Implementation and rule of law

Draconian laws coupled with lax implementation deliver too much power to regulators. This makes the business environment unpredictable and makes it harder to do business.

Following the arrest for rape of a taxi driver who was hailed using the Uber app, the Delhi government has gone on to ban Uber. Not satisfied with that, it has gone on to ban all other app-based taxi hailing services (Ola and TaxiForSure are the other big ones). Following the incident last weekend, the government has suddenly decided to throw the rule book at these aggregators and accused them of running taxi services without a license. The point to note here is that until the weekend’s alleged rape, it seems that these businesses were all kosher.

A few months back Mint had an excellent piece (I hope I’ve got the link right) on the absurdities of some of India’s labour laws, and pointed out that most companies are in the breach of such laws. Essentially while the labour laws in India are not very short of being Draconian, what allows businesses to do business and people to go about their lives is lax implementation. And it seems that this issue of draconian laws and lax implementation is not restricted to labour alone.

The iconic Bangalore Club in Bangalore has had its liquor license withdrawn following a raid by the excise department last week. The trigger for this raid is alleged to be a case where a security guard of the club refused to let in the car of a police officer who was not carrying his membership card. This is alleged to have led to a series of cases which finally led to the excise raid and the cancellation of the license. It seems that before the police officer’s car was stopped, there was no violation of excise rules.

In a recent dispute on VAT, the Karnataka government has forced Amazon to stop storing third party goods in its “fulfilment centres”. There has since been back and forth on this and the commissioner of commercial taxes who implemented the order has since been transferred. It was initially expected that the Karnataka government would take the legislative route to clarify this tax dispute in the current Assembly session at Belagavi, but that seems to now be put on hold. Instead, it is likely that the laws are going to remain the way they are and Amazon will by “spared” on account of lax implementation.

Lax implementation of laws is a major impediment to doing business, for it removes predicability. Clear laws which are implemented well set down clear rules for businesses and there is little in terms of what is right or wrong. Such laws make it possible for businesses that choose to be “100% legal” to take a path where there is no ambiguity on their activities. Lax implementation, however, biases the playing field in favour of players who are willing to play on the borders of legality and who rely on lax implementation and benevolence by regulators to continue doing business which is technically illegal. Soon, this results in an equilibrium where everyone is in violation of some rule or the other and remains in business only due to the “benevolence” of regulators.

This implies that regulators who are in charge of implementing these draconian laws have enormous powers over the business they regulate, for any move by the business that the regulator does not like can be responded to by a throw of the proverbial rule-book. This places these businesses at the effective control of these regulators and helps perpetrate what Amit Varma calls the “mai-baap sarkar” – where you function solely due to the benevolence of the government or people acting on behalf of it.

Prime Minister Narendra Modi has stated that one of his goals is to improve the ease of doing business in India. As long as we do not have predictable and rule-based implementation of law, it results in giving significantly higher powers to the regulators, which makes the business environment unpredictable, and makes it harder to do business. If we have to improve our ease of doing business ranking to 50 (as stated by Modi), a necessary step is to implement each of our laws in letter and spirit, without any room for ambiguity. Of course this will lead to the diminishing of power of the regulators over their “regulatees”, but solving that is a political problem which the government ought to solve.

Should you have an analytics team?

In an earlier post a couple of weeks back, 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 🙂