Status and money

Over the last week or so, I’ve been discussing this post by Robin Hanson with just about anyone. The first paragraph is the one that caught my attention.

Having a romantic partner is useful in many ways. You won’t be as lonely, you can ask them for advice, you can do activities together, and you can share transport and even a household with them. But if you look carefully, you will notice that many people don’t choose such partners mainly for their promise in such roles. They instead seek high status partners, who make them look good by association. Partners who are hot, funny, rich, powerful, etc.

Nevertheless, I urge you to read the whole thing. Hanson goes on to talk about status in several other fields, such as politics or in organisations.

Broadly paraphrasing (you should still read the whole thing), he says that people want to be associated with people with high status, or people who add status to them. So politicians who can project higher status will get elected. Organisations will appoint people who can further increase the status of the organisation.

I was thinking about this today from the point of view of last night’s post, where I had compared my life in my (current) full time job to that of a consultant, which I had been for nine years prior.

Sometimes it is common for us to comment, or gossip, that someone  got hired purely on the strength of their reputation, and that their abilities are not extraordinary. Sometimes, reputations can be self-fulfilling – if you can somehow get the reputation of being good at something, more people will start with the Bayesian prior that you’re good at that, and as long as you don’t suck at that thing, the prior will continue to hold. And so more people will think you’re good at it, and so on.

So when I think of my own career, basically I realise the way to go is to get into a position that my sheer presence adds status to the organisation I’m associated with. That way, they will be more forgiving of the work that I do (or don’t do). At the same time, from my own perspective, the organisation also needs to (at least marginally) add to my status – at some level I may not want to join a club that wants me as a member.

I remember back in the day when I was consulting – one of my clients, during the negotiations prior to the engagement, had wanted me to put on LinkedIn that I was working for them. Now when I think of it from the point of view of Hanson’s post, this was the client leveraging my then reputation in data to further their own status.

This is what I need to bring to my employers as well (I have no clue if I do already with my current ones – though I’m not so popular within my (data science) domain in india). The target, if I were to think of it, is to get into that self-fulfilling space when it comes to status – that people want me just because I’m me and bring along a certain (positive) status.

Now that I’ve identified the target, I need to figure out how to get there. I know in his famous podcast, Naval said that we should optimise for wealth (a positive sum game) rather than for status (a zero sum game). But Hanson’s post, and my analysis of it, suggests that status can also lead to wealth. I need to figure out the tradeoff now!

Coasean notes

I’m well over two and a half years into my current job, easily making this my longest unbroken spell of employment ever. This is a random set of pertinent observations, more a set of notes to myself rather than for any reader, regarding how the job has been playing out.

  • The Nature of The Firm is real. For nine years, as a consultant, I enjoyed market pricing (adjusting for illiquidity and and other distortions) for all the work that I did, but also suffered from the transaction costs that Coase writes about in his famous paper.

    This meant that unless the work was reasonably well defined, or of a certain minimum size, I wouldn’t take it up – the transaction costs involved in doing the deal would far outweigh any benefits that my counterparty and I would achieve from the deal. This meant I added less value than I could have to my clients

  • “Going deep” has its benefits. If I look at some of the work that I’ve done in the last few months here, and compared that to my work in my first year here, there is an absolutely marked difference. The difference is the two years of compounded extreme domain knowledge (about the company and its business).

    From that perspective, consulting can sometimes suffer from a limitation of domain knowledge

  • Countering the above point is that I’ve “been internalised” after two plus years here. The things that excited me at the time I joined don’t excite me any more. There are times when I get what I think are interesting insights, and then just don’t bother about showing them to anyone, based on the historical reaction to such insights.

    A fresh consultant, on the other hand, would share more, and would thus get more done

  • The biggest advantage of being “in house” is the data – I have access to pretty much ALL data in the company, and if I don’t have access to something, there is a good chance that the data doesn’t exist. This means I’m able to craft better hypotheses and do better analysis, compared to the time when I relied on clients to share specific datasets with me (pretty much nobody opened up full live access to their database to me)
  • In a way I also miss the novelty of being a consultant – because you work with a company for a short period of time, you are bringing in new ideas and insights in that period of time, and people pay you attention for it. As an in-house employee, you become a part of the furniture. And a lot of the time, it is a good thing if nobody notices you
  • Lack of friction in terms of taking up work means average quality of work can suffer. If you are very particular about the kind of work you want to do, it’s good if you can be a consultant – the friction means it’s easier to say no there.
  • As a consultant, by definition, I was a “hybrid worker”, working by myself for long periods of time and then visiting the client for meetings and discussions. That had worked out brilliantly well for me.

    However, I realise “that hybrid” is different from “this hybrid” (the job), since here people have access to my calendar and are able to schedule meetings even at times when I’m not in office. Rather, since my company has a multiple-headquarter setup, I even prefer to take meetings with colleagues not in Bangalore on days when I’m at home.

  • The biggest difference between monogamy (one employer) and polyamory (two or more “clients”) is that in the latter, no one owns your time. Because they know that they are “one of several” (even if at some point in time they are “one of one” it doesn’t matter, since that’s a special case), they can’t take your time for granted. And that gives you immensely more control over your time.

    This was possibly the hardest part for me getting back to a full time job – the lack of control over my time since I had now sold ALL of it to one company.

  • The flip side of this is that, at least for someone like me, not having to keep selling myself constantly is a brilliant feeling. Though, there is some amount of “within the company selling” that has to happen from time to time.
  • Apart from control over my time, the thing I miss the most about my consulting life are the “semi work meetings” – these are meetings with prospective clients, people who can lead you to prospective clients, old clients, etc. Where there is a tinge of work to the meeting, but you also catch up on several other things.

    Now that I’m in a job, and one that is entirely internal facing, there is no concept of “pseudo work meetings”. It is either proper work meetings (or “water cooler conversations”) with colleagues, or proper socialisation with others. That means I’m meeting far fewer people on average, nowadays

  • I admit that having become a sort of a “company man“, I’ve started taking myself more seriously than I would like to. Of late I’ve started making a conscious effort to dial this back a little bit, and I think it’s already making me happier.
  • Oh, and game theory rocks. Not a day goes by without me thinking about “saama daana bhEda danDa

I can go on and on and on, but I think this is enough for now. If I have more, I’ll write another post.

Bad Data Analysis

This is a post tangentially related to work, so I must point out that all views here are my own, and not views of my employer or anyone else I’m associated with

The good thing about data analysis is that it’s inherently easy to do. The bad thing about data analysis is also that it’s inherently easy to do – with increasing data democratisation in companies, it is easier than ever than pulling some data related to your hypothesis, building a few pivot tables and charts on Excel and then presenting your results.

Why is this a bad thing, you may ask – the reason is that it is rather easy to do bad data analysis. I’m never tired of telling people who ask me “what does the data say?”, “what do you want it to say? I can make it say that”. This is not a rhetorical statement. As the old saying goes, you can “take data down into the basement and torture it until it confesses to your hypothesis”.

So, for example, when I hire analysts, I don’t check as much for the ability to pull and analyse data (those can be taught) as I do for their logical thinking skills. When they do a piece of data analysis, are they able to say that it makes sense or not? Can they identify that some correlations data shows are spurious? Are they taking ratios along the correct axis (eg. “2% of Indians are below the poverty line”, versus “20% of the world’s poor is in India”)? Are they controlling for instrumental variables?

This is the real skill in analytics – are you able to draw logical and sensible conclusions from what the data says? It is no coincidence that half my team at my current job has been formally trained in economics.

One of the externalities of being a head of analytics is that you come across a lot of bad data analysis – you are yourself responsible for some of it, your team is responsible for some more and given the ease of analysing data, there is a lot from everyone else as well.

And it becomes part of your job to comment on this analysis, to draw sense from it, and to say if it makes sense or not. In most cases, the analysis itself will be immaculate – well written queries and logic / code. The problem, almost all the time, is in the logic used.

I was reading this post by Nabeel Qureshi on puzzles. There, he quotes a book on chess puzzles, and talks about the differences between how experts approach a problem compared to novices.

The lesson I found the most striking is this: there’s a direct correlation between how skilled you are as a chess player, and how much time you spend falsifying your ideas. The authors find that grandmasters spend longer falsifying their idea for a move than they do coming up with the move in the first place, whereas amateur players tend to identify a solution and then play it shortly after without trying their hardest to falsify it first. (Often amateurs, find reasons for playing the move — ‘hope chess’.)

Call this the ‘falsification ratio’: the ratio of time you spend trying to falsify your idea to the time you took coming up with it in the first place. For grandmasters, this is 4:1 — they’ll spend 1 minute finding the right move, and another 4 minutes trying to falsify it, whereas for amateurs this is something like 0.5:1 — 1 minute finding the move, 30 seconds making a cursory effort to falsify it.

It is the same in data analysis. If I think about the amount of time I spend in analysing data, a very very large percentage of it (can’t put a number since I don’t track my time) goes in “falsifying it”. “Does this correlation make sense?”; “Have I taken care of all the confounding variables?”; “Does the result hold if I take a different sample or cut of data?”. “Has the data I’m using been collected properly?”; “Are there any biases in the data that might be affecting the result?”; And so on.

It is not an easy job. One small adjustment here or there, and the entire recommendations might flip. Despite being rigorous with the whole process, you can leave in some inaccuracy. And sometimes what your data shows may not conform to the counterparty (who has much better domain knowledge)’s biases – and so you have a much harder job selling it.

And once again – when someone says “we have used data, so we have been rigorous about the process”, it is more likely that they are more wrong.

Hybrid work

I’m in a job that can broadly be described as “hybrid”. The mandate from HR is that we are are “expected to be in office three days a week, and live in the same city as the office”. Nobody really checks how often people go in to office, though I do end up going three times a week on average.

Of late, some tech “gurus” have taken on dunking on hybrid work. DHH of 37signals / Basecamp (I quite like his blog, in general) wrote that “hybrid combines the worst of in-person and remote“. Then, Paul Graham wrote some tweets on remote work. I quite like this one:

Back to hybrid work – I’m in a hybrid role now, where I go into office about three days a week on average, and stay home the other two days (in general, because Monday is crowded with long online meetings, and another day to do some “thinking work”). Different people in my company have different such strategies, and all come into office on their own schedules.

This is not the first time I’m doing “hybrid”. During my rather long independent consulting career, I largely worked from home but travelled to clients’ offices ever so often (once a week if in Bangalore; one week a month if not; on average). It was about getting the best combination of focussed work and collaboration. It worked then, and it works now.

In fact, as far back as 2007 I was in a hybrid office. I was in what is now called a “global capability centre”, and interacting with headquarters in Texas meant being available for calls later in the evening. Consequently, we could work from home a few days a week as long as we were available for these calls.

Coming as it did at the beginning of my career, it was a disaster. I slacked like nobody’s business. Less time spent in office meant less time understanding parts of the business not directly concerned with what I was working on. Most of my development in that period happened due to my independent reading and writing, rather than due to my work.

Now, once again, I’m in a company with “multiple headquarters”. This means that irrespective of where you are, you end up spending a considerable amount of time on video calls with people in other locations. According to DHH, video calls when you are in office is a waste of office time. I agree with him there. The way I manage is through my schedule.

Of course, it helps that I have a reputation in office that I don’t like to do unnecessary meetings – and all matters need to be resolved to the extent possible in text messages or email. This means I spend less time on video calls than many of my colleagues, and when I find a lot of them appearing on a day, i spend that day at home.

Also, I have an unspoken agreement with my (rather small) team on days of the week when we’ll meet in office, and so the technical discussions I find so difficult to have online can be had in person.

Hybrid primarily works because of optionality (a rather underappreciated concept). In my line of business, things can get so technical that there is a limit on the complexity of discussions that can be had online. Similarly, things can get so technical that we need undisturbed alone time to think through some of the solutions.

Hybrid works because it allows for both – it allows you to have your me time for your deep thinking, and the optionality of summoning a teammate to office “tomorrow” for some deep collaboration. The former is unavailable in an all-in office; the latter is not possible if you’re fully remote (I’ve experienced this during the pandemic years).

Yes, hybrid means you need to live within commuting distance of office (sometimes during interviews, I see candidates furiously googling for “richmond circle” or “residency road” when I tell them our office is there. It’s a strong signal that they’re not going to join 😛 ). However, that you only need to commute twice a week (rather than 5 times a week) means you can choose to live a little bit farther.

Yes, it does make hiring harder (compared to all-remote), but once hired, people can be far more productive in a hybrid model. With the option of doing deep work without the danger / fear of someone poking you (this literally happened to me yesterday) when you’re in the middle of deep work!

So yes, put me down as someone who likes the hybrid model of work.

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.


The Law Of Comparative Advantage and Priorities

Over a decade ago I had written about two kinds of employees – those who offer “competitive advantage” and those who offer “comparative advantage”.

Quoting myself:

So in a “comparative advantage” job, you keep the job only because you make it easier for one or more colleagues to do more. You are clearly inferior to these colleagues in all the “components” of your job, but you don’t get fired only because you increase their productivity. You become the Friday to their Crusoe.

On the other hand, you can keep a job for “competitive advantage“. You are paid because there are one or more skills that the job demands in which you are better than your colleagues

Now, one issue with “comparative advantage” jobs is that sometimes it can lead to people being played out of position. And that can reduce the overall productivity of the team, especially when priorities change.

Let’s say you have 2 employees A and B, and 2 high-priority tasks X and Y. A dominates B – she is better and faster than B in both X and Y. In fact, B cannot do X at all, and is inferior to A when it comes to Y. Given these tasks and employees, the theory of comparative advantage says that A should do X and B should do Y. And that’s how you split it.

In this real world problem though, there can be a few issues – A might be better at X than B, but she just doesn’t want to do X. Secondly, by putting the slower B on Y, there is a floor on how soon Y can be delivered.

And if for some reason Y becomes high priority for the team, with the current work allocation there is no option than to just wait for B to finish Y, or get A to work on Y as well (thus leaving X in the lurch, and the otherwise good A unhappy). A sort of no win situation.

The whole team ends up depending on the otherwise weak B, a sort of version of this:

A corollary is that if you have been given what seems like a major responsibility it need not be because you are good at the task you’ve been given responsibility for. It could also be because you are “less worse” than your colleagues at this particular thing than you are at other things.



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!

Key Person Risk and Creative Professions

I’m coming to the conclusion that creative professions inevitably come with a “key person risk”. And this is due to the way teams in such professions are usually built.

I’ll start with a tweet that I put out today.

(I had NOT planned this post at the time when I put out this tweet)

I’ll not go into defining creative professions here, but I will leave it to say that you typically know it when you see one.

The thing with teams in such professions is that people who are good and creative are highly unlikely to get along with each other. Going into the animal kingdom for an analogy, we can think of dividing everyone in any such professions into “alphas” and “betas”. Alphas are the massively creative people who usually rise to lead their teams. Betas are the rest.

And given that any kind of creativity is due to some amount of lateral thinking, people good at creative professions are likely to hallucinate a bit (hallucination is basically lateral thinking taken to an extreme). And stretching it a bit more, you can say that people who are good at creative tasks are usually mad in one way or another.

As I had written briefly this morning, it is not usual for mad people (especially of a similar nature of madness) to get along with each other. So if you have a creative alpha leading the team, it is highly unlikely that he/she will have similar alphas in the next line of leadership. It is more likely that the next line of leadership will have people who are good complements to the alpha leader.

For example, in the ongoing World Cup, I’ve seen several tactical videos that have all said one thing – that Rodrigo De Paul’s primary role in the Argentinian team is to “cover for Messi”. Messi doesn’t track back, but De Paul will do the defending for him. Messi largely switches off, but De Paul is industrious enough to cover for Messi. When Messi goes forward, De Paul goes back. When Messi drops deep, De Paul makes a forward run.

This is the most typical creative partnership that you can get – one very obviously alpha creative supported by one or more steady performers who enable the creative person to do the creative work.

The question is – what happens when the creative head (the alpha) leaves? And the answer to this are going to be different in elite sport and the corporate world (and I’m mostly talking about the latter in this post).

In elite sport, when Messi retires (which he is likely to do after tomorrow’s final, irrespective of the result), it is virtually inconceivable that Argentina will ask De Paul to play in his position. Instead, they will look into others who are already playing in a sort of Messi role, maybe (or likely) at an inferior level and bring them up. De Paul will continue to play his role of central midfielder and continue to support whoever comes into Messi’s role.

In corporate setups, though, when one employee leaves, the obvious thing to do is to promote that person’s second in command. Sometimes there might be a battle for succession among various seconds in command, and the losers also leave the company. For most teams, where seconds in command are usually similar in style to the leader, this kind of succession planning works.

For creative teams, however, this usually leads to a disaster. More often than not, the second in command’s skills will be very different from that of the leader. If the leader had been an alpha creative (that’s the case we’re largely discussing here), the second in command is more likely to be a steady “water carrier” (a pejorative term used to describe France’s current coach Didier Deschamps).

And if this “water carrier” (no offence meant to anyone by this, but it is a convenient description) stays in the job for a long time, it is likely that the creative team will stop being creative. The thing that made it creative in the first place was the alpha’s leadership (this is especially true of small teams), and unless the new boss has recognised this and brings in a new set of alphas (or identifies potential alphas in the org and quickly promotes them), the team will start specialising in what was the new boss’s specialisation – which is to hold things steady and do all the right things and cover for someone who doesn’t exist any more.

So teams in creative professions have a key man risk in that if a particularly successful alpha leaves, the team as it remains is likely to stagnate and stop being creative. The only potential solutions I can think of are:

  • Bring in a new creative from outside to lead the team. The second in command remains just that
  • Coach the second in command to identify diverse (and creative alpha) talents within the team and recognise that there are alphas and betas. And the second in command basically leads the team but not the creative work
  • Organise the team more as a sports team where each person has a specific role. So if the attacking midfielder leaves, replace with a new attacking midfielder (or promote a junior attacking midfielder into a senior attacking midfielder). Don’t ask your defensive midfielders to suddenly become an attacking midfielder
  • Put pressure from above for alphas to have a sufficient number of other alphas as the next line of command. Retaining this team is easier said than done, and without betas the team can collapse.

Of course, if you look at all this from the perspective of the beta, there is an obvious question mark about career prospects. Unless you suddenly change your style (easier said than done), you will never be the alpha, and this puts in place a sort of glass ceiling for your career.

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.