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#Rstats continues its march in data mining

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From the famous KARL REXER ANNUAL DATA MINING SURVEY

HIGHLIGHTS from the 2013 Data Miner Survey:
  • SURVEY & PARTICIPANTS:  68-item survey conducted online in 2013.  Participants: 1,259 analytic professionals from 75 countries.  This is the 6th Data Miner Survey.
  • FOCUS ON CRM:  In the past few years, there has been an increase among data miners in the already substantial area of customer-focused analytics.  Respondents are looking for a better understanding of customers and seeking to improve the customer experience.  This can be seen in their goals, analyses, big data endeavors, and in the focus of their text mining.
  • BIG DATA:  Many in the field are talking about the phenomena of Big Data.  There are clearly some areas in which the volume and sources of data have grown.  However it is unclear how much Big Data has impacted the typical data miner.  While data miners believe that the size of their datasets have increased over the past year, data from previous surveys indicate that the size of datasets have been fairly consistent over time.
  • THE ASCENDANCE OF R:  The proportion of data miners using R is rapidly growing, and since 2010, R has been the most-used data mining tool.  While R is frequently used along with other tools, an increasing number of data miners also select R as their primary tool.
  • CHALLENGES IN THE USE OF ANALYTICS:  Data miners continue to report challenges at each level of the analytic process.  Companies often are not using analytics to their fullest and have continuing issues in the areas of deployment and performance measurement.
  • ENGAGEMENT & JOB SATISFACTION:  The Data Miners in our survey are highly engaged with the analytic community: consuming and producing content, entering competitions and searching for education and growth within their jobs.  All of these activities lead to high job satisfaction, which has been increasing over time.
  • ANALYTIC SOFTWARE:  Data miners are a diverse group who are looking for different things from their data mining tools.  Ease-of-use and cost are two distinguishing dimensions.  Software packages vary in their strengths and features.  STATISTICA, KNIME, SAS JMP and IBM SPSS Modeler all receive high satisfaction ratings.
  • OTHER FINDINGS include the labels analytic professionals use to describe themselves (Data Scientist is #1), the algorithms being used (regression, decision trees, and cluster analysis continue to be the triad of core algorithms), and computing environments (cloud computing is increasing).

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