## Management and Verification

For those of you who are new here, my wife and I used to organise “NED Talks” in our home in Bangalore. The first edition happened in 2015 (organised on a whim), and encouraged by its success, we organised 10 more editions until 2019. We have put up snippets of some talks here.

In the second edition of the NED Talks (February 2015), we had a talk by V Vinay (noted computer scientist, former IISc professor, co-inventor of Simputer, co-founder of Strand Life Sciences, Ati Motors, etc. etc.), where he spoke about “computational complexity”.

Now, having studied computer science, “computational complexity” was not a new topic to me, but one thing that Vinay said has stayed with me – it is that verifying an algorithm is far more efficient than actually executing the algorithm.

To take a simple example, factorising a number into prime factors is NP Hard – in other words, it is a really hard problem. However, verifying the prime factorisation of a number is trivial – you can just multiply the factors and see if it gives back the number you started with.

I was thinking about this paradigm the ohter day when I was thinking about professional managers – several times in life I have wondered “how can this person manage this function when he/she has no experience in that function?”. Maybe it is because I had been subjected to two semesters of workshop in the beginning of my engineering, but I have intuitively assumed that you can only manage stuff that you have personally done – especially if it is a non-trivial / specialist role.

But then – if you think about it, at some level, management is basically about “verification”. To see whether you have done your work properly, I don’t need to precisely know how you have done it. All I need to know is whether you have done bullshit – which means, I don’t need to “replicate your algorithm”. I only need to “verify your algorithm”, which computer science tells us can be an order of magnitude simpler than actually building the algorithm.

The corollary of this is that if you have managed X, you need not be good at X, or actually even have done X. All it shows is that you know how to manage X, which can be an order of magnitude simple than actually doing X.

This also (rather belatedly) explains why I have largely been wary of hiring “pure managers” for my team. Unless they have been hands on at their work, I start wondering if they actually know how to do it, or only know how to manage it (and I’m rather hands on, and only hire hands on people).

And yet another corollary is that if you have spent too long just managing teams, you might have gotten so used to just verifying algorithms that you can’t write algorithms any more.

And yet another before I finish – computer science has a lot of lessons to offer life.

## Management watch

About a year back, a few months after I had started my current job, I was working late into the evening. I was sitting on the sofa with my laptop when my wife said, “you cannot call yourself senior management if you work like this”.

“What do you mean”, I asked.

“If you are truly senior management, you should not be using your computer after normal work hours. You should be doing everything using your phone. Do you remember, six months into my job at <@#R@#\$@@>, I would work late into the night, but only with my phone?”, she countered.

I had to admit this was a good point. More practically, in terms of work stuff, I started thinking about making dashboards and reports more mobile-friendly. I started questioning interactive dashboards – if they are aimed at top management, the latter largely see the stuff on their phones, so interactivity is full of fat fingers.

Of course, the nature of my job means that I can never truly be senior management by this metric – I’m generally  too hands on to be able to work exclusively on my phone. However, that hasn’t stopped me from evangelising this theory of my wife. The theory itself is strong enough.

Recently I’d met a former client. He was using an iPad as a work “laptop”. I told him the theory and that he has truly arrived. He said he had been given a choice of an iPad and a Surface –  basically his company has internalised how senior management ought to be treated.

While I can never be senior management by this metric, I’m in a way trying to leapfrog it. Recently I got myself an Apple Watch. Apart from other things, it gives me notifications for all my messages, and I can reply using the watch as well. And this is where the magic begins.

For starters, Apple offers this standard set of templatised replies you can use. Now, Apple being Apple (and not Google), these replies are not customised to the message that you get. It drives me nuts that there is an “OK” and a “Sure!” and a “No” but no “Yes”. If this template doesn’t work for you, you can actually type a message on the watch itself. My fingers are fat (and I wear my watch on my dominant hand), so this is not so useful for me. However, there is also a voice typing mode, and that is rather good. And that is where things get real.

The other day, I shut work early and went off for a walk (I like doing that). My team had not shut their work though, and they kept bombarding me with messages. And that is when I realised I could actually read their messages and REPLY TO THEM using my watch. Most of the messages were the template monosyllables. Sometimes I spoke into my watch (without breaking my stride), and let Apple’s excellent voice-to-text do the rest.

And so I have this new theory, which is an extension of my wife’s theory. The next level of senior management is to be able to get all your work done simply using your watch – not even needing your phone. Of course, limitations exist – only a few lines of text are shown for each email, and images don’t load, but it is only a matter of time before watches solve for this.

But then, I’ve discovered one massive downside of replying to messages using my watch – the tone. The template monosyllables are all come across as rude (or curt). And the voice-to-text means you don’t really have your filter on while typing, and you end up “writing as you would speak”, and that can’t be great as well.

The other day I was walking from our Michaelpalya office to our Binnamangala office, when I was bombarded with messages from someone. And without breaking my stride I replied to all the messages, speaking into my watch. I “wrote” as I would speak (complete with swearwords), and that turned out to be an incredibly rude set of messages I ended up sending (I apologised later that day when I saw what I’d “written” on my phone later).

So leapfrogging and trying to act too cool can sometimes come at a price.

## Why WFH is unsustainable

A couple of weekends back I decided to re-read Yuval Noah Harari’s Sapiens. Rather than digging into my kindle for the regular version (which I’d read in 2015), I decided to read the graphic novel instead.

I’d purchased a copy of it a few months back, and a month ago, my daughter had finished reading it (it was only after she finished reading that I realised the extent of the sex and violence in the book. anyways).

Since I was re-reading, there was nothing particularly new. It was just a refresher of everything I’d read and enjoyed back in 2015. And one of the things I read was something highly pertinent to what I’d been thinking about the preceding Friday – on gossip.

One of the key points that Harari makes in Sapiens is that what makes us sapiens sapiens is our ability to gossip. Many other animals communicate, but most of their communication is “necessary”. “Oh look, there’s a lion”, or “there is a dead elephant near the lake” types.

Homo sapiens is unique in that most of our conversation is, fundamentally speaking, rather unnecessary stuff. It is basically “gossip”. That we gossip, however, means that we evolved to have a far richer vocabulary. We communicate and bond a lot more. And we are able to create “shared fictions” that means it is far easier for us to cooperate with strangers. And that lets us do more. Then again – it all started with gossip.

This, I realised, is why I find working from home rather isolating. It’s been over a year since I got back to full time employment. There have been two waves of covid-19 after that. This has meant I’ve hardly been to office in this time. Yes, there have been spells when I’ve travelled, or spent a week at office, but they have been few and far in between.

Apart from collaboration with my team, work has been fine. However, what I realise I miss is the general “bonding” that you would come to expect when you work for a company. The problem is with remote work.

While chat (we use Google Chat; other companies use Slack or DBabble of Microsoft Teams or Discord) is good enough for most “quick communication”, the big problem is that everything you say is necessarily in writing. Yes, you can delete or modify, some messengers have disappearing messages and all that.

Yet, because you need to put everything in writing, you say less than you otherwise would. Most importantly, you think twice before you gossip. It takes a long time for pairs of people to build sufficient mutual trust to be able to gossip (and when I think of it, most of this kind of trust has developed through offline interactions). Even if I trust you, I’ll think maybe one and a half times before putting gossip in writing.

So prolonged period of remote work means work gets robbed of the core human element – gossip. And extending what Harari says in sapiens, when you gossip less, you believe in fewer shared fictions (though by definition all of you in your company believe in the fiction of the limited liability corporation). And you cooperate less.

I can’t wait to get back to office (planning in 2 weeks or so), and (hopefully) start gossiping again. It won’t be easy since so far I’ve largely been remote. However, if we can get a sustained period of office work going, we should be able to gossip and bond and be a little more human.

## Returns to experience and business school career choices

Go to any elite business school, especially one where the average years of pre-MBA industry experience is low, and ask students what they want to do. Most first year students will tell you that they either want to do “marketing” or “investment banking”. Second year students will still say this, but some will also say “consulting”.

With the benefit of a lot of hindsight (it’s nearly 16 years since I graduated from business school), there is definite merit in these being primary career choices for business school students – rather than other seemingly equally valid careers such as B2B sales, or product management, or not-for-profits, or data analytics, or logistics.

It has to do with reversibility, and “one-way doors”.

Different professions have different levels of “returns to experience”. In some professions, all that mattters is the total amount of contiguous experience you’ve had in that particular profession.

I figured this out the hard way, for example, in my brief flirtation with getting back to becoming a banking quant in 2017. I had left the profession (banking quant) in late 2011, to become an independent consultant. A series of financial services projects later, I wondered if I could get back to what I was doing earlier. Except that they wouldn’t have me back – all they cared about was that I had “been out of the industry for 5 years”, and what experience I got in those 5 years didn’t really matter.

In other words, investment banking is a “high returns to experience” industry, where your experience within the industry is highly valued, but anything outside is completely disregarded.

Marketing (though not “digital marketing”) is also similarly – your experience outside the field is not valued at all. So even if you look to get into consumer goods marketing at a later point of time in your career, you will most likely have to start right at the bottom, at an entry level position. All your years of experience doing something else are of no use here.

You notice a pattern (despite the small number of data points I’ve offered)? Popular out-of-business-school careers are professions with a high “perceived returns to experience”. The reason why so many business school students want to do marketing or investment banking is because they are irreversible choices. You either get in from school, or get in later on but start at the bottom anyway. So you might as well get in straight from school.

Technology and data and product management and B2B sales and corporate strategy and logistics and general management are all rather more forgiving – a large number of employers offering these jobs give adequate weightage to experience outside of the field as well. Which means it is easier to switch into these professions at a later point of time in one’s career.

Putting it another way, starting your career in a hard-to-enter (or “enter-at-bottom”) field is a risk-averse way of building your career. If you don’t like it, you can always move to a more welcoming career path. Start in a more welcoming place, and you’ll find it harder to move to a less welcoming career.

So that explains marketing and investment banking, but what about strategy consulting? Surely, strategy consulting should value diverse experience, for that will make you a better consultant? The difference here is between strategy consulting and “brand name strategy consulting”. If you work for a “brand name strategy consultant”, you’re not only offering your own advice – you are also offering advice on behalf of that firm.

This means, in order to do so, you need adequate training in the ways of the firm. And so there will always be (less than 100% of course) a discount on the rest of your experience – in order to learn the ways nad means of the firm that you are going to represent, you will need to start at a more junior level than your experience dictates. So once again you might as well get in right upfront, straight out of school.

So the next time a business school student tells you she wants to do marketing or investment banking or strategy consulting, don’t berate her for “being too cliched and not open minded enough”. She is just being rational, and playing the optionality in the way it should be.

## Legacy Metrics

Yesterday (or was it the day before? I’ve lost track of time with full time WFH now) the Times of India Bangalore edition had two headlines.

One was the Karnataka education minister BC Nagesh talking about deciding on school closures on a taluk (sub-district) wise basis. “We don’t want to take a decision for the whole state. However, in taluks where test positivity is more than 5%, we will shut schools”, he said.

That was on page one.

And then somewhere inside the newspaper, there was another article. The Indian Council for Medical Research has recommended that “only symptomatic patients should be tested for Covid-19”. However, for whatever reason, Karnataka had decided to not go by this recommendation, and instead decided to ramp up testing.

These two articles are correlated, though the paper didn’t say they were.

I should remind you of one tweet, that I elaborated about a few days back:

The reason why Karnataka has decided to ramp up testing despite advisory to the contrary is that changing policy at this point in time will mess with metrics. Yes, I stand by my tweet that test positivity ratio is a shit metric. However, with the government having accepted over the last two years that it is a good metric, it has become “conventional wisdom”. Everyone uses it because everyone else uses it.

And so you have policies on school shutdowns and other restrictive measures being dictated by this metric – because everyone else uses the same metric, using this “cannot be wrong”. It’s like the old adage that “nobody got fired for hiring IBM”.

ICMR’s message to cut testing of asymptomatic individuals is a laudable one – given that an overwhelming number of people infected by the incumbent Omicron variant of covid-19 have no symptoms at all. The reason it has not been accepted is that it will mess with the well-accepted metric.

If you stop testing asymptomatic people, the total number of tests will drop sharply. The people who are ill will get themselves tested anyways, and so the numerator (number of positive reports) won’t drop. This means that the ratio will suddenly jump up.

And that needs new measures – while 5% is some sort of a “critical number” now (like it is with p-values), the “critical number” will be something else. Moreover, if only symptomatic people are to be tested, the number of tests a day will vary even more – and so the positivity ratio may not be as stable as it is now.

All kinds of currently carefully curated metrics will get messed up. And that is a big problem for everyone who uses these metrics. And so there will be pushback.

Over a period of time, I expect the government and its departments to come up alternate metrics (like how banks have now come up with an alternative to LIBOR), after which the policy to cut testing for asymptomatic people will get implemented. Until then, we should bow to the “legacy metric”.

And if you didn’t figure out already, legacy metrics are everywhere. You might be the cleverest data scientist going around and you might come up with what you think might be a totally stellar metric. However, irrespective of how stellar it is, that people have to change their way of thinking and their process to process it means that it won’t get much acceptance.

The strategy I’ve come to is to either change the metric slowly, in stages (change it little by little), or to publish the new metric along with the old one. Depending on how clever the new metric is, one of the metrics will die away.

## Ronald Coase, Scott Adams and Intrapersonal Vertical Integration

I have a new HR policy. I call it “intrapersonal vertical integration”. Read on.

I

Back in the 193os, economist Ronald Coase wrote an article on “the nature of the firm” (the link is to Wikipedia, not to the actual paper). It was a description of why people form companies and partnerships and so on, rather than all being gig workers negotiating each piece of work.

The key concept here was one of transaction costs – if everyone were to be a freelancer, like I was between 2012 and 2020 (both included), then for every little piece of work there would need to be a piece of negotiation.

“Can you build this dashboard for me?”
“Yes. That would be \$10000”
“No, I’ll only pay \$2000”
“9000”
“3000 final”
“get lost”

During my long period of freelancing, I internalised this, and came up with a “minimum order value” – a reasonable amount which could account for transaction costs like the above (just as I write this, I’m changing videos on Youtube for my wife, and she’s asking me to put 30 second videos. And I’m refusing saying “too much transaction cost. I need my hands for something else (blogging)” ).

This worked out fine for the projects that I actually got, but transaction costs meant that a lot of the smaller deals never worked out. I lost out on potential revenue from those, and my potential clients lost out on work getting done.

So, instead, if I were to be part of a company, like I am now, transaction costs are far lower. Yes, we might negotiate on exact specifications, or deadlines, but price was a single negotiation at the time I joined the firm. And so a lot more work gets done – better for me and better for the company. And this is why companies exist. It might sound obvious, but Coase put it in a nice and elegant theoretical framework.

II

I’ve written about this several times on my blog – Scott Adams’s theory that there are two ways in which you can be really successful.

1. Become the best at one specific thing.
2. Become very good (top 25%) at two or more things.

This is advice that I have taken seriously, and I’ve followed the second path. Being the best at one specific thing is too hard, and too random as well – “the best” is a sort of a zero sum game. Instead, being very good in a few things is easier to do, and as I’d said in one of my other posts on this, being very good in uncorrelated things is a clear winner.

I will leave this here and come back later on in the post, like how Dasharatha gave some part of the mango to Sumitra (second in line), and then decided to come back to her later on in the distribution.

III

I came up with this random theory the other day on the purpose of product managers. This theory is really random and ill-formed, and I haven’t bothered discussing it with any real product managers.

The need for product managers comes from software engineers’ insistence on specific “system requirement specifications”.

I learnt software engineering in a formal course back in 2002. Back then, the default workflow for software engineering was the so-called “waterfall model”. It was a linear sequential thing where the first part of the process goes in clearly defining system requirement specifications. Then there would be an unambiguous “design document”. And only then would coding begin.

In that same decade (2000s), “agile” programming became a thing. This meant fast iterations and continuous improvements. Software would be built layer by layer. However, software engineers had traditionally worked only with precise specifications, and “ambiguous business rules” would throw them off. And so the role of the product manager was created – who would manage the software product in a way that they would interface with ambiguous business on one side, and precise software engineers on the other.

Their role was to turn ambiguity to certainty, and get work done. They would never be hands on – instead their job would be to give precise instructions to people who would be hands on.

I have never worked as either a software engineer or a product manager, but I don’t think I’d enjoy either job. On the one hand, I don’t like being given precise instructions, and instead prefer ambiguity. On the other, if I were to give precise instructions, I would rather use C++ or Python to give those instructions than English or Kannada. In other words, if I were to be precise in my communication, I would rather talk to a computer than to another human.

It possibly has to do with my work history. I spent a little over two years as a quant at a top tier investment bank. As part of the job, I was asked to write production code. I used to protest, saying writing C++ code wasn’t the best use of my time or effort. “But think about the effort involved in explaining your model to someone else”, the higher ups in the company would tell me. “Wouldn’t it be far easier to just code it yourself?”

IV

Coase reasoned that transaction costs are the reason why we need a firm. We don’t need frequent negotiations and transaction costs, so if people were to get together in the form of a firm, they could coordinate much better and get a lot more work done, with more value accruing to every party involve.

However, I don’t think Coase went far enough. Just putting people in one firm only eliminates one level of transaction costs – of negotiating conditions and prices. Even when you are in the same firm, coordinating with colleagues implies communication, and unless precise, the communication links can end up being the weak links in how much the firm can achieve.

Henry Ford’s genius was to recognise the assembly line (a literal conveyor belt) as a precise form of communication. The workers in his factories were pretty much automatons, doing their precise job, in the knowledge that everyone else was doing their own. The assembly line made communication simpler, and that allowed greater specialisation to unlock value in the firm – to the extent that each worker could get at least five dollars a day and the firm would still be profitable.

It doesn’t work so neatly in what can be classified as “knowledge industries”. Like with the product manager and the software engineer, there is a communication layer which, if it fails, can bring down the entire process.

And there are other transaction costs implied in this communication – let’s say you are building stuff that I need to build on to make the final product. Every time I think you need to build something slightly different, it involves a process of communication and negotiation. It involves the product manager to write a new section in the document. And when working on complex problems, this can increase the complexity multifold.

So we are back to Scott Adams (finally). Building on what I’d said before – you need to be “very good” at two or more things, and it helps if these things are uncorrelated (in terms of being able to add unique value). However, it is EVEN MORE USEFUL if the supposedly uncorrelated skills you have can be stacked, in a form of vertical integration.

In other words, if you are good at several things that are uncorrelated, where the output of one thing can be the input into another, you are a clear winner.

Adams, for example, is good at understanding business, he is funny and he can draw. The combination of the first two means that he can write funny business stories, and that he can also draw means he has created a masterpiece in the form of Dilbert.

Don’t get me wrong – you can have a genius storyteller and a genius artist come together to make great art (Goscinny and Uderzo, for example). However, it takes a lot of luck for a Goscinny to find his Uderzo, or vice versa. I haven’t read much Asterix but what I’m old by friends is that the quality dropped after Uderzo was forced to be his own Goscinny (after the latter died).

At a completely different level – I have possibly uncorrelated skills in understanding business and getting insight out of data. One dovetails into the other and so I THINK I’m doing well in business intelligence. If I were only good at business, and needed to keep asking someone to churn the data on each iteration, my output would be far far slower and poorer.

So I extend this idea into “intrapersonal vertical integration”. If you are good at two or more things, and one can lead into another, you have a truly special set of skills and can be really successful.

Putting it another way – in knowledge jobs, communication can be so expensive that if you can vertically integrate yourself across multiple jobs, you can add significant value even if you are not the best at each of the individual skills.

Finish

In knowledge work, communication is the weakest link, so the fewer levels of communication you have, the better and faster you can do your job. Even if you get the best for every level in your chain, the strength (or lack of it) of communication between them can mean that they produce suboptimal output.

Instead if you can get people who are just good at two or more things in the chain (rather than being the best at any one), you can add significantly better value.

Putting it another way, yes, I’m batting for bits-and-pieces players rather than genuine batsmen or bowlers. However, the difference between what I’m saying and cricket is that in cricket batting and bowling are not vertically integrated. If they were, bits and pieces players would work far far better.

The Downside

I’ve written about this before. While being good at uncorrelated things that dovetail into one another can be a great winning strategy, liquidity can be your enemy. That you are unique means that there aren’t too many like you. And so organisations may not want to bet too much on you – since you will be hard to replace. And decide to take the slack in communication and get specialists for each position instead.

PS:

I have written a book on transaction costs and liquidity. As it happens, today it is on display at the Bangalore Literature Festival.

## Why calls are disruptive to work

It is well known in my company that I don’t like phone calls. I mean – they are useful at times, but they have their time and place. For most normal office communication, it is far easier to do it using chat or mail, and less disruptive to your normal work day.

Until recently, I hadn’t been able to really articulate why phone calls (this includes Meet / Zoom / Teams / whatever) are disruptive to work, but recently had an epiphany when I was either drunk or hungover (can’t remember which now) during/after a recent company party.

Earlier that day, during the said party, one colleague (let’s call him C1) had told me about another colleague (let’s call him C2) and his (C2’s) penchant for phone calls. “Sometimes we would have written a long detailed document”, C1 said, “and then C2 will say, ‘I have to make one small point here. Can you please call me?’. He’s just the opposite of you”

I don’t know why after this I started thinking about circuit switching and packet switching. And then I realised why I hate random office calls.

Currently I use a Jio connection for my phone. The thing with Ji0 (and 4G in general, I think) is that it uses packet switching for phone calls – it uses the same data network for calls as well. This is different from earlier 2G (and 3G as well, if I’m not wrong) networks where calls were made on a different voice (circuit switching) network. Back then, if you got a call, your phone’s data connection would get interrupted – no packages could be sent because your phone was connected through a circuit. It was painful.

Now, with packet switching for phone calls as well, the call “packets” and the browsing “packets” can coexist and co-travel on the “pipes” connecting the phone to the tower and the wide world beyond. So you can take phone calls while still using data.

Phone calls in the middle of work disrupt work in exactly the same way.

The thing with chatting with someone while you’re working is that you can multitask. You send a message and by the time they reply you might have written a line of code, or sent another message to someone else. This means chatting doesn’t really disrupt work -it might slow down work (since you’re also doing work in smaller packets now), but your work goes on. Your other chats go on. You don’t put your life on hold because of this call.

A work phone call (especially if it has to be a video call) completely disrupts this network. Suddenly you have to give one person (or persons) at the end of the line your complete undivided attention. Work gets put on hold. Your other conversations get put on hold. The whole world slows down for you.

And once you hang up, you have the issue of gathering the context again on what you were doing and what you were thinking about and the context of different conversations (this is a serious problem for me). Everything gets disrupted. Sometimes it is even difficult to start working again.

I don’t know if this issue is specific to me because of my ADHD (and hence the issues in restarting work). Actually – ADHD leads to another problem. You might be hyper focussing on one thing at work, and when you get a call you are still hyper focussed on the same thing. And that means you can’t really pay attention to the call you are on, and can end up saying some shit. With chat / email, you don’t need to respond to everything immediately, so you can wait until the hyper focus is over!

In any case, I’m happy that I have the reputation I have, that I don’t like doing calls and prefer to do everything through text. The only downside I can think of of this is that you have to put everything in writing.

PSA: Google Calendar now allows you to put “focus time” on your own calendar. So far I haven’t used it too much but plan to use it more in the near future.

## Pipe jobs

Sangeet Paul Choudary, my friend from business school, became a global business guru essentially based on one idea – that businesses can either be “platforms” or “pipes”, and that a business that is a platform can add far more value than a business that is just a pipe.

If I think about it, I currently work for a company that can be best described as a pipe (rather than a platform) and I think it’s doing quite well. From that perspective, though a platform business can be more successful it’s possible to build a good pipe business as well.

All that aside – one random thought I’ve got in recent days is that – pipes and platforms don’t apply to businesses alone. Even people can be “pipes”. Rather certain peoples jobs make them pipes. In other words they are pipe jobs.

What are pipe jobs? These are jobs where the persons responsibility is to act as a pipe between two other people. The pair of people they connect can vary over time – but this is the essence of the job. Essentially the job is about acting as a bridge between two people.

The classic pipe job is the translator or interpreter – whose job is to literally ensure that two people who might otherwise find it hard to communicate can communicate.

However there are more such jobs. For example you must have come across people in your company who – irrespective of what you as them, ask someone else for the answer. And then convey that answer to you. In other words – they are a pipe through which the question and answer flows.

That said, they need not ask the same person for the answer each time. Instead they might decide based on the question who the right person to ask might be. In fact that is a classic way in which they add value – by determining which two ends to connect themselves to.

Spokespersons and envoys, of course, are again classic pipes. They lack independent authority but represent their masters/mistresses, and act as a pipe between them and the rest of the world. Unlike the corporate pipes mentioned above, theee people usually don’t add the additional value of figuring out which ends to connect.

So in a corporate context, how do you go from being a pipe to a platform ? A risk averse way is to be a connector – to determine which two ends to connect each time you are asked something. I thjnk there are several titles for this kind of role – seen a lot in software companies.

A more risky but much more rewarding way to get out of pipedom is to develop an opinion – you might still connect and represent people but over a period of time you learn and develop an opinion. So not every question needs to be forwarded to the other end of the pipe. However your years as a pipe would have helped you build credibility among the ends of the pipe. And so you can be a better pipe.

I think this theory is genetic enough – most of you who work for companies should be able to think of several roles whose jobs essentially involve being a pipe!

What have I missed out on here ?

## Why online meetings work but not online conferences

Sitting through a “slip fielding meeting” this morning, I had an epiphany – on why office work and meetings have adjusted fairly well to online formats, but not conferences. It has to do with backchannel conversations.

In meetings where everyone is in the same room, there is naturally just one conversation. Everyone is speaking to everyone else at the same time. Unless the meeting is humongously large, it is considered rude for people to “cross talk” in the meeting, and hence there is just one conversation. Of course, in the last decade or so, people have taken to texting at meetings and stuff, but that is still small.

The advantage with moving this kind of a meeting online is that now crosstalk is fully legit, as long as you are doing it using text only. Anyway, everyone is sitting with their computers. All it takes is one simple alt-tab or command-tab, and you can chat away with others present in the meeting. In fact, this makes online meetings MORE efficient by increasing the information flow (since the main channel of large meetings are usually low throughput).

It is the other way round with conferences and events. In conferences and events, the whole point is backchannel conversation. Pretty much nobody is there to listen to the lectures or panel discussions anyways – all that most attendees want to do is to meet other attendees.

And off-line conferences are conveniently structured to enable such interaction. By having multiple parallel sessions, for example, it becomes legit to just stay out and talk to others. There is always a buzz in the corridors (one conference which was single-session-at-a-time only turned out to be bloody boring).

The other thing is that most backchannel and side channel conversations at conferences are between people who don’t yet know each other, and who are there for discovery. So you need to physically bump into someone to talk to them – you can’t randomly start a conversation with someone.

And this translates horribly to online. Online is great for backchannel and side channel conversations with people you already know well – like colleagues. When you don’t know most other people, side channel conversation is awkward. And the main channel content in conferences is largely useless anyway.

This is why it is important that conferences and seminars and other such events move to an offline format asap. For large work meetings we can continue online even after we’re all back at office.

PS: I’m firmly in the DJ D-Sol camp in terms of calling people back to work, at least to make them live in or close to the “home locations”. This way, you have the optionality to meet at short notice without planning, something that fully remote work makes it really hard.

## Modelling for accuracy

Recently I’ve been remembering the first assignment of my “quantitative methods 2” course at IIMB back in 2004. In the first part of that course, we were learning regression. And so this assignment involved a regression problem. Not too hard at first sight – maybe 3 explanatory variables.

We had been randomly divided into teams of four. I remember working on it in the Computer Centre, in close proximity to some other teams. I remember trying to “do gymnastics” – combining variables, transforming them, all in the hope of trying to get the “best possible R square”. From what I remember, most of the groups went “R square hunting” that day. The assignment had been cleverly chosen such that for an academic exercise, the R Square wasn’t very high.

As an aside – one thing a lot of people take a long time to come to terms with is that in “real life” (industry problems) R squares aren’t usually that high. Forecast accuracy isn’t that high. And that the elegant methods they had learnt back in school / academia may not be as elegant any more in industry. I think I’ve written about this, but I can’t find the link now.

Anyway, back to QM2. I remember the professor telling us that three groups would be chosen at random on the day of the assignment submission, and from each of these three groups one person would be chosen at random who would have to present the group’s solution to the class. I remember that the other three people in my group all decided to bunk class that day! In any case, our group wasn’t called to present.

The whole point of this massive build up is – our approach (and the approach of most other groups) had been all wrong. We had just gone in a mad hunt for R square, not bothering to figure out whether the wild transformations and combinations that we were making made any business sense. Moreover, in our mad hunt for R square, we had all forgotten to consider whether a particular variable was significant, and if the regression itself was significant.

What we learnt was that while R square matters, it is not everything. The “model needs to be good”. The variables need to make sense. In statistics you can’t just go about optimising for one metric – there are several others. And this lesson has stuck with me. And guides how I approach all kinds of data modelling work. And I realise that is in conflict with the way data science is widely practiced nowadays.

The way data science is largely practiced in the wild nowadays is precisely a mad hunt for R Square (or area under ROC curve, if you’re doing a classification problem). Whether the variables used make sense doesn’t matter. Whether the transformations are sound doesn’t matter. It doesn’t matter at all whether the model is “good”, or appropriate – the only measure of goodness of the model seems to be the R square!

In a way, contests such as Kaggle have exacerbated this trend. In contests, typically, there is a precise metric (such as R Square) that you are supposed to maximise. With contests being evaluated algorithmically, it is difficult to evaluate on multiple parameters – especially not whether “the model is good”. And since nowadays a lot of data scientists hone their skills by participating in contests such as on Kaggle, they are tuned to simply go R square hunting.

Also, the big difference between Kaggle and real life is that in Kaggle, the model that you build doesn’t matter. It’s just a combination. You get the best R square. You win. You take the prize. You go home.

You don’t need to worry about how the data for the model was collected. The model doesn’t have to be implemented. No business decisions need to be made based on the model. Contest done, model done.

Obviously that is not how things work in real life. Building the model is only one in a long series of steps in solving the business problem. And when you focus too much on just one thing – the model’s accuracy in the data that you have been given, a lot can be lost in the rest of the chain (including application of the model in future situations).

And in this way, by focussing on just a small portion of the entire data science process (model building), I think Kaggle (and other similar competition platforms) has actually done a massive disservice to data science itself.

Tailpiece

This is completely unrelated to the rest of the post, but too small to merit a post of its own.

Suppose you ask a software engineer to sort a few datasets. He goes about applying bubble sort, heap sort, quick sort, insertion sort and a whole host of other techniques. And then picks the one that sorted the given datasets fastest.

That’s precisely how it seems “data science” is practiced nowadays