work – Pertinent Observations

New blood joins this team

I intended to write this a year ago, when Sadio Mane left Liverpool after six brilliant years at the club. There was much heartbreak among the club fan base about Mane leaving, and a lot of people saw it as a failure on the part of the management and ownership in terms of not being able to keep him.

Now, a year on, I admit that Darwin Nunez hasn’t quite set the club on fire (though I personally quite like him), but as a general principle, this kind of “freshening up” is a highly necessary process in a team, if you need to avoid stagnation.

A month or two back, I was watching some YouTube video on “Liverpool’s greatest Premier League goals against Manchester City” (this was just before the 4-1 hammering at the Etihad). As the goals were shown one by one, I kept trying to guess which season and game it was in.

There were important clues – whether Firmino wore 9 or 11, whether Mane wore 19 or 10, the identity of some players, the length of Trent Alexander Arnold’s hair, my memory of the scoreline from that game, etc. (Liverpool always wear the home Red at the Etihad, so the colour of the away kit wasn’t a clue).

However, for one goal I simply wasn’t able to figure out which season it was. There was TAA wearing 66, Fabinho, Henderson, the fab front three (Firmino-Mane-Salah, wearing 9-10-11 respectively) and Robertson. That’s when it hit me that for a fairly long time, a large part of Liverpool’s team had stayed constant! There was very little change at the club.

Now, there are benefits to having a consistently settled team (as the fabulous 2021-22 season showed), but there is also the danger of stasis. In something like football where careers are short, you don’t want the whole team “getting old together”. In the corporate world, people can get into too much of a comfort zone. And cynicism can set in.

Good new employees are always buzzing with ideas, fearless about what has been rejected before and who thinks how. As people spend longer in the organisation, though, colleagues become predictable and certain ways of doing things become institutionalised. Sooner than you know it, you would have become a “company man”, (figuratively) wearing the same white shirt and blue suits as your fellow company men, and socialising with your colleagues at the (figurative) company club.

There can be different kinds of companies here – some companies allow people to retain a lot of their individuality; and there the “decay” into company-manhood is slower. In this kind of a place, the same set of people can stay together for longer and still continue to innovate and add significant value to one another.

Other companies are less forgiving, and you very quickly assimilate, and lose part of your idiosyncrasy. Insofar as innovation comes out of fresh ideas and thinking and unusual connections, these companies are not very good at it. And in such companies, pretty much the only way to keep the innovative wheel going and continue to add value is by bringing in fresh blood well-at-a-faster-rate.

Putting it another way, if you are a cohesive kind of company, some attrition may not actually be a bad thing (unless you are growing rapidly enough to expand your team rapidly). To grow and innovate, you need people to think different.

And you get there either by having the sort of superior culture where existing employees continue to think different long after they’ve been exposed to one another’s thoughts; or by continuing to bring in fresh employees.

There is no other way.

Round Tables

One of the “features” of being in a job is that you get invited to conferences and “industry events”. I’ve written extensively about one of them in the past – the primary purpose of these events is for people to be able to sell their companies’ products, their services and even themselves (job-hunting) to other attendees.

Now, everyone knows that this is the purpose of these events, but it is one of those things that is hard to admit. “I’m going to this hotel to get pitched to by 20 vendors” is not usually a good enough reason to bunk work. So there is always a “front” – an agenda that makes it seemingly worthy for people to attend these events.

The most common one is to have talks. This can help attract people at two levels. There are some people who won’t attend talks unless they have also been asked to talk, and so they get invited to talk. And then there are others who are happy to just attend and try to get “gyaan”, and they get invited as the audience. The other side of the market soon appears, paying generous dollars to hold the event at a nice venue, and to be able to sell to all the speakers and the audience.

Similarly, you have panel discussions. Organisers in general think this is one level better than talks – instead of the audience being bored by ONE person for half an hour, they are bored by about 4-5 people (and one moderator) for an hour. Again there is the hierarchy here – some people won’t want to attend unless they have been put on the panel. And who gets to be on the panel is a function of how desperate one or more sponsors is to sell to the potential panelists.

The one thing most of these events get right is to have sufficient lunch and tea breaks for people to talk to each other. Then again, these are brilliant times for sponsors to be able to sell their wares to the attendees. And it has the positive externality that people can meet and “network” and talk among themselves – which is the best value you can get out of an event like this one.

However, there is one kind of event that I’ve attended a few times, but I can’t understand how they work. This is the “round table”. It is basically a closed room discussion with a large number of invited “panellists”, where everyone just talks past each other.

Now, at one level I understand this – this is a good way to get a large number of people to sell to without necessarily putting a hierarchy in terms of “speakers” / “panellists” and “audience”. The problem is that what they do with these people is beyond my imagination.

I’ve attended two of these events – one online and one offline. The format is the same. There is a moderator who goes around the table (not necessarily in any particular order), with one question to each participant (the better moderators would have prepared well for this). And then the participant gives a long-winded answer to that question, and the answer is not necessarily addressed at any of the other participants.

The average length of each answer and the number of participants means that each participant gets to speak exactly once. And then it is over.

The online version of this was the most underwhelming event I ever attended – I didn’t remember anything from what anyone spoke, and assumed that the feeling was mutual. I didn’t even bother checking out these people on LinkedIn after the event was over.

The offline version I attended was better in the way that at least we could get to talk to each other after the event. But the event itself was rather boring – I’m pretty sure I bored everyone with my monologue when it was my turn, and I don’t remember anything that anyone else said in this event. The funny thing was – the event wasn’t recorded, and there was hardly anyone from the organising team at the discussion. There existed just no point of all of us talking for so long. It was like people who organise Satyanarayana Poojes to get an excuse to have a party at home.

I’m wondering how this kind of event can be structured better. I fully appreciate the sponsors and their need to sell to the lot of us. And I fully appreciate that it gives them more bang for the buck to have 20 people of roughly equal standing to sell to – with talks or panels, the “potential high value customers” can be fewer.

However – wouldn’t it be far more profitable to them to be able to spend more time actually talking to the lot of us and selling, rather than getting all of us to waste time talking nonsense to each other? Like – maybe just a party or a “lunch” would be better?

Then again – if you want people to travel inter-city to attend this, a party is not a good enough excuse for people to get their employers to sponsor their time and travel. And so something inane like the “round table” has to be invented.

PS: There is this school of thought that temperatures in offices and events are set at a level that is comfortable for men but not for women. After one recent conference I attended I have a theory on why this is the case. It is because of what is “acceptable formal wear” for men and women.

Western formal wear for men is mostly the suit, which means dressing up in lots of layers, and maybe even constraining your neck with a tie. And when you are wearing so many clothes, the environment better be cool else you’ll be sweating.

For women, however, formal wear need not be so constraining – it is perfectly acceptable to wear sleeveless tops, or dresses, for formal events. And the temperatures required to “air” the suit-wearers can be too cold for women.

At a recent conference I was wearing a thin cotton shirt and could thus empathise with the women.

Shrinking deadlines

I’m reminded of this old joke/riddle, which also happened to feature in Gowri Ganesha. “If a 1 metre long sari takes 1 hour to dry in the sun, how long will and 8 metre long sari take to dry?”.

The instinctive answer, of course, is 8 hours, while if you think about it (and assume that you have enough clothesline space to not need to fold), the correct answer is likely to be 1 hour.

Now this riddle is completely unconnected to do with the point of the post, except that both have to do with time.

And then one day you find, ten years have got behind you.
No one told you when to run. You missed the starting gun.

Ok enough distractions. I’m now home, home again.

Modern workspaces are synonymous with tight deadlines. Even when you give a conservative estimate on how long something will take, you get asked to compress the timelines further. If you protest too much and say that there is a lot to be done, sometimes you might get asked to “put one more person on the job and get it done quickly”.

This might work for routine, or “fighter” jobs – for example, if your job is to enter and copy data for (let’s say) 1000 records, you can easily put another person on the job, and the entire job will be done in about half the time (allowing for a little time for the new person to learn the job and for coordination).

As the job gets more complex, the harder it gets. At one level, there is more time to be spent by the new person coming into the job. Then, as the job gets more complex, it gets harder to divide and conquer, or to “specialise”. This means there is lesser impact to the new person coming in.

And then when you get closer and closer to the stud end of the spectrum, the advantage of putting more people to get the work done faster get lesser and lesser. There comes a point when the extra person actively becomes a liability. Again – I’m reminded of my childhood when occasionally I would ask my mother if she needed help in cooking. “Yes, the best way for you to help is for you to stay out of the kitchen”, she would say.

And then when the job gets really creative, there is a further limit on compression – a lot of the work is done “offline”. I keep telling people about how I finally discovered the proof of Ramsey’s numbers (3,3) while playing table tennis in my hostel, or how I had solved a tough assignment problem while taking a friend’s new motorcycle for a ride.

When you want to solve problems “offline” (to let the insight come to you rather than going hunting for it – I had once written about this) – there is no way to shorten the process. You need to let the problem stew in your head, and hope that some time it will get solved.

There is nothing that can be done here. The more you hurry up, the less the chances you give yourself of solving the problem. Everything needs to take its natural course.

I got reminded of it when we missed a deadline last Friday, and I decided to not think about it through the weekend. And then, an hour before I got to work on Monday, an idea occurred in the shower which fixed the problem. Even if I’d stressed myself (and my team) out on Friday, or done somersaults, the problem would not have been solved.

As I’d said in 2004, quality takes time.

Average skill and peak skill

One way to describe how complex a job is is to measure the “average level of skill” and “peak level of skill” required to do the job. The more complex the job is, the larger this difference is. And sometimes, the frequency at which the peak level of skill is required can determine the quality of people you can expect to attract to the job.

Let us start with one extreme – the classic case of someone turning screws in a Ford factory. The design has been done so perfectly and the assembly line so optimised that the level of skill required by this worker each day is identical. All he/she (much more likely a he) has to do is to show up at the job, stand in the assembly line, and turn the specific screw in every single car (or part thereof) that passes his way.

The delta between the complexity of the average day and the “toughest day” is likely to be very low in this kind of job, given the amount of optimisation already put in place by the engineers at the factory.

Consider a maintenance engineer (let’s say at an oil pipeline) on the other hand. On most days, the complexity required of the job is very close to zero, for there is nothing much to do. The engineer just needs to show up and potter around and make a usual round of checks and all izz well.

On a day when there is an issue however, things are completely different – the engineer now needs to identify the source of the issue, figure out how to fix it and then actually put in the fix. Each of this is an insanely complex process requiring insane skill. This maintenance engineer needs to be prepared for this kind of occasional complexity, and despite the banality of most of his days on the job, maintain the requisite skill to do the job on these peak days.

In fact, if you think of it, a lot of “knowledge” jobs, which are supposed to be quite complex, actually don’t require a very high level of skill on most days. Yet, most of these jobs tend to employ people at a far higher skill level than what is required on most days, and this is because of the level of skill required on “peak days” (however you define “peak”).

The challenge in these cases, though, is to keep these high skilled people excited and motivated enough when the job on most days requires pretty low skill. Some industries, such as oil and gas, resolve this issue by paying well and giving good “benefits” – so even an engineer who might get bored by the lack of work on most days stays on to be able to contribute in times when there is a problem.

The other way to do this is in terms of the frequency of high skill days – if you can somehow engineer your organisation such that the high skilled people have a reasonable frequency of days when high skills are required, then they might find more motivation. For example, you might create an “internal consulting” team of some kind – they are tasked with performing a high skill task across different teams in the org. Each time this particular high skill task is required, the internal consulting team is called for. This way, this team can be kept motivated and (more importantly, perhaps) other teams can be staffed at a lower average skill level (since they can get help on high peak days).

I’m reminded of my first ever real taste of professional life – an internship in an investment bank in London in 2005. That was the classic “high variance in skills” job. Having been tested on fairly extreme maths and logic before I got hired, I found that most of my days were spent just keying in numbers in to an Excel sheet to call a macro someone else had written to price swaps (interest rate derivatives).

And being fairly young and immature, I decided this job is not worth it for me, and did not take up the full time offer they made me. And off I went on a rather futile “tour” to figure out what kind of job has sufficient high skill work to keep me interested. And then left it all to start my own consultancy (where others would ONLY call me when there was work of my specialty; else I could chill).

With the benefit of hindsight (and having worked in a somewhat similar job later in life), though, I had completely missed the “skill gap” (delta between peak and average skill days) in my internship, and thus not appreciated why I had been hired for it. Also, that I spent barely two months in the internship meant I didn’t have sufficient data to know the frequency of “interesting days”.

And this is why – most of your time might be spent in writing some fairly ordinary code, but you will still be required to know how to reverse a red-black tree.

Most of your time might be spent in writing SQL queries or pulling some averages, but on the odd day you might need to know that a chi square test is the best way to test your current hypothesis.

Most of your time might be spent in managing people and making sure the metrics are alright, but on the odd day you might have to redesign the process at the facility that you are in charge of.

In most complex jobs, the average day is NOT similar to the most complex day by any means. And thus the average day is NOT representative of the job. The next time someone I’m interviewing asks me what my “average day looks like”, I’ll maybe point that person to this post!

Chat GPT and Information Content

For the last week or so I’ve been fooling around with Chat GPT. It’s been a lot of fun, if you can endure the verbosity of its answers. Yesterday, at work, I used it for tech support when some people I was talking to were using too much jargon.

Before I came home from work yesterday, my last conversation was with a couple of recruiters, about a role that I’m hiring for. So when I got home, on a whim, I wondered if I could check out of Chat GPT could write job descriptions for me.

I asked:

write a job description for a senior data scientist at India’s largest logistics company. 4-7 years exp. Needs to have experience in ML, OR and statistics, and be able to work close to business and on ambiguous problems

It answered (pasting in full since the screenshot above is limited):

Job Title: Senior Data Scientist

Company: India’s Largest Logistics Company

Location: India

Experience: 4-7 years

We are seeking an experienced and highly skilled Senior Data Scientist to join our team at India’s largest logistics company. In this role, you will be responsible for using machine learning, operations research, and statistics to drive business insights and decision making.

As a Senior Data Scientist, you will have a deep understanding of data science techniques and technologies, and will be able to apply them to complex and ambiguous problems. You will be able to work closely with business teams to understand their needs and to develop solutions that drive value for the company.

To be successful in this role, you should have at least 4-7 years of experience in data science, and should have a strong background in machine learning, operations research, and statistics. You should be able to work independently and be comfortable working on complex and ambiguous problems.

Responsibilities:

Use machine learning, operations research, and statistics to drive business insights and decision making

Work closely with business teams to understand their needs and develop solutions

Develop and maintain data pipelines and systems

Collaborate with other data scientists and cross-functional teams to develop and implement solutions

Qualifications:

Bachelor’s or Master’s degree in a quantitative field, such as computer science, statistics, or engineering

At least 4-7 years of experience in data science

Strong background in machine learning, operations research, and statistics

Ability to work independently and on complex and ambiguous problems

Excellent communication and collaboration skills

Experience with Python, R, or other programming languages is a plus

We offer a competitive salary and benefits package, as well as opportunities for growth and advancement. If you are an experienced data scientist with a passion for driving business insights and decision making, we encourage you to apply.

In the past when I’ve been hiring, I’ve been a bit annoyed by people asking for job descriptions. I’ve largely found them to be fluff and boilerplate, and I’ve tried to get away with a sentence or two. However, due to the insistence of recruiters and candidates alike, I’ve ended up writing elaborate job descriptions. I don’t need to any more.

This post, however, is not about hiring (but yeah – I am hiring, and the above job description is reasonably accurate). It’s about information content in general.

Among other things, this tells you how little information content there usually is in job descriptions!

— Karthik S (@karthiks) December 7, 2022

Traditionally, information content has been measured in bits. A bit is the amount of information contained in an equi-probable binary event.

Sometimes when we find that someone is too verbose and using too many words when fewer would suffice, we say that their bit rate is low. We also use “low bit rate” to describe people such as former Prime Minister Atal Behari Vajpayee, who would speak incredibly slowly.

However, beyond the bit, which is a fairly technical concept, it has been difficult to quantify information content. Sometimes you read an article or a story and find that there is nothing much to it. But given the natural language, and the context of various words, it is impossible to quantify the information content.

Now, with Chat GPT, maybe it becomes a bit easier (though one would need a “reverse chat GPT algo”, to find the set of prompts required for Chat GPT to churn out a particular essay). Above, for example, I’ve shown how much fluff there generally is to the average job description – a fairly short prompt generated this longish description that is fairly accurate.

So you can define the information content of a piece or essay in terms of the number of words in the minimum set of prompts required for Chat GPT (or something like it) to come up with it. If you are a boring stereotypical writer, the set of prompts required will be lower. If you are highly idiosyncratic, then you will need to give a larger number of prompts for Chat GPT to write like you. You know where I’m going.

This evening, in office, a colleague commented that now it will be rather easy to generate marketing material. “Even blogs might become dead, since with a few prompts you can get that content”, he said (it can be a legit service to build off the Chat GPT API to take a tweet and convert it into an essay).

I didn’t tell him then but I have decided to take it up as a challenge. I consider myself to be a fairly idiosyncratic writer, which means I THINK there is a fair bit of information content in what I write, and so this blog will stay relevant. Let’s see how it goes.

PS: I still want to train a GAN on my blog (well over a million words, at last count) and see how it goes. If you know of any tools I can use for this, let me know!

Heads of departments

Recently I was talking to someone about someone else. “He got an offer to join XXXXXX as CTO”, the guy I was talking to told me, “but I told him not to take it. Problem with CTO role is that you just stop learning and growing. Better to join a bigger place as a VP”.

The discussion meandered for a couple of minutes when I added “I feel the same way about being head of analytics”. I didn’t mention it then (maybe it didn’t flash), but this was one of the reasons why I lobbied for (and got) taking on the head of data science role as well.

I sometimes feel lonely in my job. It is not something anyone in my company can do anything about. The loneliness is external – I sometimes find that I don’t have too many “peers” (across companies). Yes, I know a handful of heads of analytics / data science across companies, but it is just that – a handful. And I can’t claim to empathise with all of them (and I’m sure the feeling is mutual).

Irrespective of the career path you have chosen, there comes a point in your career where your role suddenly becomes “illiquid”. Within your company, you are the only person doing the sort of job that you are doing. Across companies, again, there are few people who do stuff similar to what you do.

The kind of problems they solve might be different. Different companies are structured differently. The same role name might mean very different things in very different places. The challenges you have to face daily to do your job may be different. And more importantly, you might simply be interested in doing different things.

And the danger that you can get into when you get into this kind of a role is that you “stop growing”. Unless you get sufficient “push from below” (team members who are smarter than you, and who are better than you on some dimensions), there is no natural way for you to learn more about the kind of problems you are solving (or the techniques). You find that your current level is more than sufficient to be comfortable in your job. And you “put peace”.

And then one day you find ten years have got behind youNo one told you when to run, you missed the starting gun

(I want you to now imagine the gong sound at the beginning of “Time” playing in your ears at this point in the blogpost)

One thing I tell pretty much everyone I meet is that my networking within my own industry (analytics and data science) is shit. And this is something I need to improve upon. Apart from the “push from below” (which I get), the only way to continue to grow in my job is to network with peers and learn from them.

The other thing is to read. Over the weekend I snatched the new iPad (which my daughter had been using; now she has got my wife’s old Macbook Air) and put all my favourite apps on it. I feel like I’m back in 2007 again, subscribing to random blogs (just that most of them are on substack now, rather than on Blogspot or Livejournal or WordPress), in the hope that I will learn. Let me see where this takes me.

And maybe some people decide that all this pain is simply not worth it, and choose to grow by simply becoming more managerial, and “building an empire”.

George Mallory and Metrics

It is not really known if George Mallory actually summited the Everest in 1924 – he died on that climb, and his body was only found in 1999 or so. It wasn’t his first attempt at scaling the Everest, and at 37, some people thought he was too old to do so.

There is this popular story about Mallory that after one of his earlier attempts at scaling the Everest, someone asked him why he wanted to climb the peak. “Because it’s there”, he replied.

George Mallory (extreme left) and companions

In the sense of adventure sport, that’s a noble intention to have. That you want to do something just because it is possible to do it is awesome, and can inspire others. However, one problem with taking quotes from something like adventure sport, and then translating it to business (it’s rather common to get sportspeople to give “inspirational lectures” to business people) is that the entire context gets lost, and the concept loses relevance.

Take Mallory’s “because it’s there” for example. And think about it in the context of corporate metrics. “Because it’s there” is possibly the worst reason to have a metric in place (or should we say “because it can be measured?”). In fact, if you think about it, a lot of metrics exist simply because it is possible to measure them. And usually, unless there is some strong context to it, the metric itself is meaningless.

For example, let’s say we can measure N features of a particular entity (take N = 4, and the features as length, breadth, height and weight, for example). There will be N! was in which these metrics can be combined, and if you take all possible arithmetic operations, the number of metrics you can produce from these basic N metrics is insane. And you can keep taking differences and products and ratios ad infinitum, so with a small number of measurements, the number of metrics you can produce is infinite (both literally and figuratively). And most of them don’t make sense.

That doesn’t normally dissuade our corporate “measurer”. That something can be measured, that “it’s there”, is sometimes enough reason to measure something. And soon enough, before you know it, Goodhart’s Law would have taken over, and that metric would have become a target for some poor manager somewhere (and of course, soon ceases to be a metric itself). And circular logic starts from there.

That something can be measured, even if it can be measured highly accurately, doesn’t make it a good metric.

So what do we do about it? If you are in a job that requires you to construct or design or make metrics, how can you avoid the “George Mallory trap”?

Long back when I used to take lectures on logical fallacies, I would have this bit on not mistaking correlation for causation. “Abandon your numbers and look for logic”, I would say. “See if the pattern you are looking at makes intuitive sense”.

I guess it is the same for metrics. It is all well to describe a metric using arithmetic. However, can you simply explain it in natural language, and can the listener easily understand what you are saying? And more importantly, does that make intuitive sense?

It might be fashionable nowadays to come up with complicated metrics (I do that all the time), in the hope that it will offer incremental benefit over something simpler, but more often than not the difficulty in understanding it makes the additional benefit moot. It is like machine learning, actually, where sometimes adding features can improve the apparent accuracy of the model, while you’re making it worse by overfitting.

So, remember that lessons from adventure sport don’t translate well to business. “Because it’s there” / “because it can be measured” is absolutely NO REASON to define a metric.

Speed, Accuracy and Shannon’s Channel Coding Theorem

I was probably the CAT topper in my year (2004) (they don’t give out ranks, only percentiles (to two digits of precision), so this is a stochastic measure). I was also perhaps the only (or one of the very few) person to get into IIMs that year despite getting 20 questions wrong.

It had just happened that I had attempted far more questions than most other people. And so even though my accuracy was rather poor, my speed more than made up for it, and I ended up doing rather well.

I remember this time during my CAT prep, where the guy who was leading my CAT factory once suggested that I was making too many errors so I should possibly slow down and make fewer mistakes. I did that in a few mock exams. I ended up attempting far fewer questions. My accuracy (measured as % of answers I got wrong) didn’t change by much. So it was an easy decision to forget above accuracy and focus on speed and that served me well.

However, what serves you well in an entrance exam need not necessarily serve you well in life. An exam is, by definition, an artificial space. It is usually bounded by certain norms (of the format). And so, you can make blanket decisions such as “let me just go for speed”, and you can get away with it. In a way, an exam is a predictable space. It is a caricature of the world. So your learnings from there don’t extend to life.

In real life, you can’t “get away with 20 wrong answers”. If you have done something wrong, you are (most likely) expected to correct it. Which means, in real life, if you are inaccurate in your work, you will end up making further iterations.

Observing myself, and people around me (literally and figuratively at work), I sometimes wonder if there is a sort of efficient frontier in terms of speed and accuracy. For a given level of speed and accuracy, can we determine an “ideal gradient” – on which way a person needs to move in order to make the maximum impact?

Once in a while, I take book recommendations from academics, and end up reading (rather, trying to read) academic books. Recently, someone had recommended a book that combined information theory and machine learning, and I started reading it. Needless to say, within half a chapter, I was lost, and I had abandoned the book. Yet, the little I read performed the useful purpose of reminding me of Shannon’s channel coding theorem.

Paraphrasing, what it states is that irrespective of how noisy a channel is, using the right kind of encoding and redundancy, we will be able to predictably send across information at a certain maximum speed. The noisier the channel, the more the redundancy we will need, and the lower the speed of transmission.

In my opinion (and in the opinions of several others, I’m sure), this is a rather profound observation, and has significant impact on various aspects of life. In fact, I’m prone to abusing it in inexact manners (no wonder I never tried to become an academic).

So while thinking of the tradeoff between speed and accuracy, I started thinking of the channel coding theorem. You can think of a person’s work (or “working mind”) as a communication channel. The speed is the raw speed of transmission. The accuracy (rather, the lack of it) is a measure of noise in the channel.

So the less accurate someone is, the more the redundancy they require in communication (or in work). For example, if you are especially prone to mistakes (like I am sometimes), you might need to redo your work (or at least a part of it) several times. If you are the more accurate types, you need to redo less often.

And different people have different speed-accuracy trade-offs.

I don’t have a perfect way to quantify this, but maybe we can think of “true speed of work” by dividing the actual speed in which someone does a piece of work by the number of iterations they need to get it right. OK it is not so straightforward (there might be other ways to build redundancy – like getting two independent people to do the same thing and then tally the numbers), but I suppose you get the drift.

The interesting thing here is that the speed and accuracy is not only depend on the person but the nature of work itself. For me, a piece of work that on average takes 1 hour has a different speed-accuracy tradeoff compared to a piece of work that on average takes a day (usually, the more complicated and involved a piece of analysis, the more the error rate for me).

In any case, the point to be noted is that the speed-accuracy tradeoff is different for different people, and in different contexts. For some people, in some contexts, there is no point at all in expecting highly accurate work – you know they will make mistakes anyways, so you might as well get the work done quickly (to allow for more time to iterate).

And in a way, figuring out speed-accuracy tradeoffs of the people who work for you is an important step in getting the best out of them.

Financial ratio metrics

It’s funny how random things stick in your head a couple of decades later. I don’t even remember which class in IIMB this was. It surely wasn’t an accounting or a finance class. But it was one in which we learnt about some financial ratios.

I don’t even remember what exactly we had learnt that day (possibly return on invested capital?). I think it was three different financial metrics that can be read off a financial statement, and which then telescope very nicely together to give a fourth metric. I’ve forgotten the details, but I remember the basic concepts.

A decade ago, I used to lecture frequently on how NOT to do data analytics. I had this standard lecture that I called “smelling bullshit” that dealt with common statistical fallacies. Things like correlation-causation, or reasoning with small samples, or selection bias. Or stocks and flows.

One set of slides in that lecture was about not comparing stocks and flows. Most people don’t internalise it. It even seems like you cannot get a job as a journalist if you understand the distinction between stocks and flows. Every other week you see comparisons of someone’s net worth to some country’s GDP, for example. Journalists make a living out of this.

In any case, whenever I would come to these slides, there would always be someone in the audience with a training in finance who would ask “but what about financial ratios? Don’t we constantly divide stocks and flows there?”

And then I would go off into how we would divide a stock by a flow (typically) in finance, but we never compared a stock to a flow. For example, you can think of working capital as a ratio – you take the total receivables on the balance sheet and divide it by the sales in a given period from the income statement, to get “days of working capital”. Note that you are only dividing, not comparing the sales to the receivables. And then you take this ratio (which has dimension “days”) and then compare it across companies or across regions to do your financial analysis.

If you look at financial ratios, a lot of them have dimensions, though sometimes you don’t really notice it (I sometimes say “dimensional analysis is among the most powerful tools in data science”). Asset turnover, for example, is sales in a period divided by assets and has the dimension of inverse time. Inventory (total inventory on BS divided by sales in a period) has a dimension of time. Likewise working capital. Profit margins, however, are dimensionless.

In any case, the other day at work I was trying to come up with a ratio for something. I kept doing gymnastics with numbers on an excel sheet, but without luck. And I had given up.

Nowadays I have started taking afternoon walks at office (whenever I go there), just after I eat lunch (I carry a box of lunch which I eat at my desk, and then go for a walk). And on today’s walk (or was it Tuesday’s?) I realised the shortcomings in my attempts to come up with a metric for whatever I was trying to measure.

I was basically trying too hard to come up with a dimensionless metric and kept coming up with some nonsense or the other. Somewhere during my walk, I thought of finance, and financial metrics. Light bulb lit up.

My mistake had been that I had been trying to come up with something dimensionless. The moment I realised that this metric needs to involve both stocks and flows, I had it. To be honest, I haven’t yet come up with the perfect metric (this is for those colleagues who are reading this and wondering what new metric I’ve come up with), but I’m on my way there.

Since both a stock and a flow need to be measured, the metric is going to be a ratio of both. And it is necessarily going to have dimensions (most likely either time or inverse time).

And if I think about it (again I won’t be able to give specific examples), a lot of metrics in life will follow this pattern – where you take a stock and a flow and divide one by the other. Not just in finance, not just in logistics, not just in data science, it is useful to think of metrics that have dimensions, and express them using those dimensions.

Some product manager (I have a lot of friends in that profession) once told me that a major job of being a product manager is to define metrics. Now I’ll say that dimensional analysis is the most fundamental tool for a product manager.

A day at an award function

So I got an award today. It is called “exemplary data scientist”, and was given out by the Analytics India Magazine as part of their MachineCon 2022. I didn’t really do anything to get the award, apart from existing in my current job.

I guess having been out of the corporate world for nearly a decade, I had so far completely missed out on the awards and conferences circuit. I would see old classmates and colleagues put pictures on LinkedIn collecting awards. I wouldn’t know what to make of it when my oldest friend would tell me that whenever he heard “eye of the tiger”, he would mentally prepare to get up and go receive an award (he got so many I think). It was a world alien to me.

Parallelly, I used to crib about how while I’m well networked in India, and especially in Bangalore, my networking within the analytics and data science community is shit. In a way, I was longing for physical events to remedy this, and would lament that the pandemic had killed those.

So I was positively surprised when about a month ago Analytics India Magazine wrote to me saying they wanted to give me this award, and it would be part of this in-person conference. I knew of the magazine, so after asking around a bit on legitimacy of such awards and looking at who had got it the last time round, I happily accepted.

Most of the awardees were people like me – heads of analytics or data science at some company in India. And my hypothesis that my networking in the industry was shit was confirmed when I looked at the list of attendees – of 100 odd people listed on the MachineCon website, I barely knew 5 (of which 2 didn’t turn up at the event today).

Again I might sound like a n00b, but conferences like today are classic two sided markets (read this eminently readable paper on two sided markets and pricing of the same by Jean Tirole of the University of Toulouse). On the one hand are awardees – people like me and 99 others, who are incentivised to attend the event with the carrot of the award. On the other hand are people who want to meet us, who will then pay to attend the event (or sponsor it; the entry fee for paid tickets to the event was a hefty $399).

It is like “ladies’ night” that pubs have, where on a particular days of the week, women who go to the pub get a free drink. This attracts women, which in turn attracts men who seek to court the women. And what the pub spends in subsidising the women it makes back in terms of greater revenue from the men on the night.

And so it was at today’s conference. I got courted by at least 10 people, trying to sell me cloud services, “AI services on the cloud”, business intelligence tools, “AI powered business intelligence tools”, recruitment services and the like. Before the conference, I had received LinkedIn requests from a few people seeking to sell me stuff at the conference. In the middle of the conference, I got a call from an organiser asking me to step out of the hall so that a sponsor could sell to me.

I held a poker face with stock replies like “I’m not the person who makes this purchasing decision” or “I prefer open source tools” or “we’re building this in house”.

With full benefit of hindsight, Radisson Blu in Marathahalli is a pretty good conference venue. An entire wing of the ground floor of the hotel is dedicated for events, and the AIM guys had taken over the place. While I had not attended any such event earlier, it had all the markings of a well-funded and well-organised event.

As I entered the conference hall, the first thing that struck me was the number of people in suits. Most people were in suits (though few wore ties; And as if the conference expected people to turn up in suits, the goodie bag included a tie, a pair of cufflinks and a pocket square). And I’m just not used to that. Half the days I go to office in shorts. When I feel like wearing something more formal, I wear polo T-shirts with chinos.

My colleagues who went to the NSE last month to ring the bell to take us public all turned up company T-shirts and jeans. And that’s precisely what I wore to the conference today, though I had recently procured a “formal uniform” (polo T-shirt with company logo, rather than my “usual uniform” which is a round neck T-shirt). I was pretty much the only person there in “uniform”. Towards the end of the day, I saw one other guy in his company shirt, but he was wearing a blazer over it!

Pretty soon I met an old acquaintance (who I hadn’t known would be at the conference). He introduced me to a friend, and we went for coffee. I was eating a cookie with the coffee, and had an insight – at conferences, you should eat with your left hand. That way, you don’t touch the food with the same hand you use to touch other people’s hands (surprisingly I couldn’t find sanitiser dispensers at the venue).

The talks, as expected, were nothing much to write about. Most were by sponsors selling their wares. The one talk that wasn’t by a sponsor was delivered by a guy who was introduced as “his greatgrandfather did this. His grandfather did that. And now this guy is here to talk about ethics of AI”. Full Challenge Gopalakrishna feels happened (though, unfortunately, the Kannada fellows I’d hung out with earlier that day hadn’t watched the movie).

I was telling some people over lunch (which was pretty good) that talking about ethics in AI at a conference has become like worshipping Ganesha as part of any elaborate pooja. It has become the de riguer thing to do. And so you pay obeisance to the concept and move on.

Ok so i'm really really annoyed by all this focus on "ethical AI". It is just driven by "social desirability bias". Talking about ethical AI seemingly makes you look ethical so people talk about it. fund research in it. etc.

— Karthik S (@karthiks) March 27, 2022

The awards function had three sections. The first section was for “users of AI” (from what I understood). The second (where I was included) was for “exemplary data scientists”. I don’t know what the third was for (my wife is ill today so I came home early as soon as I’d collected my award), except that it would be given by fast bowler and match referee Javagal Srinath. Most of the people I’d hung out with through the day were in the Srinath section of the awards.

Overall it felt good. The drive to Marathahalli took only 45 minutes each way (I drove). A lot of people had travelled from other cities in India to reach the venue. I met a few new people. My networking in data science and analytics is still not great, but far better than it used to be. I hope to go for more such events (though we need to figure out how to do these events without that talks).

PS: Everyone who got the award in my section was made to line up for a group photo. As we posed with our awards, an organiser said “make sure all of you hold the prizes in a way that the Intel (today’s chief sponsor) logo faces the camera”. “I guess they want Intel outside”, I joked. It seemed to be well received by the people standing around me. I didn’t talk to any of them after that, though.