Analytics: 2011 in Review and 2012 Predictions


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Now that the New Year is upon us it is time to review analytics in 2011 and look ahead to 2012.

2011 in review:

  • Analytics became a Sport: Several analytic competition sites became highly visible in 2011, the most prominent being These sites host analytic competitions that anyone can enter and provide a rich learning environment for both new and seasoned analytic professionals. The winners are not just those who place in the money – the winners are everyone who participates and learns within the community environment.
  • Moneyball and Analytics Hype: Speaking of sports, I’m a baseball fan so I naturally enjoyed the movie Moneyball. Analytics hype continued to climb in 2011 with the help of that movie. See this recent post for more on my thoughts on the state of analytics hype in 2011.
  • 2011 was the year of R (an open sourced analytic solution widely regarded as one of the most powerful analytic tools available): Corporate adoption gained momentum in 2011, as the corporate analytic community continued to understand the power of the solution and myths such as it is not a tool for production environments were quickly debunked.

2012 predictions:

  • Data Scientists- Evolution of the Analytic Professional: I read several articles and whitepapers in 2011 that estimated the gap between available analytic professionals and the ever increasing analytic work over the next few years. I do agree that a lack of analytic talent does exist, and will expand, but I believe that another significant gap will become more visible in 2012. That gap is current skills compared to the skills required to effectively use the newly evolving data and analytic environments. Analytic professionals need to evolve their skills required to analyze data in more complex technical systems (as an example, huge data environments that require lower level programming to access). Those who can analyze data efficiently in deep technical systems, with minimal help, and also are business domain experts, are deemed Data Scientists.
  • Analytics in the Cloud: Before cloud-based analytics could be adopted, cloud-based data systems had to be accepted, and those systems are now trusted by most as a viable solution (trusted being the key term as they have been viable for awhile now). Expect cloud-based, pay as you go, analytic solutions continue to take form and take off in 2012. Analytics in the Cloud is not a good fit for everyone currently … but it is a great fit for some.
  • The year of Analytic Reality: Analytics hype will peak in 2012 as executives demand a visible ROI towards their analytic investments. Nothing is wrong with hype, as hype enhances the education process, but hype needs to turn into reality. 2012 will be the year of transition from analytic hype to analytic reality.
  • Analytic Evolution Continues to be Supercharged by R: R is a full featured object-oriented analytic programming language that integrates well and is evolving much quicker than other available analytic solutions. R will continue to evade analytic environments in 2012, especially as R continues to progress into Big Data solutions with advances such as The Marriage Between Hadoop and R. R still has its drawbacks and is not for everyone: The primary take away here is that R has significantly enhanced analytic evolution, and will continue to do so, through a vibrant and dynamic open source analytic community. In turn, it will continue to push the big analytic providers to evolve. It is this evolutionary tidal wave that has yet to crest in the corporate world.

Republished with author's permission from original post.

Roman Lenzen
Roman Lenzen, Partner and Chief Data Scientist at Optumine, has delivered value added analytical processes to several industries for 20+ years. His significant analytical, technical, and business process experience provides a unique perspective on improving process efficiency and customer profitability. Roman was previously VP of Analytics at Quaero and Director of Analytics at Merkle. Roman's education includes a Bachelor of Science degree in Mathematics from Marquette University and Masters of Science in Statistics from DePaul University.


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