportation, people are already driving automated cars while you are just about to learn how to use the car.

Humanity, fame and love

Among all the Ted Talks I have watched in the past couple of months, this is by far the most inspiring one, to me at least.

“Thoughts on humanity, fame and love”, by Shah Rukh Khan, our very own Bollywood superstar.

It’s just a 17.51 minute talk, worth listening from the start to the end, but I particularly like the last 13.19 remaining minutes. In short, no matter how creative, how innovative, how high-tech savvy, smart and successful you are, you will not be able to sustain yourself in the world where sometimes cruelty is more prevalent than humanity to mankind, if you don’t have love and compassion within you and with others.

The transcript of the last 13.19 is as follows:

13:19 It’s very difficult to remember. Which loosely translates into actually saying that all the books of knowledge that you might read and then go ahead and impart your knowledge through innovation, through creativity, through technology, but mankind will never be the wiser about its future unless it is coupled with a sense of love and compassion for their fellow beings. The two and a half alphabets which form the word “प्रेम,” which means “love,” if you are able to understand that and practice it, that itself is enough to enlighten mankind. So I truly believe the future “you” has to be a you that loves. Otherwise it will cease to flourish. It will perish in its own self-absorption.

14:09 So you may use your power to build walls and keep people outside, or you may use it to break barriers and welcome them in. You may use your faith to make people afraid and terrify them into submission, or you can use it to give courage to people so they rise to the greatest heights of enlightenment. You can use your energy to build nuclear bombs and spread the darkness of destruction, or you can use it to spread the joy of light to millions. You may filthy up the oceans callously and cut down all the forests.You can destroy the ecology, or turn to them with love and regenerate life from the waters and trees. You may land on Mars and build armed citadels, or you may look for life-forms and species to learn from and respect. And you can use all the moneys we all have earned to wage futile wars and give guns in the hands of little children to kill each other with, or you can use it to make more food to fill their stomachs with.

15:25 My country has taught me the capacity for a human being to love is akin to godliness. It shines forth in a world which civilization, I think, already has tampered too much with. In the last few days, the talks here, the wonderful people coming and showing their talent, talking about individual achievements, the innovation, the technology, the sciences, the knowledge we are gaining by being here in the presence of TED Talks and all of you are reasons enough for us to celebrate the future “us.” But within that celebration the quest to cultivate our capacity for love and compassion has to assert itself, has to assert itself, just as equally.

16:14 So I believe the future “you” is an infinite you. It’s called a chakra in India, like a circle. It ends where it begins from to complete itself. A you that perceives time and space differently understands both your unimaginable and fantastic importance and your complete unimportance in the larger context of the universe. A you that returns back to the original innocence of humanity, which loves from the purity of heart, which sees from the eyes of truth, which dreams from the clarity of an untampered mind.

17:07 The future “you” has to be like an aging movie star who has been made to believe that there is a possibility of a world which is completely, wholly, self-obsessively in love with itself. A world — really, it has to be a you to create a world which is its own best lover. That I believe, ladies and gentlemen, should be the future “you.”

What is machine learning?

There has been an ongoing debate about the distinctions and similarities between data analytics (“DA”), statistical modelling (“SM”) and machine learning (“ML”). Myself and my classmates included.

People who are not fully-equipped with the understanding and applications of the 3 terms (DA, SM, ML) mentioned above often use the terms interchangeably. I think it is extremely important to set the terminologies right and consistent across your team/organization. Despite the glorious success of machine learning techniques improving businesses’ performance famed by users, there are many challenges that were not revealed (as much as the success) and it is important to address those challenges if we want to increase the adoption rate across companies, industries and countries.

One of the known challenges is the miscommunication between the decision makers (i.e. the senior management) and the data science team. One of the main issues is getting the buy-in from the decision makers in believing in the output produced by the data science team and changing their mindset from intuition-based decision making to data-driven decision making.

In my opinion, one of the fundamental, if not the earliest ways to address this challenge is to ensure that everyone in the team/organization are on the same page in utilizing and what more understanding the terminologies used in the 3 different aspects – DA, SM, ML. I have always believed that in whatever you do or learn, get the foundations right.

So what is ML and what is the difference between ML and DA and SM?

ML is a computer activity which learns patterns, insights, key relationships from existing historical data and subsequently predict the future based on new and unseen data. The learning and predictive capability are equally important, that make up what machine learning is about.

DA consists of 3 different types of analytics – descriptive analytics, predictive analytics and prescriptive analytics. Hence, it is worth noting which type of analytics that one is referring to when they talk about DA; it could be a specific type of analytics or a combination of the 2 or 3 of them. DA is a tool for ML and both, predictive and prescriptive analytics fall under ML.

SM on the other hand, overlaps descriptive analytics of DA and predictive analytics of ML. On the descriptive analytics, the simplest form would be summarizing the data such as the average, median, mode to understand better the data we have and the work has been done many decades ago by statisticians. On the predictive analytics, while linear regression is the most frequently used model by statisticians and it is also the most basic form of ML models, the interpretation and application are different. For statisticians, it is important for them to understand the distribution of the data, whether it is a normal distribution, binomial distribution, poisson distribution etc. However, to perform ML, it is not necessary for us to know the distribution of data. Also, because of the increasing volume of data, we are able to split the data into training (for learning) and test (for predicting) data. Unlike the linear regression model run by statisticians where previously amount of data is limited, the act of splitting the data into training and testing is not required. As such, the computer is predicting based on historical and seen data. This violates the belief that past performance do not guarantee the future results.

Hope this explanation is suffice for now, will expand it further in the near time future.

The final week

Exactly 7 days from now will be the final class for me in MIT.

I can’t believe it has been a year.

I wish I could extend another term, but at the same time,  a one year program is probably just nice to end on a sweet spot.

It’s been one of the best years of my life, full of different kinds of experiences, adventures, learnings, challenges, memories. Couldn’t have done it without my husband’s support and family’s back at home. And the people I met here whom I now consider them as my long life friends, makes my life here more colourful.

R-coding playbook 

I am currently developing a playbook for all the R codes I have learned and used so that it will be easier for me to refer to in the future.

Sharing the link here so that you can benefit from it too. It is still a running list, I will add on as I go along.

I got the idea after I got really tired of opening my previous assignments, class slides and googling for the function/code I need, each time I got stucked while coding. Such a waste of time. You see, I am just a beginner. I only started using R this year. So I still need to refer to the coding examples.

In case you are wondering, I am in Analytics Edge class, one of the best classes I have attended.  For those of you who will be joining MIT and interested to learn more about analytics (subset of machine learning), I would highly recommend you to take this course. It is opened not just to graduates but also undergraduates. It is a huge class because of the high demand.

Warren Buffett and Amazon

I am a huge fan of Warren Buffett. I mean, who doesn’t? However, I wish I can say that I have read all the pages of all his letters to shareholders. I tried, but sometimes it gets too detailed about the financials that it lose my attention. Nevertheless, when I read, I always find philosophical advice and quotes about investments, business, management and life, from Warren Buffett himself and his partner, Charlie Munger.

So just wanted to share you one of his views, among many, in his recent annual shareholder meeting. I wish I could attend the meeting since I’m currently in the US but I have to own at least one share of Berkshire Hathaway’s stock. Too late now.

Anyway, this is what Warren Buffett thinks of Amazon and the CEO, Jeff Bezos.

We never owned a share of Amazon (AMZN, -0.28%) … I was too dumb to realize what was going to happen. I admired Jeff, but I did not think he’d succeed on the scale that he has, and I didn’t even think of the possibility that he’d do the things with the cloud services. I never even considered buying Amazon.

If you asked me if while he was building up the retail operation he’d also be doing something that would disrupt the tech industry, that would have been a long shot for me. I really underestimated the brilliance of the execution. It’s one thing to dream about, it’s another thing to do it … [Amazon stock] always looked expensive, and I really never thought he would do what he did today. I thought he was really brilliant, but I didn’t think he’d be where he is today when I looked at it three, five, eight years ago anyway.

Even Warren Buffett can be wrong sometimes and he admitted his mistakes. I think this is a trait that is truly admirable but often difficult to find in great leaders nowadays. As they get more accomplishments and higher roles and opportunities, they can become ‘untouchable’ and ‘always right’ person.

Something for us to ponder upon.

Required readings for aspiring VCs (repost)

If you are interested to learn more about VCs or you aspire to become a VC, then you should look at the list of required readings for aspiring VCs.

I find it very useful. It also led me to encountering more blogs written by prominent VCs and the associated people in the industry.

So far, I’ve seen a couple of VCs (including USV), which develop an investment thesis as part of their process in determining the sector/market they want to enter. Unless you want to become like 500 Startups then maybe you don’t need to go down that road.

I am starting to develop my own investment thesis which I will share with you once I have a clearer abstract.

Love the Latin Americans

I just realized that I’m left with only an hour before the day ends and I have not posted anything. So, to keep the momentum going, I just wanted to say something about the Latin Americans since I just came back from the Latin American party organized by the fellows.

The best part of the Sloan Fellows program is that the diversity of the cohort is very rich. I think it is the most diverse program in MIT. There are 110 of us, spread across 35 different countries. So, I have the luxury to be exposed to the various cultures.

One of the continent that I love most is the Latin Americans. I can almost connect with them easily. I think it is because they are generally very loving, friendly and genuine people. They care so much about their people, but also the rest of the cohort. Whenever they meet us, they will want to hug us. Relationships matter to them, a lot. And of course, trust them to organize a party. They can just dance and dance and dance. That’s how they connect and network with each other.

Love the Latin Americans – Colombians, Brazilians, Argentinians and Peruvians.

Harvard vs. MIT

I have been observing for a while, the difference between the accomplishments of people (students and professors) from Harvard and MIT. Why do I make a comparison between this 2 Ivy Leagues only? Simply because I am currently in MIT and the fact that MIT and Harvard share somewhat similar ecosystem given their short distance from each other and students from MIT and Harvard like to cross-register so that they can learn from the best of both worlds. Some of the professors in MIT also came from Harvard (not the other way round though) and we are often taught from Harvard case studies.

When people ask me, who are the prominent great leaders or successful people that come from MIT? I couldn’t answer almost instantaneously. As opposed if the question was asked from Harvard instead.  By prominent, I mean those that typically fall into the category of Top 100, Top 50, Top 10 awards by featured business magazines such as Forbes, Fortune etc. So does that mean Harvard is better than MIT?

I don’t think so.

I realized that Harvard produces notable leaders such as politicians, founders, CEOs, Wall Street decision makers and players, entrepreneurs (with Stanford as its main rival). To name a few – Barack Obama, George Bush, Franklin Roosevelt, Ben Bernanke, Steve Ballmer, Bill Gates, Mark Zuckerberg, George Doriot, and the list goes on.

However, MIT produces great researchers, theorist  which involves mathematics, science and technology and finance. Because they are mainly researchers and theorist, they are not widely known. However, they set out the critical foundation that some are revolutionary. I suppose the name, Massachusetts Institute of Technology does reflect the accomplishments after all. For example, do you know that the application of “hash chain” that makes blockchain works, was coined by Leslie Lamport who was from MIT? In finance, we have Robert Merton who developed the Black Scholes theory which is now being widely used to price options. In operations, we have John Little, who came up with the famous Little’s Law formula.

I could go on, but that’s the theory I have right now, based on my observations. I suppose it is worth hunting them down and track further their differences and similarities among Harvard and MIT professors and students.

Board of Director

So last week, a few of my classmates and I had the privilege of having lunch with Sandy Moose, after attending her talk session.

Sandy Moose is no stranger in the consulting world. She is the first female consultant hired at BCG back in 1968. She was hired by the founder himself, Bruce Henderson, who was a legend in the business world during that time. Apart from her excellent consultancy work (which garnered her numerous awards), she also sits on the board of some public listed companies, one of them being Verizon.

What I would like to share is her advice/recommendations on how to be a Board of Director.

  • If you don’t have any experience yet, start by being a chairman of a committee or if you are comfortable, join the board of a non-profit organization.
  • When you are attending your very first Board meeting, try to sit, listen and observe first, before raising your hand to give comments.
  • Must show that you support the management and other directors, but do raise any of your concerns in a diplomatic/polite manner. Be a loyal critique.
  • Get to know the other directors and management. Visit the company’s site/plant if any.
  • Understand the business so that you can give strategic high level advice, but do not try to micromanage and get involved with the management of day-to-day business. If this is a startup, then yes, that’s a different story.

What great CEOs do differently

I find this article from Harvard Business Review (May-June 2017 edition) interesting, especially as I am about to embark into a new challenging yet exciting journey. If you are wondering, not becoming a CEO (just yet!), but I think in anything that you do, it’s good to act and think like a CEO, so that you are well-equipped when the opportunity arises.

So the article highlighted 4 behaviors that set successful CEOs apart, as follows:

  1. Deciding with speed and conviction – “Good CEOs realize that a wrong decision may be better than no decision at all”
  2. Engaging for impact – “Listen and solicit views but do not default to consensus-driven decision making. While it is good, it is too slow and sometimes you end up with lowest common denominator”
  3. Adapting proactively – “CEOs are constantly faced with situations where a playbook simply doesn’t exist” 
  4. Delivering reliably – “Board typically opted for a ‘safer’ candidate who was known for delivery steady, predictable results year after year, than a candidate who performed significantly one year with many other misses in the past”

I 100% agree with 1 to 3 behaviors, but I’m not too sure about number 4. I think it is an important behavior, but I’m not sure if you are considered a better performing CEO than a CEO who exceeds target after how many years of missing it. I think it depends on the business you are in and understand the underlying reasons of not achieving the target. Imagine a start up, you can’t possibly penalize them just because their business have in the red for a few years – it’s just the nature of a start up where you will not make money in the first few years. Also, some CEOs are hired to turnaround a troubled business. But having said that, the CEOs were picked because they have proved themselves worthy in the prior years. So yeah, I think this is a grey area.

I would probably add one more important trait:

Willing to fail

However, I think it is more relevant for startups and incumbents which are in the process of innovating and less relevant for stable public listed companies. Nevertheless, I think it is good to shape that “willing to fail” mindset among the employees. They will dare to challenge and give great ideas and make significant changes.


It’s funny how the moment I changed my blog’s subtitle to “consistency is the game”, I ended up not posting for 2 weeks.

There goes my consistency.

I have no other excuses but lack of urgeness in blogging. The only consistent thing I did in the blogging world for the past 2 weeks was consistently reading Fred Wilson’s blog because he never failed to post one everyday.

Other than that, I have been very occupied with classes, assignments, meetings, discussions and more readings. On top of that, on a personal note, I am about to enter my 2nd trimester but the fatigue doesn’t seem to fade away.

Anyway, let’s try a different approach. I’m going to change the subtitle of my blog.

Any ideas?