On Developers and Technology Adoption

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The ongoing promotion of developers from serf to kingmakers has many implications, but perhaps none so important as technology adoption. For decades, we’ve been seeing the manifestation of this trend, as the growth in market share of technologies from Chrome to Linux to Mac to MySQL have been driven at least in part by developer populations that preferred them. This was in evidence yet again last week on a Google Hangout James and I participated in organized by IBM.

In discussing the market for analytics, one of the topics of discussion was people, or more specifically the lackthereof. One of the least controversial statements one can make in the technology industry today is that demand for talent is outstripping the supply, at least in most markets. Analytics are no exception. As a result of this shortage, many organizations are the proverbial beggars now unable to be choosers. Where they may previously have hired for analytical roles only those trained on sanctioned analytical tools, businesses are now compelled to hire outside of these comfort zones. And in the analytical world, this most often benefits the statistical language R.

When developers – or in this case, statisticians – are permitted to choose their own tools, an increasing number turn to R if for no other reason than the fact that it was the basis for their academic training. So prevalent is R in academic environments, in fact, that the datasets associated with the text taught in my second semester of statistics were available as a downloadable package via R’s repository, CRAN.

What this means, then, is that like Linux, MySQL, PHP, AWS and other technologies before it, R is being annointed in bottom up fashion as a fundamentally important technology moving forward – it’s not often we get to watch this in real time. Whether enterprises approve of that or not. Even those conservative enterprises who wish to dictate technology choices to their rank and file will find that increasingly difficult in a tight labor market. Fragmentation is the new reality, and as developers make more choices – if only by default – the number of technologies employed within enterprises will inevitably rise. Those businesses that understand this at a fundamental level and do not seek to oppose it will have significant advantages in both hiring and productivity. Those vendors, meanwhile, that appreciate that hetereogeneity is the new norm and optimize for interoperability will, likewise, benefit.

As for the developers? With their newfound influence comes greater responsibility; just as enterprises need to be more flexible in technology adoption, so too should developers be more careful in the selection process. Heterogeneity is fine, even beneficial. Chaos, less so.

Disclosure: IBM is a RedMonk customer.


  1. I’m wondering whether R vs Python in data analysis will break down a bit like Chef vs Puppet, broadly speaking … people coming from different types of backgrounds preferring the style of one over the other.

  2. It’s an interesting question, and the parallels are certainly there. Certainly those from a development background are advantaging Python, while those trained in statistics are – as mentioned above – growing up with R. 

    I tend to think the two platforms won’t mirror each other quite as closely as Chef and Puppet, however, simply because their data capabilities aside, they’re pretty much Apples and Oranges.  

    Python is obviously a versatile, general purpose language, while R was created explicitly – and exclusively – to attack the problem of statistics. 

    So I expect that you’re correct that background will continue to influence adoption, but that R will benefit from its singularity of focus.

  3. First tried to convince Williams College to move to using R a decade ago. Five or six years later, they did!

    If you aren’t using R now, you will be someday.

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