Perspective: Million vs. Billion

"How long is a million seconds? How many days do you think that is?" I asked.

"I don't know," she said, then started counting, realised it was pretty hard to do in your head, then stopped.

I gave her the answer: "approximately 11 days".

"Now," I continued, "how about a billion? How many days is a billion seconds?"

This time she was ready. The answer was intuitive now. A million and a billion – they're not too different. We talk about millionaires and billionaires in pretty much the same breath. A little rough arithmetic and we're done.

"A hundred days?" she guessed. "Maybe a little more?"

31 years.

 


I was reminded of this fact recently in a book on financial planning by Tony Robbins, where he was trying to push the point on how we often think we know what we want, but because never thought about it in greater detail are probably really wrong about that. He used the example of how we think the road to becoming a millionaire and the road to becoming a billionaire are pretty much the same.

They're different. Very different.

Another recent story came to mind, this time in the world of fitness: that of how a pro gambler won a $1 million dollar bet to go below 10% body fat. Reading the story, I was reminded how different it was to go from 25% to 15%, as compared to going from 15% to sub-10%. Seemingly similar goals, but in reality very different.

I wonder how many other things we take for granted to be similar but in reality are anything but.

Getting Excited About Small Data

The next few quarters for analytics in my company are, from my perspective, going to be game-changing, and I’m excited to say my team’s taking the lead on it: from machine learning and advanced visualisations to new ways of thinking about data, we’re currently taking the steps to get to what I call “the next phase of analytics”. We are a small team with big dreams.

But what I often get from friends (and some colleagues) when I tell them about the things my team is doing, though, are questions on how “big data” is playing a part in it. Specifically, how it figures in our plans for the next few quarters.

When I tell them it doesn’t, they look at me as if I just said I loved eating broccoli ice-cream: perplexed; a little disgusted; and mixed with a bit of pity on the side. (If you clicked on that link or you know that song, you might have guessed I’m doing that parenting thing.)

“Big data” simply doesn’t factor in those plans (yet). We have enough small data to worry about to even think about big data. And yet, to them small data is yesterday’s news. It’s as if small data doesn’t count; as if it’s nothing to get excited about.

But it does count. And to those who haven’t yet experienced the joys of wringing all the value out of small data, it is downright exciting.

Sure, big data has the potential to change the world, and in many cases it already has. But by and large most of the value of big data still lies in its potential.

Small data, on the other hand, has long shown its ability to change the world.

I love especially this little story from the book mind+machine by Marc Vollenwider:

Using just 800 bits of HR information, an investment bank saved USD 1 million every year, generating an ROI of several thousand percent. How? Banking analysts use a lot of expensive data from databases paid through individual seat licenses. After bonus time in January, the musical chairs game starts and many analyst teams join competitor institutions, at which point the seat license should be canceled. In this case, the process step simply did not happen, as nobody thought about sending the corresponding instructions to the database companies in time. Therefore, the bank kept unnecessarily paying about USD 1 million annually. Why 800 bits? Clearly, whether someone is employed (“1”) or not (“0”) is a binary piece of information called a “bit”. With 800 analysts, the bank had 800 bits of HR information. The anlaytics rule was almost embarrassingly simple: “If no longer employed, send email to terminate the seat license.” All that needed to happen was a simple search for changes in employment status int he employment information from HR.

The amazing thing about this use case is it just required some solid thinking, linking a bit of employment information with the database licenses.

Small data can have big impact.

So yes, I am excited about small data!

And no, big data won’t be part of our coming analytics revolution. (Yet.)

The problem with running a team at full capacity

I shared this earlier on LinkedIn, but thought that it was worth sharing it here too as a reminder to myself: Six Myths of Product Development

I came across the article above while researching why a team that traditionally does great work may sometimes stumble (yes, mine). The past few weeks had been a whirlwind of activity, with team output close to or at an all time high. We were publishing and developing things left and right, and everyone was running close to capacity. It was great.

Then came an e-mail that questioned the quality of the output. Then another. Much of the great work threatened to come undone, but thankfully most made it through unscathed. We were still, generally, in a good place. But this was a wake up call. Something needed to be done.

After I explained to her my conundrum, my knowledgeable friend, Google, suggested an article from the Harvard Business Review website called “Six Myths of Product Development.”

It was a most excellent suggestion.

The article highlighted six myths or fallacies:

  1. High utilization of resources will improve performance.
  2. Processing work in large batches improves the economics of the development process.
  3. Our development plan is great; we just need to stick to it.
  4. The sooner the project is started, the sooner it will be finished.
  5. The more features we put into a product, the more customers will like it.
  6. We will be more successful if we get it right the first time.

It didn’t take long for me to realise that our problem was very likely linked to #1: I’d neglected slack.

You see, I normally tend guard slack time jealously as I know time-pressures are often a big cause of low quality output. But given the myriad of “urgent” business needs had allowed myself and the team to run too close to full capacity.

We have seen that projects’ speed, efficiency, and output quality inevitably decrease when managers completely fill the plates of their product-development employees—no matter how skilled those managers may be. High utilization has serious negative side effects… Add 5% more work, and completing it may take 100% longer. But few people understand this effect.

It’s funny how bringing down the amount of expected output may actually increase it.

(As an aside, I love point #6 – I’m a big fan of “fail fast, fail often” as I believe strongly in “the wisdom of crowds”, where we can aggregate feedback and iterate quickly, especially for early development. But it’s not always easy to get business buy-in, especially when all they see in “fail fast, fail often” is “fail”!)

 

The need for theory in prediction models

I’d like to share this wonderful quip by philosopher Robert Long, that was quoted in the (also insightful and actually pretty good) book A Richer Life by Philip Roscoe:

Let’s say that in early 2001 I formulate a theory to the effect that there is a Constant Tolkienian Force in the Universe that produces a Tolkien film every year. When Austrians complain that my theory ignores the fact that films are products of human action and not of constant impersonal forces, I reply: ‘Oh, I know that. My theory isn’t supposed to be realistic. The question is whether it’s a good predictor.’ So I test it in 2001, 2002, and 2003. Lo and behold, my theory works each year! … But unless I pay some attention to the true explanation of this sequence of film releases, I’ll be caught by surprise when the regularity fails for 2004.

The above quote relates to the “blind” prediction models that we build (maybe statistical, maybe machine learning), where “accurate prediction” is so often the aim of the game.

The problem with just going for accurate predictions, without thinking about the underlying causes or theory, is that it’s difficult to tell whether or not the predictions are flukes, or if they contain true insight that may not be so obvious to anybody or anything else but the algorithm.

Though the quote above is rather tongue-in-cheek (the “Tolkienian Force” model is really built on just three data points, way too few for statistical significance) the moral of the need for a good theory wasn’t lost on me: theory is the human ying to the technological yang. They balance and support each other, creating something much more powerful and persuasive than either would alone. When one is amiss, the other somehow doesn’t feel right either.

Another (More Selfish) Benefit of Theory

They’re actually really useful when persuading the business on accepting the results of a prediction model that they otherwise know nothing about. Without a theory, on what basis should the business believe the model? On faith?

That’s a tough sell.

And if you were the one who built the model, and they “trusted” the model because they trust you, what happens if the model fails?

Well, you fail with the model.

With a convincing theory, the model stands on its own merits. They may “trust” the model a bit more because they trust you, but if the model fails, it’s the theory that fails with it, not you.

Playing Baseball without a Bat – a great example of effective statistical visualisation

Came across a very interesting and persuasive video on baseball via Kottke.org today. It’s a great example of what an interesting question, effective visualisation, and some statistical knowledge can do.

The question the video seeks to answer is the following: what would happen if baseball player Barry Bonds, who happened to play one of his greatest (if not the greatest) baseball seasons ever in 2004, played without a baseball bat?

I’m not a baseball fan, and frankly quite a number of the things that were mentioned in the video were lost on me. But I’m a fan of interesting statistics and great visualisations, and this definitely had both.

And despite having a few doubts at its conclusion (the results seem too good to be true – watch to the end!), it is convincing and definitely worth a watch if you’re either into baseball or statistical visualisations.

On meritocracy, luck, and giving back

Kottke’s post on meritocracy, a concept that I had in my younger days considered infallible, reminded me that even those of us who have worked hard and achieved so-called “success” have much to owe to “luck”.

Even the smartest, hardest working, most beautiful of us all, would likely have not fared well, had we been born in the midst of a famine to parents who couldn’t even afford to feed themselves.

And even the dumbest, most slothful, and ugly of us all, would not have fared too badly, had we been born to highly influential and powerful parents whom held us in even the slightest regard.

So let us all remain humble if are ever lucky and become “more successful” than others.

We probably owe more to chance and luck than we think.

Lucky

I met up with a friend last week over lunch, and one of the things that was brought up in the conversation was on our work, our careers. He was genuinely happy and excited for me that I was (finally) going to graduate from my Master’s degree in Analytics.

To him, my having these analytical skills, backed with a Master’s degree, would easily propel me to the top. I would, he said, be in high demand.

Being quite the realist, though, I didn’t exactly share his optimism.  I knew that even if I was the best in the world at what I did, if nobody knew what I did, it didn’t matter. There would be far too many people like me with similar qualifications and experiences.

But I knew where he was coming from.

It was true that my skill set was in demand. And it was true that I probably had an easier time than most in finding career opportunities. Unlike many others I knew, I was in the rather envious position of not worrying whether or not I’d find another job if I left my current one, by choice or otherwise, because I knew I would. I only stayed because I wanted to.

It then occurred to me how lucky I was.

Living the Dream

“I am living the dream,” I said to the group, “doing what I love.”

I was in a management development workshop organised by the company, and that was my response to the question, “tell us something nobody else in the workshop knows.”

It had come spontaneously and was as much a surprise to me as it was to everyone else.

It wasn’t that my career was perfect — I still had much I wanted to do; much I wanted to achieve.

But given all the million-and-one constraints, my career’s turned out pretty good: leveraging my business-IT background, I work within Sales but deal with technology (even doing some scripting and programming) every single day; I develop data products that are used by hundreds, from the frontline through to senior management; I regularly get to present my ideas and train Sales on technology and data literacy; and I lead a team of wonderful colleagues who do excellent work (and at the same time have a great boss); it’s almost precisely how I would have envisioned a “good” career outcome (shame about the pay!)

But it could have been so different.

I knew was lucky.

Right Place, Right Time

I was lucky in that my parents weren’t poor, and had purchased a computer for the home even when that wasn’t a very common thing to do. And I was lucky that I was allowed to use this very expensive toy, which exposed me to technology at a very young age.

I was lucky that I grew up in a time when the Singapore government wasn’t too interested on clamping down on software piracy — I suspect the government did this on purpose because many of us, though not poor, were not rich enough to actually purchase professional-grade software to play around with. 99% of what I know I learned on bootleg software.  This move alone probably bumped up Singapore’s technological literacy a fair bit.

I was lucky that I was never stopped in pursuing my love for technology — when I opted for a technology-focused polytechnic education (i.e. the Diploma route) instead of going the more traditional “junior college” (i.e. the A-Levels route), I never met any parental resistance (which in a way, was because I was lucky enough that my grades were good but never exceptional, and so my parents didn’t really care — had they been exceptional, my guess would be that the would have been far more opinionated).

I was lucky that I was hired for an analytics position at the very last interview that I decided to go for before heading into the world of Financial Advising, thereby leading me to my current world of technology and analytics… what were the chances?

Right place. Right time. And if not enabled by the luck, at least not hindered.

But not everyone will be so fortunate, and it is up to us, the lucky and empowered ones, to give back and to try to provide opportunities to others who may not be as lucky.

Yet.

On Giving Back

My one simple philosophy on giving back: that anyone whom I work  or in any way interact with should find that if I had never appeared in their lives they would have been a little poorer for it.

I seek to be the luck in people’s lives.

Because so often they are in mine.

What you do determines what you see

Author’s note: This post was originally titled “Déformation Professionnelle”, but I had trouble understanding it myself and have renamed it for easier future reference!

This post in three words: Profession -> Perception -> Truth

The following text is taken from the excellent book The Art of Thinking Clearly, by Rolf Dobelli.

A man takes out a loan, starts a company, and goes bankrupt shortly afterward. He falls into a depression and commits suicide.

What do you make of the story?

As a business analyst, you want to understand why the business idea did not work: was he a bad leader? Was the strategy wrong, the market too small or the competition too large?

As a marketer, you imagine the campaigns were poorly organised, or that he failed to reach his target audience… As a banker, you believe an error took place in the loan department.

As socialist, you blame the failure of capitalism.

As a religious conservative, you see in this a punishment from God.

As a psychiatrist, you recognise low serotonin levels.

Which is the “correct” viewpoint?

The above is also what is known as Déformation Professionnelle (what a term!) — a tendency to look at things from the point of view of one’s own profession rather than from a broader perspective.

I’m only too wary of falling into this trap, which is especially easy for me to do because my expertise lies in data and its derivatives and the scientific method , things I hold dear and believe are as close you can get to a panacea for all the world’s ills.

Which is why I often preface the ideas I share with, “if I put on my analytics hat…”, because I know not everybody will share the same view. And I respect that.