AnalyticOps: Part 4 - What is AnalyticOps?

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How is AnalyticOps different than DevOps or Data Science?

Well, it took four parts to get to this point, but we’ve used our time to discuss some of the abstractions that are required to understand the idea referred to as “AnalyticOps”. Our journey started with the abstract concept of “what is an analytic” while the second covered the operative concept of “deploying” the analytic with an analytic engine or deployment server.  

Digging into the details, AnalyticOps encompasses the day-to-day activities, concerns, and focus of the person or people responsible for deploying analytics on the IT infrastructure. The AnaltyicOps function is accountable not to the data scientists nor the IT infrastructure leader, but directly to the business units depending on the analytics to make decisions. Let’s take a look at the role of an AnalyticOps specialist, and perhaps you’ll see that you, or someone on your team, is filling this role without even knowing it!  

One of the primary functions of an AnalyticOps specialist involves taking the analytics from the data science team, in whatever form they use (R, Python, a spreadsheet of values, etc.) and deploying them into the live data of the business. This might include verifying that the analytics work properly with respect to the computing infrastructure, the data sources, and the data science metrics like predictive quality.

An AnalyticOps specialist will also manage scaling the verified and working analytics. Production implementations may need to utilize more data,  with geographically distributed sources of data and compute resources. The AnalyticOps specialist takes care of these details., right down to optimizing the analytics utilization of specific computers.

Ensuring operational safety of deployed analytics is yet another responsibility of the AnaltyicOps specialist. For example, if a working, but incorrect analytic gets into a pricing engine, an organization can lose millions of dollars in a matter of minutes. A proper production implementation, owned via the AnalyticOps role, can prevent such costly errors from ever seeing the light of day.

But certainly practitioners in the data science or DevOps space will ask: “Isn’t this similar to what data scientists and DevOps people do?” Or, more bluntly, “I am doing that today, and we don’t need someone else to manage that stuff.” Certainly this is true today, but as the use of analytics becomes more mature, and abstractions like analytic deployment engines become more common, this must change. Separating out the specific tasks that are uniquely AnalyticOps allows an organization’s data scientists to instead pursue new machine learning algorithms, feature exploration, and value added analytic ideas. Similarly, DevOps folks will continue to focus on managing all that data an infrastructure, especially as Big Data continues its journey from disk (Apache Hadoop) to memory (Apache Spark) and on to network streams (Apache Storm).

For example, let’s say the data scientists come up with a recurrent neural net analytic that is 6% more predictive on the validation sets than the currently deployed random forest. The AnalyticOps specialist is uniquely positioned to not only verify that the neural net is indeed more predictive on live data, but to also see that the new method is 75% more computationally expensive on the existing infrastructure. With this visibility of predictive quality and operational cost, the business might decide to stay with the random forest because the ROI of the 6% increase for the cost that will be incurred can’t be justified.

Managing the life cycle of analytic assets--including their creation, updates, and cost histories--is another important role of the AnalyticOps specialist. Performing this role over an extended period of time allows the AnalyticsOps specialist to make minor (and at times major) adjustments to deploy analytics in a safe, process oriented, repeatable way. And the organization doubly benefits as this work does not distract the data scientists from finding “the next valuable analytic” or prevent the DevOps focus for wiring in that cool new feature for the client/customer.

So in short, AnalyticOps might not be that sexy when compared to “data science”, but like many extremely valuable and interesting technical jobs, it’s right in the heart of one of the biggest areas of value creation the tech world has seen in decades. AnalyticOps is quite literally a lynchpin of the analytics trend as organizations continue to increase the number of analytics that are critical to their mission.

Written by Stu Bailey