Rexer Analytics 2011 Data Miner Survey: Summary Report

The report summarizing the Rexer Analytics 2011 Data Miner Survey is out. Among key findings: CRM / Marketing remains top application; R continues to climb and is used by almost half of respondents. Only 12% rate their company as having very high analytic sophistication.

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Rexer Analytics has released the 37 page summary report for its 2011 Data Miner Survey.

Over 1,300 data miners from 60+ countries responded to a 52 question survey. Participation was 40% Corporate, 27% Consultants, 18% Academics, 7% NGO/Gov't, and 8% Vendors.

Some of the key findings:

FIELDS & GOALS: Data miners work in a diverse set of fields. CRM / Marketing has been the #1 field in each of the past five years. Fittingly, "improving the understanding of customers," "retaining customers," and other CRM goals continue to be the goals identified by the most data miners.

ALGORITHMS: Decision trees, regression, and cluster analysis continue to form a triad of core algorithms for most data miners. However, a wide variety of algorithms are being used. A third of data miners currently use text mining and another third plan to in the future. Text mining is most often used to analyze customer surveys and blogs/social media.

TOOLS: R continued its rise this year and is now being used by close to half of all data miners (47%). R users report preferring it for being free, open source, and having a wide variety of algorithms. Many people also cited R's flexibility and the strength of the user community. STATISTICA is selected as the primary data mining tool by the most data miners (17%). Data miners report using an average of 4 software tools overall. STATISTICA, KNIME, Rapid Miner, and Salford Systems received the strongest satisfaction ratings in 2011.

TECHNOLOGY: Data Mining most often occurs on a desktop or laptop computer, and frequently the data is stored locally. Model scoring typically happens using the same software used to develop models.

ANALYTIC CAPABILITY AND SUCCESS: Only 12% of corporate respondents rate their company as having very high analytic sophistication. However, companies with better analytic capabilities are outperforming their peers. Respondents report analyzing analytic success via Return on Investment (ROI), and analyzing the predictive validity or accuracy of their models. Challenges to measuring analytic success include client or user cooperation and data availability/quality.

SHARED INSIGHTS:

• In the 2010 Survey data miners shared best practices in overcoming the key challenges data miners face.
• In the 2011 Survey data miners shared their best practices for measuring analytic success and
• examples of the positive impact that data mining can have to benefit society, health, and the world.
• In addition, 225 R users shared information about how and why they are using R

For more information contact Karl Rexer, PhD, krexer@RexerAnalytics.com
www.RexerAnalytics.com

See survey highlights at
rexeranalytics.com/Data-Miner-Survey-Results-2011.html.