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Why 2016 might be the year of citizen data scientists

Why 2016 might be the year of citizen data scientists

Find out what citizen data scientists will do in this new data ecosystem. Also, know the potential benefits and trade-offs to leaning on these pros for analytics.

I recently visited with Shawn Rogers, Chief Research Officer at Dell Statistica, a business unit within Dell's software group. Rogers predicted a growth in Internet of Things (IoT) big data initiatives in 2016, in addition to an enterprise push for more real-time IoT applications. He said that more big data and analytics applications would move to the edges of the enterprise instead of to a centralized, single data repository, thereby distributing big data pools across the enterprise.

"I think that 2016 could be the year of the citizen data scientist because users throughout the business want a more democratized approach to big data and analytics," said Rogers. "Not every company can afford a data scientist, which is a big reason why citizen data scientists will become a big part of the data ecosystem as it evolves."

What citizen data scientists will do in this new data ecosystem

In April 2015, Gartner research analyst Alexander Linden described citizen data scientists as "people on the business side that may have some data skills, possibly from a math or even social science degree — and putting them to work exploring and analyzing data."

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The theory is that true data scientists are relatively rare, expensive ($119,000 is the average annual salary), and that smart business folks with degrees in math or even social science could probably bridge the gap for a lot less salary. To be sure, there are trade-offs.

  • These workers are not likely to have any or perhaps very little background in data analytics.
  • There could be a greater risk of breaches in data compliance, security, and privacy requirements.
  • Lay data scientists could go off on their own to prepare and to interpret data, and come up with strategies that work against those of other business units.

However, a positive outcome could be that citizen data scientists could get closer to finding the answers a company wants from its analytics than a data scientist with not much of a background in business could offer.

For instance, would a pure data scientist analyzing the mode of freight that a large piece of equipment should be transported with know that the width of an ocean cargo carrying ship has to be able to accommodate the entire footprint of the equipment and its lashings? Or that the selection of a particular overland route has to consider not only road, air, and waterway infrastructure, but also whether the height of the equipment can pass under existing bridges?

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These are logistical question that aren't taught in college classes that a person experienced in the business is likely to know. This helps build the case for the citizen data scientist.

How companies and vendors can position themselves for citizen data scientists

Company business unit managers and HR should assess internal talent to determine which individuals have the potential to develop into citizen data scientists in end business units.

IT should be engaged to work with business management in the evolution of this potentially new type of data stewardship that establishes guidelines, conducts periodic audits, and provides best practices designed to maintain clean pools of data throughout the enterprise in a distributed data environment. This has seldom been done successfully in the past, so it is a tall order.

Vendors need to continue to do what they have been doing the past 12 months: deliver easy to use analytics tools that end users without extensive IT experience can use.

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