Analytics for general managers

While good managers have always been required to be analytical, the level of analytical ability being asked of managers has been going up over the years, with the increase in availability of data.

Now, this post is once again based on that one single and familiar data point – my wife. In fact, if you want me to include more data in my posts, you should talk to me more.

Leaving that aside, my wife works as a mid-level manager for an extremely large global firm. She was recruited straight out of business school for a “MBA track” program. And from our discussions about her work in the first few months, one thing she did lots of was writing SQL queries. And she still spends a lot of her time writing queries and building Excel models.

This isn’t something she was trained for, or was tested on while being recruited. She did her MBA in a famously diverse global business school, the diversity of its student bodies implying the level of maths and quantitative methods being kept rather low. She was recruited as a “general manager”. Yet, in a famously data-driven company, she spends a considerable amount of time on quantitative stuff.

It wasn’t always like this. While analytical ability has what (in my opinion) set apart graduates of elite MBA programs from those of middling MBA programs, the level of quantitative ability expected out of MBAs (apart from maybe those in finance) wasn’t too high. You were expected to know to use spreadsheets. You were expected to know some rudimentary statistics- means and standard deviations and some basic hypothesis testing, maybe. And you were expected to be able to make managerial decisions based on numbers. That’s about it.

Over the years, though, as the corpus of data within (and outside) organisations has grown, and making decisions based on data has become fashionable (a brilliant thing as far as I’m concerned), the requirement from managers has grown as well. Now they are expected to do more with data, and aren’t always trained for that.

Some organisations have responded to this problem by supplying “data analysts” who are attached to mid level managers, so that the latter can outsource the analytical work to the former and spend most of their time on “managerial” stuff. The problem with this is twofold – it is hard to guarantee a good career path to this data analyst (which makes recruitment hard), and this introduces “friction” – the manager needs to tell the analyst what precise data and analysis she needs, and iterating on this can lead to a lot of time lost.

Moreover, as the size of the data has grown, the complexity of the analysis that can be done and the insights that can be produced has become greater as well. And in that sense, managers who have been able to adapt to the volume and complexity of data have a significant competitive advantage over their peers who are less comfortable with data.

So what does all this mean for general managers and their education? First, I would expect the smarter managers to know that data analysis ability is a competitive advantage, and so invest time in building that skill. Second, I know of some business schools that are making their MBA programs less quantitative, as their student body becomes more diverse and the recruitment body becomes less diverse (banks are recruiting far less nowadays). This is a bad move. In fact, business schools need to realise that a quantitative MBA program is more of a competitive advantage nowadays, and tune their programs accordingly, while not compromising on the diversity of the student intake.

Then, there is a generation of managers that got along quite well without getting its hands dirty with data. These managers will now get challenged by younger managers who are more conversant with data. It will be interesting to see how organisations deal with this dynamic.

Finally, organisations need to invest in training programs, to make sure that their general managers are comfortable with data, and analysis, and making use of internal and external data science resources. Interestingly enough (I promise I hadn’t thought of this when I started writing this post), my company offers precisely one such workshop. Get in touch if you’re interested!

Generalist and specialist managers

A really long time ago, I’d written this blog post about “comparative advantage” versus “competitive advantage” employees. A competitive advantage employee is better at a particular kind of task or skill compared to the rest of the team, and he is valued for that kind of skill.

A comparative advantage employee, on the other hand, is “dominated” by at least one other person in the team – in the sense that someone else is better than this person at everything required for the job. In that sense, the value that the comparative advantage employee adds is by taking load off his colleagues, and allowing them to do more (and focus on the more productive parts of their jobs).

Thinking about it now, I realise that a similar classification exists from the manager’s perspective as well. And this is broadly correlated with whether the manager manages a “generalist” or a “specialist” team.

A specialist manager manages a team all of whose members work on and excels at one specialist task. This task could come from any part of the organisation – it could be sales or a particular kind of operations, or some financial activity or whatever. The defining feature of this kind of task is that it is usually repetitive and needs to be done in high volumes. Such tasks also offer high “returns to experience”.

The average employee of a specialist team is usually a “comparative advantage” employee. In most cases, such an employee is likely to be “dominated” by the manager, and the value he adds is by taking the load off the manager and allowing him to do more. Over the course of time, he becomes good enough at the job to become a manager himself, and the cycle continues – he will manage a team of people who are mostly inferior to him in the job.

Due to managers dominating direct reports, such teams end up being largely hierarchical, and there can be a tendency for the manager to micro-manage – if you are better at the task than the person actually doing it, you can do worse than giving specific instructions.

Generalist managers, on the other hand, manage teams that involve at least a few competitive advantage employees. What this implies is that there is a set of people who are better than the manager at certain parts of the business. The manager’s role in such a team is more of a facilitator, in terms of bringing the team together and coordinating in a way that they can maximise the team’s effectiveness.

Generalist managers seldom micromanage, since usually their team members know better (literally). They are also usually open-minded, since extracting full value from the team means recognising each member’s strengths (and consequently their own weaknesses). They learn the art of asking questions and verifying insights and work of the team in a cheap manner (remember from complexity theory that the complexity of verifying a solution can be much lower than the complexity of finding a solution).

Regular readers of the blog might have anticipated this paragraph – the trouble comes when a generalist manager has to manage a specialist team or the other way round.

A generalist manager managing a specialist team may not offer as much as he can to the team based on his experience. He might be too hands-off and team members used to more handholding and direction might feel lost. And so on.

A specialist manager managing a generalist team can be more damaging – not appreciating that some members might know more about some parts of the business might limit the performance of the team (since what the team can do is limited by what the manager knows). Also too much micromanagement on employees who know better about some parts of the business than the manager can result in disillusionment and ultimately backfire on the manager.

I wonder if this has something to do with the Peter Principle!