Bridging the Gap Between Data Science and DevOps

Bridging the Gap Between Data Science and DevOps

What’s the real value of data science? Hailed as the sexiest job of the 21 century just a few years ago, there are rumours that it’s not quite proving its worth. Gianmario Spacnaga, a data scientist for Barclays bank in London, told Computing magazine at Spark Summit Europe in October 2015 that, in many instances, there’s not enough impact from data science teams – “It’s not a playground. It’s not academic”. His solution sounds simple. We need to build a bridge between data science and DevOps:

This idea makes a lot of sense. It’s become clear over the past few years that ‘data’ itself isn’t enough; it might even be distracting for some organizations. Sometimes too much time is spent in spreadsheets and not enough time is spent actually doing stuff. Making decisions, building relationships, building things – that’s where real value comes from.

What Spacnaga has identified is ultimately a strategic flaw within how data science is used in many organizations. There’s often too much focus on what data we have and what we can get, rather than who can access it and what they can do with it. If data science isn’t joining the dots, DevOps can help. True, a large part of the problem is strategic, but DevOps engineers can also provide practical solutions by building dashboards and creating APIs. These sort of things immediately give data additional value by making they make it more accessible and, put simply, more usable. Even for a modest medium sized business, data scientists and analysts will have minimal impact if they are not successfully integrated into the wider culture.

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While it’s true that many organizations still struggle with this, Airbnb demonstrate how to do it incredibly effectively. Take a look at their Airbnb Engineering and Data Science publication on Medium. In this post, they talk about the importance of scaling knowledge effectively. Although they don’t specifically refer to DevOps, it’s clear that DevOps thinking has informed their approach. In the products they’ve built to scale knowledge, for example, the team demonstrate a very real concern for accessibility and efficiency. What they build is created so people can do exactly what they want and get what they need from data. It’s a form of strict discipline that is underpinned by a desire for greater freedom.;



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