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.
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
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