Programmers should fear no shame

You say , "I work as a programmer.". She says, "err. my boyfriend is waiting for me." This awkward moment need not be the case.

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When I started my career in Kuala Lumpur in the IT business in the early 2000s, a conversation at a nightclub venue would end quite quickly when it came to discussing either the company I was working for, which was virtually unknown or the next best alternative – the industry I was in (“IT industry”).

Several things have changed over the last 20 years.

The first is I would never step into a nightclub again. Filthy place.

The second is that programmers, which would for the sake of brevity, include everyone from a junior programmer, database analyst, security analyst to Chief Technical Officer, should not feel ashamed of their vocation.

For the car companies, the average wannabe will recognize 8 out of 10 companies. For Data Companies, you probably will not struggle to get even 1 out of 10. If you get one out 10, you probably are too close to being a nerd.

Data Companies are comparable to Car Companies in valuation

Thanks to Brandon Knoblauch for designing the base template for the automobile manufacturers. QlikView and Tableau’s valuation was based on the acquisition price.

Consider the graphic above which compares the market valuation of the Top Automobile companies against a collection of some data companies.

The important points to note are several fold.

In terms of name recognition, you could walk up to almost any wannabe in KLCC and ask them whether they recognize  the car companies, or its flagship brand. For car companies, you should get at least 8 out of 11 – Tesla, Toyota, Volkswagen, Mercedes, BMW, Ferrari, Honda and Hyundai.

For the Data Companies, you probably will struggle to get even 1 out of 10. If you get one out 10, you probably are too close to being a nerd.

However, in terms of market capitalization, these 10 publicly listed data companies command a market cap valuation of $374 billion, comparable to all major automobile companies, if you exclude Tesla. We have purposely excluded the big  Oracle – $183 billion, SAP – $192 billion, IBM – $108 billion, or privately held giants like SAS, where it is not possible to have a readily determinable valuation.  We have also media companies like Bloomberg or Thomson Reuters, which is $39 billion, because while they provide data, they are too close to “media” and the senior folk in the company would fail a basic hello world programming challenge.

Moreover, we have excluded the Big Three,  Microsoft, Google or Amazon, all important players in the big data business, each of which its individual valuation is either comparable or more than all the top 11 automobile companies combined, including Tesla.

Creators of Python’s NLTK module

Example,  check out the 3 gentleman above. No, they are not a boy band.

Unknown to most people, these are the folk who were the brains behind the Natural Language Toolkit or NLTK. Natural Language Processing are the computer routines that power the “smarts” behind things like Google Search, which enables computers to understand humans, even women. NLTK will be our focus for the next 7 days – once a computer understands what a human wants, the power of it to be able to supply a data driven answer should be exponentially better than the best expert around.

The point to note is that name recognition alone does not translate to a valuable entity. Data companies are super valuable and yet to the average “wannabe” in Kuala Lumpur, they will refer to them as IT companies and rate the person down, down, down in the social order scale. Nor should aspiring data entrepreneurs struggle to explain to their mothers whether there is a market for their product.


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