By Tony Baer
It’s no secret that rocket .. err … data scientists are in short supply. The explosion of data and the corresponding explosion of tools, and the knock-on impacts of Moore’s and Metcalfe’s laws, is that there is more data, more connections, and more technology to process it than ever. At last year’s Hadoop World, there was a feeding frenzy for data scientists, which only barely dwarfed demand for the more technically oriented data architects. In English, that means:
1. Potential MacArthur Grant recipients who have a passion and insight for data, the mathematical and statistical prowess for ginning up the algorithms, and the artistry for painting the picture that all that data leads to. That’s what we mean by data scientists.
The data architect side will be the more straightforward nut to crack. Understanding big data platforms (Hadoop, MongoDB, Riak) and emerging Advanced SQL offerings (Exadata, Netezza, Greenplum, Vertica, and a bunch of recent upstarts like Calpont) is a technical skill that can be taught with well-defined courses. The laws of supply and demand will solve this one – just as they did when the dot com bubble created demand for Java programmers back in 1999.
Behind all the noise for Hadoop programmers, there’s a similar, but quieter desperate rush to recruit data scientists. While some data scientists call data scientist a buzzword, the need is real.
However, data science will be a tougher number to crack. It’s all about connecting the dots, not as easy as it sounds. The V’s of big data – volume, variety, velocity, and value — require someone who discovers insights from data; traditionally, that role was performed by the data miner. But data miners dealt with better-bounded problems and well-bounded (and known) data sets that made the problem more 2-dimensional.
The variety of Big Data – in form and in sources – introduces an element of the unknown. Deciphering Big Data requires a mix of investigative savvy, communications skills, creativity/artistry, and the ability to think counter-intuitively. And don’t forget it all comes atop a foundation of a solid statistical and machine learning background plus technical knowledge of the tools and programming languages of the trade.
Sometimes it seems like we’re looking for Albert Einstein or somebody smarter.
Nature abhors a vacuum
As nature abhors a vacuum, there’s also a rush to not only define what a data scientist is, but develop programs that could somehow teach it, software packages that to some extent package it, and otherwise throw them into a meat … err, the free market. EMC and other vendors are stepping up to the plate to offer training, not just on platforms, but for data science. Kaggle offers an innovative cloud-based, crowdsourced approach to data science, making available a predictive modeling platform and then staging sponsored 24-hour competitions for moonlighting data scientists to devise the best solutions to particular problems (redolent of the Netflix $1 million prize to devise a smarter algorithm for predicting viewer preferences).
With data science talent scarce, we’d expect that consulting firms would buy up talent that could then be “rented’ to multiple clients. Excluding a few offshore firms, few systems integrators (SIs) have yet stepped up to the plate to roll out formal big data practices (the logical place where data scientists would reside), but we expect that to change soon.
Opera Solutions, which has been in the game of predictive analytics consulting since 2004, is taking the next step down the packaging route. having raised $84 million in Series A funding last year, the company has staffed up to nearly 200 data scientists, making it one of the largest assemblages of genius this side of Google. Opera’s predictive analytics solutions are designed for a variety of platforms, SQL and Hadoop, and today they join the SAP Sapphire announcement stream with a release of their offering on the HANA in-memory database. Andrew Brust provides a good drilldown on the details on this announcement.
From SAP’s standpoint, Opera’s predictive analytics solutions are a logical fit for HANA as they involve the kinds of complex problems (e.g., a computation triggers other computations) that their new in-memory database platform was designed for.
There’s too much value at stake to expect that Opera will remain the only large aggregation of data scientists for hire. But ironically, the barriers to entry will keep the competition narrow and highly concentrated. Of course, with market demand, there will inevitably be a watering down of the definition of data scientists so that more companies can claim they’ve got one… or many.
The laws of supply and demand will kick in for data scientists, but the ramp up of supply won’t be as quick as that for the more platform-oriented data architect or engineer. Of necessity, that supply of data scientists will have to be augmented by software that automates the interpretation of machine learning, but there’s only so far that you can program creativity and counter-intuitive insight into a machine.
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