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Doug Henschen
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Big Data Talent War: 7 Ways To Win

53% of big data-focused companies say analytics experts will be tough to find for the next two years. Here's how IT leaders plan to train, borrow, or steal talent--and what job seekers should know.
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Tip 3: Share Your Existing Analytics Expertise
Large companies and sophisticated companies often have analytics experts on staff. They're usually found in the research and development or finance departments. But some companies are pushing these groups to share the expertise.

Dow Chemical wanted to get more predictive throughout its business, so in 2005 it kicked off an experiment in which two analytics experts from R&D were asked to help out in operational areas. These experts helped the purchasing department develop a freight and logistics cost model to analyze about $2.8 billion in annual truck, rail, ship and airfreight costs worldwide. A supply chain effort led to a model to analyze $4 billion in annual raw materials spending. Both efforts helped Dow save big bucks by accurately predicting costs and enabling procurement people to buy early or wait to buy on better terms, reducing cost by renegotiating contracts.

Early successes at Dow led to a corporate-wide initiative in 2010 through which it has shifted 10 of its Ph.D.-level analytics professionals to work full time with business units to develop predictive and statistical forecasts. Enhanced sales forecasts backed by advanced analytics have reduced forecasting errors. Business units now know by mid-month whether they'll meet monthly performance targets so they can adjust their strategies accordingly. Exchange rate and margin analyses have helped Dow make decisions about where to buy raw materials and how to determine pricing of finished products.

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Tony Kontzer
Tony Kontzer,
User Rank: Apprentice
1/23/2013 | 8:26:14 PM
re: Big Data Talent War: 7 Ways To Win
I'm shocked to come to this story more than 2 months after it posted and see no comments here. This seems to me a valuable and insightful set of tips that can serve as a launch point for a discussion on how to best fill the data science talent gap. And I'm sure readers would be able to add more tips of their own--and maybe even offer counter-evidence questioning the effectiveness of one of the tips listed here. What say you, readers?

Tony Kontzer
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