analytics weekly roundup

Weekly Roundup | Top 10 IT Skills In Demand for 2018, Prescriptive Analytics, The Analytics Paradox

Andrea Steffes-Tuttle Weekly Roundup

We’re excited to kick off our new Weekly Roundup series, where we compile notable articles with interesting insights into the world of advanced analytics. This week’s roundup highlights the IT skills most in demand for 2018, takes a look at the value of prescriptive analytics, and discusses how to avoid stagnation in your analytics strategy.

Top 10 IT Skills in Demand for 2018

from TechSpective, by PK Agarwal, Dean and CEO of Northeastern University – Silicon Valley

We are facing an era of rapid change and widespread disruption for the technology industry. Anyone who stops advancing their career education will quickly become sidelined in such a fast-paced market. To stay relevant, IT professionals should continually seek to reskill and upskill themselves through the ongoing development of new technical proficiencies, and by expanding their professional networks. Here is a list of the most in-demand tech skills for 2018.

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What is Prescriptive Analytics?

from IoT for All, by Michael Riemer, entrepreneur

Did you say Predictive Analytics? No, prescriptive. Without a clear path to delivering valuable outcomes, current analytics deliver insufficient value. Prescriptive analytics goes beyond knowing and provides recommended actions based on prior outcomes.

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How a Good Analytics Strategy Can Become the Victim of Its Own Success

from Kellogg Insight, based on insights from Eric T. Anderson and Florian Zettelmeyer, Academic Directors of Kellogg’s Executive Education program on Leading with Big Data and Analytics

There’s a parable that Eric Anderson, a professor of marketing at the Kellogg School, likes to tell, one he’s deemed the “Analytics Paradox.” The paradox is that the better a firm gets at gleaning insights from analytics—and acting on those insights—the more streamlined their operations become. This, in turn, makes the data resulting from those operations more homogeneous. But over time, homogeneity becomes a problem: variable data—and, yes, mistakes—allow algorithms to continue to learn and optimize. As the variability in the new data shrinks, the algorithms don’t have much to work with anymore.

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Did you see an interesting article in the last week? Share it with us! Send it to astuttle [at] lityx.com.