Tag: cusp

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 🙂

 

Fundraising

The growth of a new company usually consists of one short period of high growth preceded and followed by rather long periods of steady growth. Sometimes there might be more than one period of high growth, but for most companies, it is that one period when there is a point of inflexion and growth goes to a new trajectory.

Now, my point is that if you want to raise venture funding, you better do it when you think you are on the cusp of one such inflexion. Usually points of inflexion are associated with some increase in “leading” investment, and a small chance that the company will get on to a new trajectory, and a big chance that the company will go under.

This crude chart shows the typical trajectory of a young company. The beginning of the red zone is when you should raise venture money
This crude chart shows the typical trajectory of a young company. The beginning of the red zone is when you should raise venture money

If you look at the picture here, the beginning of the red region is the state where you need to get venture funding. The thing with the black regions is that irrespective of how you fund those, at best you can expect steady growth. Now, venture capital funds, the way they are structured, are not set out to fund steady growth. The way venture funds make money is when one out of a number of their investments makes shockingly great returns, while the rest go under. They are not in the business of funding steady returns.

Hence, when they fund your company they value you assuming that in case your company is successful there will be steep growth, which will enable them to recover their investment. And if your company is in steady growth phase, it is never going to be able to do that. And you will have a case of your investors pushing you to do more or something different from what you had planned doing. The problem here lies in the fact that you raised the wrong kind of funding!

In times like this or at the turn of the millennium, when venture capital is big, it can sometimes become the preferred mode of fundraising for a lot of companies. The problem, however, is that most of them don’t realize that venture funding is probably not the best form of funding for them at their size and scale, and then get weighed down by investors.

On a similar note, you should go public once you know that there are no really big points of inflexion coming up, and that your company is set on a path to steady growth. Again that follows from the fact that investors in the stock market (where they pick up tiny shares in each company) are usually in it for long-term steady growth. And if you happen to take undue risks and they don’t pay off, your stock will get hammered unnecessarily.