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Alteryx “14 for 14:  Analytics Predictions for 2014”

Tuesday, December 17th 2013
By Wes Stroh

As is the case this time of year, folks are offering up their predictions for what will come in 2014.  Alteryx presented a webinar recently on “14 for 14: Analytics Predictions for 2014” with panelists from Tableau, Cloudera and Revolution Analytics.  Most interesting to me were the following predictions:
R Will Replace Legacy SAS Solutions and Will Go Mainstream: R-based analytics goes mainstream at the expense of legacy environments like SAS & SPSS. With over 2 million users and another 3 million analysts looking for better solutions, R’s time is now!
 
Hadoop Moves From Curiosity to Critical: Hadoop moves beyond batch-based data processing and storage, to a general purpose compute infrastructure that will be core to the enterprise data fabric. This means analytics will continue to be the #1 use case for Big Data.
 
Gartner’s Prediction that the LOB Will Drive Analytics Spend Will Happen! Gartner’s prediction that in 2014 40% of BI and analytics spend will happen in the line of business, will actually happen. Increasing analytic adoption will drive investments in cloud and easier-to-use tools.
 
NoSQL Meets Analytics: The big data infrastructure one-two is completed as NoSQL’s move beyond operational databases to mixed workloads including analytics. MongoDB’s valuation will be validated by more shifts in the database market.
 
Business Ownership of Analytics Will Ignite an IT Revival: Faced with analysts demands, IT organizations will form strategic partnerships with individual departments in order to remain more relevant to the overall data-driven needs of the organization.
Notable trends are the democratization of complex analytical methods and lower cost of ownership of analytics, due to the proliferation of cloud-based tools, bringing analytic solutions directly to LOB analysts.  And panelists on the webinar discussed how the need for highly trained data scientists could be replaced by “empowered” analysts working directly with big data in marketing departments, for example, deriving their own insights.
 
You can read all 14 predictions and take a glance at the slides here, or register to watch the archived webinar here.
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