Slavedriver sandwich

Something that happened at home earlier today reminded me of my very first full-time job, which I had ended up literally running away from barely two months after I’d started. I like to call this the “slavedriver sandwich”.

The basic problem is this – you need to get someone you normally have no influence over to do something for you, and this something is contrary to what this person needs to do. You somehow need to convince this person to do this – effectively, you need to “slave-drive” her so that what you want done is done.

The problem is that you aren’t even sure that you want this thing to be done. The only reason you are slavedriving the person you’re slavedriving is because someone else (let’s call this person “the boss”) is slavedriving you, and trying to make you get this person to do this.

The boss is very clear on what she wants done, and how she wants it done, but for reasons of her own choosing, doesn’t want to get it done directly. She wants you to do it. And you aren’t convinced that what she needs to be done is the right thing to be done – you agree with the basic principles but think there’s a better way to do it than slavedriving the person you normally have no control over.

Like I remember this time from 2006 when the then boss wanted some data, and I had to convince this client to give us the data. It seemed tractable that the data would be available in a day, and in CSV format. But the boss wanted it the same day, and in Excel format (yeah, I worked for people who considered conversion from CSV to Excel nontrivial). And so I was slavedriven, so that I could slave drive this client, and get the data to the boss in time (never mind that it was I who would ultimately use the data, and I actually preferred CSV!).

In other words, then and now, I was stuck in a “slavedriver sandwich”. Someone slavedriving you to slavedrive someone, and you are wondering what role you have to do in the whole business in the first place. And then you decide that you have nothing to do there, and you should just eliminate the middleman, which is yourself.

In that sense, the problem of 2006 was easy – eliminating the middleman simply meant resigning my job. The current circumstances (which I can’t particularly describe here) doesn’t allow for so elegant a solution! So it goes.

The Ramayana and the Mahabharata principles

An army of monkeys can’t win you a complex war like the Mahabharata. For that you need a clever charioteer.

A business development meeting didn’t go well. The potential client indicated his preference for a different kind of organisation to solve his problem. I was about to say “why would you go for an army of monkeys to solve this problem when you can.. ” but I couldn’t think of a clever end to the sentence. So I ended up not saying it.

Later on I was thinking of the line and good ways to end it. The mind went back to Hindu mythology. The Ramayana war was won with an army of monkeys, of course. The Mahabharata war was won with the support of a clever and skilled consultant (Krishna didn’t actually fight the war, did he?). “Why would you go for an army of monkeys to solve this problem when you can hire a studmax charioteer”, I phrased. Still doesn’t have that ring. But it’s a useful concept anyway.

Extending the analogy, the Ramayana was was different from the Mahabharata war. In the former, the enemy was a ten-headed demon who had abducted the hero’s wife. Despite what alternate retellings say, it was all mostly black and white. A simple war made complex with the special prowess of the enemy (ten heads, special weaponry, etc.). The army of monkeys proved decisive, and the war was won.

The Mahabharata war was, on the other hand, much more complex. Even mainstream retellings talk about the “shades of grey” in the war, and both sides had their share of pluses and minuses. The enemy here was a bunch of cousins, who had snatched away the protagonists’ kingdom. Special weaponry existed on both sides. Sheer brute force, however, wouldn’t do. The Mahabharata war couldn’t be won with an army of monkeys. Its complexity meant it needed was skilled strategic guidance, and a bit of cunning, which is what Krishna provided when he was hired by Arjuna ostensibly as a charioteer. Krishna’s entire army (highly trained and skilled, but footsoldiers mostly) fought on opposite side, but couldn’t influence the outcome.

So when the problem at hand is simple, and the only complexity is in size or volume or complexity of the enemy, you will do well to hire an army of monkeys. They’ll work best for you there. But when faced with a complex situation and complexity that goes well beyond the enemy’s prowess, you need a charioteer. So make the choice based on the kind of problem you are facing.

 

Reforming Air India (yet again!!)

Being a PSU, Air India faces a unique set of constraints. In order to maximize its performance, the airline should take the most optimal decisions that satisfy these constraints. 

On Monday I had to go to Mumbai on some work and I flew Air India. Normally I prefer to fly either Jet or Indigo, but given the short notice at which I had to plan my trip, and the fare difference between Air India and the other two (leaving aside some airline I don’t trust), I decided to go for the national carrier. Overall it wasn’t an unpleasant experience – my onward flight was late by ten minutes or so, while my return flight was on time. There was plenty of leg space, the food was good and online check in was hassle free. Yet, it looked like there was plenty of scope for improvement.

Now for a digression. The difference between club football and international football is that in the latter you cannot buy players (not strictly true – Spain got Brazilian born Diego Costa to play for them on account of 1. his Spanish passport, 2. that he had never played for his native Brazil, but this is an extreme assumption). To use a cliched term, in international football you need to play the hand that you’re dealt. Thus, the job of a manager of an international football team is to organize his team’s strategy and tactics according to the personnel available to him, rather than the other way round. For example, Dutch manager Louis van Gaal is known to favour a possession based passing game. However, given the set of Dutch players available to him, he has set them out as a counterattacking side.

Given the lack of degrees of freedom in running PSUs, it can be argued that running a PSU is closer to managing a national football team than it is to managing a club team. Government ownership and consequent pay structures, combined with the lack of a good lateral entry system to the Indian public sector, mean that it is hard for a PSU to “buy” personnel like private companies can. On the other hand, sacking PSU employees is a politically charged activity, and not easy to administer, so it is hard to get rid of deadwood also.

The traditional argument is that given these restrictions that PSUs face, it is impossible for them to perform at the same level as comparable private sector units. While this argument is well taken, what we need to be careful is to not let this mask any degree of poor performance by a PSU. The question, instead, that we need to ask is if the PSU is actually making best use of the “hand it has been dealt”. What we need to check is if the PSU is optimizing correct given the resources and constraints at hand.

Coming back to Air India, one of the stated causes of its poor performance is that it is overstaffed – it far exceeds its global peers in terms of the number of employees per aircraft (normally assumed to be a good metric of staff size). This was fully visible at the boarding gate on Monday, for there were four personnel with the task of barcode scanning the boarding passes. Most other airlines have two staff doing this. A clear case of overstaffing. While it may not be under the management’s control to downsize (see constraints listed above), what irked me was that they were not being put to best use.

Just to take a simple example, if you have twice the number of required staff at the boarding counter, all you need to do is to put in an additional barcode scanner and run two boarding lines instead of one – which results in doubling the pace at which the plane is boarded. This doubling of boarding pace means planes can have a much faster turnaround time at each airport – which means the number of flights that Air India can run given its stock of aircraft can increase significantly!

To take another example, Air India probably has the best leg space in the economy class among all Indian carriers – this is probably driven by the fact that a large number of government officers and ministers travel mostly by Air India, and holy cows mean that they are forced to travel  “cattle class”, the airline offers some comfort. Now, while this means each plane has one or two rows of seats less than that of other carriers, it constitutes a massive marketing opportunity for the airline! Given the leg space and comfort and meals (!!), Air India can very well position itself as a premium carrier and try to charge a premium on tickets!

On an absolute basis, the recommendations above may not be optimal – it might be well possible to make more money by sacking boarding gate employees than by cutting boarding time, or it may make more business sense to add an extra row of seats than try to enhance legspace. However, given the constraints the carrier faces, these are possibly the “second best decisions” that the carrier can take. And by not taking these decisions, the carrier is not making as much money as it can make!

Should you have an analytics team?

In an earlier post, I had talked about the importance of business people knowing numbers and numbers people knowing business, and had put in a small advertisement for my consulting services by mentioning that I know both business and numbers and work at their cusp. In this post, I take that further and analyze if it makes sense to have a dedicated analytics team.

Following the data boom, most companies have decided (rightly) that they need to do something to take advantage of all the data that they have and have created dedicated analytics teams. These teams, normally staffed with people from a quantitative or statistical background, with perhaps a few MBAs, is in charge of taking care of all the data the company has along with doing some rudimentary analysis. The question is if having such dedicated teams is effective or if it is better to have numbers-enabled people across the firm.

Having an analytics team makes sense from the point of view of economies of scale. People who are conversant with numbers are hard to come by, and when you find some, it makes sense to put them together and get them to work exclusively on numerical problems. That also ensures collaboration and knowledge sharing and that can have positive externalities.

Then, there is the data aspect. Anyone doing business analytics within a firm needs access to data from all over the firm, and if the firm doesn’t have a centralized data warehouse which houses all its data, one task of each analytics person would be to get together the data that they need for their analysis. Here again, the economies of scale of having an integrated analytics team work. The job of putting together data from multiple parts of the firm is not solved multiple times, and thus the analysts can spend more time on analyzing rather than collecting data.

So far so good. However, writing a while back I had explained that investment banks’ policies of having exclusive quant teams have doomed them to long-term failure. My contention there (including an insider view) was that an exclusive quant team whose only job is to model and which doesn’t have a view of the market can quickly get insular, and can lead to groupthink. People are more likely to solve for problems as defined by their models rather than problems posed by the market. This, I had mentioned can soon lead to a disconnect between the bank’s models and the markets, and ultimately lead to trading losses.

Extending that argument, it works the same way with non-banking firms as well. When you put together a group of numbers people and call them the analytics group, and only give them the job of building models rather than looking at actual business issues, they are likely to get similarly insular and opaque. While initially they might do well, soon they start getting disconnected from the actual business the firm is doing, and soon fall in love with their models. Soon, like the quants at big investment banks, they too will start solving for their models rather than for the actual business, and that prevents the rest of the firm from getting the best out of them.

Then there is the jargon. You say “I fitted a multinomial logistic regression and it gave me a p-value of 0.05 so this model is correct”, the business manager without much clue of numbers can be bulldozed into submission. By talking a language which most of the firm understands you are obscuring yourself, which leads to two responses from the rest. Either they deem the analytics team to be incapable (since they fail to talk the language of business, in which case the purpose of existence of the analytics team may be lost), or they assume the analytics team to be fundamentally superior (thanks to the obscurity in the language), in which case there is the risk of incorrect and possibly inappropriate models being adopted.

I can think of several solutions for this – but irrespective of what solution you ultimately adopt –  whether you go completely centralized or completely distributed or a hybrid like above – the key step in getting the best out of your analytics is to have your senior and senior-middle management team conversant with numbers. By that I don’t mean that they all go for a course in statistics. What I mean is that your middle and senior management should know how to solve problems using numbers. When they see data, they should have the ability to ask the right kind of questions. Irrespective of how the analytics team is placed, as long as you ask them the right kind of questions, you are likely to benefit from their work (assuming basic levels of competence of course). This way, they can remain conversant with the analytics people, and a middle ground can be established so that insights from numbers can actually flow into business.

So here is the plug for this post – shortly I’ll be launching short (1-day) workshops for middle and senior level managers in analytics. Keep watching this space :)