Karl Rexer sent me a few highlights from his 2015 Data Science Survey. This was formerly known as the Rexer Data Miner Survey but the term Data Scientist has surged in popularity so it has been renamed. I have blogged in the past about the survey and like many in the business I look forward to the results each year – full results should be released later this year. Meanwhile, some interesting factoids from the survey:
- Most people reported using multiple tools with a mean of 5 tools
- Top few tools / vendors when companies’ tools are combined together:
- 36.2% — R
- 11.7% — IBM SPSS Modeler or SPSS Statistics
- 11.5% — SAS or SAS Enterprise Miner or SAS JMP
- 7.9% — KNIME (free or commercial version)
- 5.1% — STATISTICA
- 4.6% — RapidMiner (free or commercial version)
- Over 60% of R users report that R Studio is their primary interface to R.
- Job satisfaction is high, but not as high as in 2013
- Only 40% feel their company has a high (or very high) degree of analytic sophistication
- The analytic workload is expanding – most analytic professionals foresee an increase in analytic projects (of corporate teams 89% foresee more analytic projects)
- Analytic teams are growing
The top analytic goals continue to revolve around customer data: improving understanding of customers, retaining customers, improving the customer experience, and improving selling. The most frequently used algorithms have remained consistent for many years: regression, clustering and decision trees have been the top algorithms since the research began in 2007. In 2015 more people report that their company has an active or pilot big data program (38% in 2015, compared to 26% in 2013). However, the size of the datasets people report they typically analyze has not grown.
I was struck once again by the poor rate of deployment and measurement – only 63% of analytic professionals report that their analytic results are usually or always deployed/utilized and only 50% report that their company usually or always measures the performance of analytic projects. We are big believers in the power of good analytic requirements to drive this up – check out this white paper on framing analytic requirements for instance.
You can get more info on the survey at rexeranalytics.com