Forrester’s 2017 Take on Tools for Data Science

In my ongoing quest to track The Popularity of Data Science Software, I’ve updated the discussion of the annual report from Forrester, which I repeat here to save you from having to read through the entire document. If your organization is looking for training in the R language, you might consider my books, R for SAS and SPSS Users or R for Stata Users, or my on-site workshops.

Forrester Research, Inc. is a company that provides reports which analyze the competitive position of tools for data science. The conclusions from their 2017 report, Forrester Wave: Predictive Analytics and Machine Learning Solutions, are summarized in Figure 3b. On the x-axis they list the strength of each company’s strategy, while the y-axis measures the strength of their current offering. The size and shading of the circles around each data point indicate the strength of each vendor in the marketplace (70% vendor size, 30% ISV and service partners).

As with Gartner 2017 report discussed above, IBM, SAS, KNIME, and RapidMiner are considered leaders. However, Forrester sees several more companies in this category: Angoss, FICO, and SAP. This is quite different from the Gartner analysis, which places Angoss and SAP in the middle of the pack, while FICO is considered a niche player.

Figure 3b. Forrester Wave plot of predictive analytics and machine learning software.

In their Strong Performers category, they have H2O.ai, Microsoft, Statistica, Alpine Data, Dataiku, and, just barely, Domino Data Labs. Gartner rates Dataiku quite a bit higher, but they generally agree on the others. The exception is that Gartner dropped coverage of Alpine Data in 2017. Finally, Salford Systems is in the Contenders section. Salford was recently purchased by Minitab, a company that has never been rated by either Gartner or Forrester before as they focused on being a statistics package rather than expanding into machine learning or artificial intelligence tools as most other statistics packages have (another notable exception: Stata). It will be interesting to see how they’re covered in future reports.

Compared to last year’s Forrester report, KNIME shot up from barely being a Strong Performer into the Leader’s segment. RapidMiner and FICO moved from the middle of the Strong Performers segment to join the Leaders. The only other major move was a lateral one for Statistica, whose score on Strategy went down while its score on Current Offering went up (last year Statistica belonged to Dell, this year it’s part of Quest Software.)

The size of the “market presence” circle for RapidMiner indicates that Forrester views its position in the marketplace to be as strong as that of IBM and SAS. I find that perspective quite a stretch indeed!

Alteryx, Oracle, and Predixion were all dropped from this year’s Forrester report. They mention Alteryx and Oracle as having “capabilities embedded in other tools” implying that that is not the focus of this report. No mention was made of why Predixion was dropped, but considering that Gartner also dropped coverage of then in 2017, it doesn’t bode well for the company.


Jobs for “Data Science” Up 7-fold, for “Statistician” Down by Half

The Bureau of Labor Statistics projects that jobs for statisticians will grow by 34% between 2014 and 2024. However, according to the nation’s largest job web site, the number of companies looking for “statisticians” is actually in sharp decline. Those jobs are likely being replaced by postings for “data scientists.”

I regularly monitor the Popularity of Data Science Software, and as an offshoot of that project, I collected data that helps us understand how the term “data science” is defined. I began by finding jobs that required expertise in software used for data science such as R or SPSS. I then examined the tasks that the jobs entailed, such as “analyze data,” and looked up jobs based only on one task at a time. I switched back and forth between searching for software and for the terms used to describe the jobs, until I had a comprehensive list of both.  In the end, I had searched for over 50 software packages and over 40 descriptive terms or tasks. I had also skimmed thousands of job advertisements. (Additional details are here).


Search Terms 2/26/2017 2/17/2014 Ratio
Big Data 20,646            10,378 1.99
Data analytics 15,774              6,209 2.54
Machine learning 12,499              3,658 3.42
Statistical analysis 11,397              9,719 1.17
Data mining   9,757              7,776 1.25
Data Science 6,873                  973 7.06
Quantitative analysis 4,095              3,365 1.22
Business analytics  4,043              2,867 1.41
Advanced Analytics 3,479              1,497 2.32
Data Scientist 3,272                 974 3.36
Statistical software 2,835              2,102 1.35
Predictive analytics                 2,411              1,497 1.61
Artificial intelligence  2,404                 794 3.03
Predictive modeling 2,264              1,804 1.25
Statistical modeling 2,040              1,462 1.40
Quantitative research                 1,837              1,380 1.33
Research analyst                 1,756              1,722 1.02
Statistical tools                 1,414              1,121 1.26
Statistician 904              1,711 0.53
Statistical packages                    784                 559 1.40
Survey research 440                 559 0.79
Quantitative modeling                    352                  322 1.09
Statistical research 208                  174 1.20
Statistical computing                    153                  108 1.42
Research computing                    133                    97 1.37
Statistical analyst                    125                  141 0.89
Data miner 34                    19 1.79

Many terms were used outside the realm of data science. Other terms were used both in data science jobs and in jobs that require little analytic skill. Terms that could not be used to specifically find data science jobs were: analytics, data visualization, graphics, data graphics, statistics, statistical, survey, research associate, and business intelligence. One term, econometric(s), required deep analytical skills, but was too focused on one field.

The search terms that were well-focused on data science, but not overly focused in a single field are listed in the following table. The table is sorted by the number of jobs found on Indeed.com on February 26, 2017. While each column displays counts taken on a single day, the large size of Indeed.com’s database of jobs keeps its counts stable. The correlation between the logs of the two counts is quite strong, r=.95, p= 4.7e-14.

During this three-year period, the overall unemployment rate dropped from 6.7% to 4.7%, indicating a period of job growth for most fields. Three terms grew very rapidly indeed with “data science” growing 7-fold, and both “data scientist” and “artificial intelligence” tripling in size. The biggest surprise was that the use of the term “statistician” took a huge hit, dropping to only 53% of its former value.

That table covers a wide range of terms, but only on two dates. What does the long-term trend look like? Indeed.com has a trend-tracking page that lets us answer that question. The figure below shows solid the growth in the percentage of advertisements that used the term “data scientist” (blue, top right), while those using the term “statistician” (yellow, lower right) are steadily declining.

The plot on the company’s site is interactive (the one shown here is not) allowing me to see that the most recent data points were recorded on December 27, 2016. On that date, the percentage of jobs for data scientist were 474% of those for statistician.

As an accredited professional statistician, am I worried about this trend? Not at all. Statistical analysis software has broadened its scope to include many new capabilities including: machine learning, artificial intelligence, Structured Query Language, advanced visualization techniques, interfaces to Python, R, and Apache Spark. The software has changed because the job known as “statistician” has changed. Statisticians aren’t going away, their jobs are evolving into what we now know as data science. And that field is growing quite nicely!


Data Science Job Report 2017: R Passes SAS, But Python Leaves Them Both Behind

I’ve just updated another section of The Popularity of Data Science Software. It is reproduced below to save you the trouble of reading the entire article. If your organization is looking for training in the R language, you might consider my books, R for SAS and SPSS Users or R for Stata Users, or my on-site workshops.

Job Advertisements

One of the best ways to measure the popularity or market share of software for data science is to count the number of job advertisements for each. Job advertisements are rich in information and are backed by money so they are perhaps the best measure of how popular each software is now. Plots of job trends give us a good idea of what is likely to become more popular in the future.

Indeed.com is the biggest job site in the U.S., making its collection the best around. As their  CEO and co-founder Paul Forster stated, Indeed.com includes “all the jobs from over 1,000 unique sources, comprising the major job boards – Monster, Careerbuilder, Hotjobs, Craigslist – as well as hundreds of newspapers, associations, and company websites.” Indeed.com also has superb search capabilities and it includes a tool for tracking long-term trends.

Searching for jobs using Indeed.com is easy, but searching for software in a way that ensures fair comparisons across packages is tricky. Some software is used only for data science (e.g. SPSS, Apache Spark) while others are used in data science jobs and more broadly in report-writing jobs (e.g. SAS, Tableau). General-purpose languages (e.g. C, Java) are heavily used in data science jobs, but the vast majority of jobs that use them have nothing to do with data science. To level the playing field I developed a protocol to focus the search for each software within only jobs for data scientists. The details of this protocol are described in a separate article, How to Search for Data Science Jobs. All of the graphs in this section use those procedures to make the required queries.

I collected the job counts discussed in this section on February 24, 2017. One might think that a sample of on a single day might not be very stable, but the large number of job sources makes the counts in Indeed.com’s collection of jobs quite consistent. The last time I collected this data was February 20, 2014, and those that were collected using the same protocol (the general purpose languages) yielded quite similar results. They grew between 7% and 11%, and correlated r=.94, p=.002.

Figure 1a shows that SQL is in the lead with nearly 18,000 jobs, followed by Python and Java in the 13,000’s. Hadoop comes next with just over 10,000 jobs, then R, the C variants, and SAS. (The C, C++, and C# are combined in a single search since job advertisements usually seek any of them). This is the first time this report has shown more jobs for R than SAS, but keep in mind these are jobs specific to data science. If you open up the search to include jobs for report writing, you’ll find twice as many SAS jobs.

Next comes Apache Spark, which was too new to be included in the 2014 report. It has come a long way in an incredibly short time. For a detailed analysis of Spark’s status, see Spark is the Future of Analytics, by Thomas Dinsmore.

Tableau follows, with around 5,000 jobs. The 2014 report excluded Tableau due to its jobs being dominated by report writing. Including report writing will quadruple the number of jobs for Tableau expertise to just over 2o,ooo.

Figure 1a. The number of data science jobs for the more popular software (those with 250 jobs or more, 2/2017).

Apache Hive is next, with around 3,900 jobs, then a very diverse set of software comes next, with Scala, SAP, MATLAB, and SPSS, each having just over 2,500 data science jobs. After those, we see a slow decline from Teradata on down.

Much of the software had fewer than 250 job listings. When displayed on the same graph as the industry leaders, their job counts appear to be zero; therefore I have plotted them separately in Figure 1b. Alteryx comes out the leader of this group with 240 jobs. Microsoft was a difficult search since it appears in data science ads that mention other Microsoft products such as Windows or SQL Server. To eliminate such over-counting, I treated Microsoft different from the rest by including product names such as Azure Machine Learning and Microsoft Cognitive Toolkit. So there’s a good chance I went from over-emphasizing Microsoft to under-emphasizing it with only 157 jobs.

Figure 1b. The number of analytics jobs for the less popular software (under 250 jobs, 2/2017).

Next comes the fascinating new high-performance language Julia. I added FORTRAN just for fun and was surprised to see it still hanging in there after all these years. Apache Flink is also in this grouping, which all have around 125 jobs.

H2O follows, with just over 100 jobs.

I find it fascinating that SAS Enterprise Miner, RapidMiner, and KNIME appear with a similar number of jobs (around 90). Those three share a similar workflow user interface that make them particularly easy to use. The companies advertise the software as not needing much training, so it may be possible that companies feel little need to hire expertise if their existing staff picks it up more easily. SPSS Modeler also uses that type of interface, but its job count is about half that of the others, at 50 jobs.

Bringing up the rear is Statistica, which was sold to Dell, then sold to Quest. Its 36 jobs trails far behind its similar competitor, SPSS, which has a staggering 74-fold job advantage.

The open source MXNet deep learning framework, shows up next with 34 jobs. Tensorflow is a similar project with a 12-fold job advantage, but these two are both young enough that I expect both will be growing rapidly in the future.

In the final batch that has few, if any, jobs, we see a few newcomers such as DataRobot and Domino Data Labs. Others have been around for years, leaving us to wonder how they manage to stay afloat given all the competition.

It’s important to note that the values shown in Figures 1a and 1b are single points in time. The number of jobs for the more popular software do not change much from day to day. Therefore the relative rankings of the software shown in Figure 1a is unlikely to change much over the coming year. The less popular packages shown in Figure 1b have such low job counts that their ranking is more likely to shift from month to month, though their position relative to the major packages should remain more stable.

Each software has an overall trend that shows how the demand for jobs changes across the years. You can plot these trends using Indeed.com’s Job Trends tool. However, as before, focusing just on analytics jobs requires carefully constructed queries, and when comparing two trends at a time, they both have to fit in the same query limit. Those details are described here.

I’m particularly interested in trends involving R so let’s see how it compares to SAS. In Figure 1c we see that the number of data science jobs for SAS has remained relatively flat from 2012 until February 28, 2017 when I made this plot. During that same period, jobs for R grew steadily and finally surpassed jobs for SAS in early 2016. As noted in a blog post (and elsewhere in this report), use of R in scholarly publications surpassed those for SAS in 2015.

Figure 1c. Data science job trends for R (blue) and SAS (orange).

A long-standing debate has been taking place on the Internet regarding the relative place of Python and R. Ironically, this debate about data science software has involved very little actual data. However, it is possible now to at least study the job trends. Figure 1a showed us that Python is well out in front of R, at least on that single day the searches were run. What has the data looked like over time? The answer is shown in Figure 1d.

Figure 1d. Jobs trends for R (blue & lower) and Python (orange & upper).

As we see, Python surpassed R in terms of data science jobs back in 2013. These are, of course, very different languages and a quick scan of job descriptions will show that the R jobs are much more focused on the use of existing methods of analysis, while the Python jobs have more of a custom-programming angle to them.


Gartner’s 2017 Take on Data Science Software

In my ongoing quest to track The Popularity of Data Analysis Software, I’ve finally decided to change the title to use the newer term “data science”. The 2017 version of Gartner’s Magic Quadrant for Data Science Platforms was just published, so I have updated my IT Research Firms section, which I repeat here to save you from having to dig through the entire 40+ page tome. If your organization is looking for training in the R language, you might consider my books, R for SAS and SPSS Users or R for Stata Users, or my on-site workshops.

IT Research Firms

IT research firms study software products and corporate strategies, they survey customers regarding their satisfaction with the products and services,  and then provide their analysis on each in reports they sell to their clients. Each company has its own criteria for rating companies, so they don’t always agree. However, I find the reports extremely interesting reading. While these reports are expensive, the companies that receive good ratings often purchase copies to give away to potential customers. An Internet search of the report title will often reveal the companies that are distributing such copies.

Gartner, Inc. is one of the companies that provides such reports.  Out of the roughly 100 companies selling data science software, Gartner selected 16 which had either high revenue or lower revenue but high growth (see full report for details). After extensive input from both customers and company representatives, Gartner analysts rated the companies on their “completeness of vision” and their “ability to execute” that vision. Figure 3 shows the resulting plot. Note that purely open source software is not rated by Gartner, but nearly all the software in Figure 3 includes the ability to interact with R and Python.

The Leader’s Quadrant is the place for companies who have a future direction in line with their customer’s needs and the resources to execute that vision. The four companies in the Leaders quadrant have remained the same for the last three reports: IBM, KNIME, RapidMiner, and SAS. Of these, they rate IBM as having slightly greater “completeness of vision” due to the extensive integration they offer to open source software compared to SAS Institute. KNIME and RapidMiner are quite similar as the are driven by an easy to use workflow interface. Both offer free and open source versions, but RapidMiner’s is limited by a cap on the amount of data that it can analyze. IBM and SAS are market leaders based on revenue and, as we have seen, KNIME and RapidMiner are the ones with high growth.

Figure 3a. Gartner Magic Quadrant for Data Science Platforms

The companies in the Visionaries quadrant are those that have a good future plans but which may not have the resources to execute that vision. Of these, Microsoft increased its ability to execute compared to the 2016 report, and Alpine, one of the smallest companies, declined sharply in their ability to execute. The remaining three companies in this quadrant have just been added: H2O.ai, Dataiku, and Domino Data Lab.

Those in the Challenger’s quadrant have ample resources but less customer confidence on their future plans. Mathworks, the makers of MATLAB, is new to the report. Quest purchased Statistica from Dell, and it appears in roughly the same position as Dell did last year.

The Niche Players quadrant offer tools that are not as broadly applicable.

In 2017 Gartner dropped coverage of Accenture, Lavastorm, Megaputer, Predixion Software, and Prognoz.


R Passes SAS in Scholarly Use (finally)

Way back in 2012 I published a forecast that showed that the use of R for scholarly publications would likely pass the use of SAS in 2015. But I didn’t believe the forecast since I expected the sharp decline in SAS and SPSS use to level off. In 2013, the trend accelerated and I expected R to pass SAS in the middle of 2014. As luck would have it, Google changed their algorithm, somehow finding vast additional quantities of SAS and SPSS articles. I just collected data on the most recent complete year of scholarly publications, and it turns out that 2015 was indeed the year that R passed SAS to garner the #2 position. Once again, models do better than “expert” opinion!  I’ve updated The Popularity of Data Analysis Software to reflect this new data and include it here to save you the trouble of reading the whole 45 pages of it.

If you’re interested in learning R, you might consider reading my books R for SAS and SPSS Users, or R for Stata Users. I also teach workshops on R, but I’m currently booked through mid October, so please plan ahead.

Figure 2a. Number of scholarly articles found in the most recent complete year (2015) for each software package.

Figure 2a. Number of scholarly articles found in the most recent complete year (2015) for each software package.

Scholarly Articles

Scholarly articles are also rich in information and backed by significant amounts of effort. The more popular a software package is, the more likely it will appear in scholarly publications as an analysis tool or even an object of study. The software that is used in scholarly articles is what the next generation of analysts will graduate knowing, so it’s a leading indicator of where things are headed. Google Scholar offers a way to measure such activity. However, no search of this magnitude is perfect; each will include some irrelevant articles and reject some relevant ones. The details of the search terms I used are complex enough to move to a companion article, How to Search For Data Science Articles. Since Google regularly improves its search algorithm, each year I re-collect the data for all years.

Figure 2a shows the number of articles found for each software package for the most recent complete year, 2015. SPSS is by far the most dominant package, as it has been for over 15 years. This may be due to its balance between power and ease-of-use. For the first time ever, R is in second place with around half as many articles. Although now in third place, SAS is nearly tied with R. Stata and MATLAB are essentially tied for fourth and fifth place. Starting with Java, usage slowly tapers off. Note that the general-purpose software C, C++, C#, MATLAB, Java, and Python are included only when found in combination with data science terms, so view those as much rougher counts than the rest. Since Scala and Julia have a heavy data science angle to them, I cut them some slack by not adding any data science terms to the search, not that it helped them much!

From Spark on down, the counts appear to be zero. That’s not the case, the counts are just very low compared to the more popular packages, used in tens of thousands articles. Figure 2b shows the software only for those packages that have fewer than 1,200 articles (i.e. the bottom part of Fig. 2a), so we can see how they compare. Spark and RapidMiner top out the list of these packages, followed by KNIME and BMDP. There’s a slow decline in the group that goes from Enterprise Miner to Salford Systems. Then comes a group of mostly relative new arrivals beginning with Microsoft’s Azure Machine Learning. A package that’s not a new arrival is from Megaputer, whose Polyanalyst software has been around for many years now, with little progress to show for it. Dead last is Lavastorm, which to my knowledge is the only commercial package that includes Tibco’s internally written version of R, TERR.


Figure 2b. The number of scholarly articles for software that was used by fewer than 1,200 scholarly articles (i.e. the bottom part of Fig. 2a, rescaled.)

Figures 2a and 2b are useful for studying market share as it is now, but they don’t show how things are changing. It would be ideal to have long-term growth trend graphs for each of the analytics packages, but collecting such data is too time consuming since it must be re-collected every year (since Google’s search algorithms change). What I’ve done instead is collect data only for the past two complete years, 2014 and 2015. Figure 2c shows the percent change across those years, with the “hot” packages whose use is growing shown in red. Those whose use is declining or “cooling” are shown in blue. Since the number of articles tends to be in the thousands or tens of thousands, I have removed any software that had fewer than 500 articles in 2014.

Figure 2c. Change in the number of scholarly articles using each software in the most recent two complete years (2013 to 2014). Packages shown in red are "hot" and growing, while those shown in blue are "cooling down" or declining.

Figure 2c. Change in the number of scholarly articles using each software in the most recent two complete years (2014 to 2015). Packages shown in red are “hot” and growing, while those shown in blue are “cooling down” or declining.

Python is the fastest growing. Note that the Python figures are strictly for data science use as defined here. The open-source KNIME and RapidMiner are the second and third fastest growing, respectively. Both use the easy yet powerful workflow approach to data science. Figure 2b showed that RapidMiner has almost twice the marketshare of KNIME, but here we see use of KNIME is growing faster. That may be due to KNIME’s greater customer satisfaction, as shown in the Rexer Analytics Data Science Survey. The companies are two of only four chosen by IT advisory firm Gartner, Inc. as having both a complete vision of the future and the ability to execute that vision (Fig. 3a).

R is in fourth place in growth, and given its second place in overall marketshare, it is in an enviable position.

At the other end of the scale are SPSS and SAS, both of which declined in use by 25% or more. Recall that Fig. 2a shows that despite recent years of decline, SPSS is still extremely dominant for scholarly use. Hadoop use declined slightly, perhaps as people turned to alternatives Spark and H2O.

I’m particularly interested in the long-term trends of the classic statistics packages. So in Figure 2d I’ve plotted the same scholarly-use data for 1995 through 2015, the last complete year of data when this graph was made. As in Figure 2a, SPSS has a clear lead, but now you can see that its dominance peaked in 2008 and its use is in sharp decline. SAS never came close to SPSS’ level of dominance, and it also peaked around 2008. Note that the decline in the number of articles that used SPSS or SAS is not balanced by the increase in the other software shown in this particular graph. However, if you add up all the other software shown in Figure 2a, you come close. There still seems to be a slight decline in people reporting the particular software tool they used.


Figure 2d. The number of scholarly articles found in each year by Google Scholar. Only the top six “classic” statistics packages are shown.

Since SAS and SPSS dominate the vertical space in Figure 2d by such a wide margin, I removed those two curves, leaving only a single point of SAS usage in 2015. The the result is shown in Figure 2e. Freeing up so much space in the plot now allows us to see that the growth in the use of R is quite rapid and is pulling away from the pack (recall that the curve for SAS has a steep downward slope). If the current trends continue, R will cross SPSS to become the #1 software for scholarly data science use by the end of 2017. Stata use is also growing more quickly than the rest. Note that trends have shifted before as discussed here. The use of Statistica, Minitab, Systat and JMP are next in popularity, respectively, with their growth roughly parallel to one another.

Figure 2e. The number of scholarly articles found in each year by Google Scholar for classic statistics packages after market leaders SPSS and SAS have been removed.

Figure 2e. The number of scholarly articles found in each year by Google Scholar for classic statistics packages after the curves for SPSS and SAS have been removed.

Using a logarithmic y-axis scales down the more popular packages, allowing us to see the full picture in a single image (Figure 2f.)  This view makes it more clear that R use has passed that of SAS, and that Stata use is closing in on it. However, even when one studies the y-axis values carefully, it can be hard to grasp how much the logarithmic transformation has changed the values. For example, in 2015 value for SPSS is well over twice the value for R. The original scale shown in Figure 2d makes that quite clear.


Figure 2f. A logarithmic view of the number of scholarly articles found in each year by Google Scholar. This combines the previous two figures into one by compressing the y-axis with a base 10 logarithm.



Rexer Data Science Survey: Satisfaction Results

by Bob Muenchen

I previously reported on the initial results of Rexer Analytics’ 2015 survey of data science tools here. More results are now available, and the comprehensive report should be released soon.  One of the more interesting questions on the survey was, “Please rate your overall satisfaction with [your previously chosen software].” Most of the measures I report in my regularly-updated article, The Popularity of Data Analysis Software are raw measures of usage, so it’s nice to have data that goes beyond usage and into satisfaction. The results are show in the figure below for the more popular software (other software had very small sample sizes and so are not shown).


Results from the question, “Please rate your overall satisfaction with [your previously chosen software].” Only software with substantial number of responses shown.

People reported being somewhat satisfied with their chosen tool, which doesn’t come a much of a surprise. If they weren’t at least somewhat satisfied, they would be likely to move on to another tool. What really differentiated the tools was the percent of people who reported being extremely satisfied. The free and open source KNIME program came out #1 with 69% of its users being extremely satisfied. (KNIME is also the 2nd fastest growing data science package among scholarly researchers).  IBM SPSS Modeler came in second with 60%, followed closely by R with 57%.

Both of the top two packages use the workflow user interface which has many advantages that I’ve written about here and here. However, RapidMiner and SAS Enterprise Miner also use the workflow interface, and their percent of extremely satisfied customers were less than half at 32% and 29%, respectively. We might wonder if people are more satisfied with KNIME because they’re using the free desktop version, but RapidMiner also has a free version, so cost isn’t a factor on that comparison.

Although both R and SAS have menu-based interfaces, they are predominantly programming languages. R has almost triple the number of extremely satisfied users, which may be the result of its being generally viewed as the more powerful language, albeit somewhat harder to learn. The fact that R is free while SAS is not may also be a factor in that difference.

I’ve been learning KNIME and its interface to R, which looks like a stripped down version of RStudio. You can see a video demonstration by Heiko Hofer here. If you’re planning on attending the UseR! 2016 conference at Stanford University this year, stop by my poster session Helping Non-programmers Use R through the use of KNIME.


R’s Growth Continues to Accelerate

Each year I update the growth in R’s capability on The Popularity of Data Analysis Software. And each year, I think R’s incredible rate of growth will finally slow down. Below is a graph of the latest data, and as you can see, R’s growth continues to accelerate.

Since I’ve added coverage for many more software packages, I have restructured the main article to reflect the value of each type of data. They now appear in this order:

  • Job Advertisements
  • Scholarly Articles
  • IT Research Firm Reports
  • Surveys of Use
  • Books
  • Blogs
  • Discussion Forum Activity
  • Programming Popularity Measures
  • Sales & Downloads
  • Competition Use
  • Growth in Capability

Growth in Capability remains last because I only have complete data for R. To save you from having to dig through all 40+ pages of the article, the updated section is below. I’ll be updating several other sections in the coming weeks. If you’re interested, you can follow this blog, or follow me on Twitter as @BobMuenchen.

If you haven’t yet learned R, I recommend my books R for SAS and SPSS Users and R for Stata Users. I do R training as well, but that’s booked up through the end of August, so please plan ahead.

Growth in Capability

The capability of analytics software has grown significantly over the years. It would be helpful to be able to plot the growth of each software package’s capabilities, but such data are hard to obtain. John Fox (2009) acquired them for R’s main distribution site http://cran.r-project.org/ for each version of R. To simplify ongoing data collection, I kept only the values for the last version of R released each year (usually in November or December), and collected data through the most recent complete year.

These data are displayed in Figure 10. The right-most point is for version 3.2.3, released 12/10/2015. The growth curve follows a rapid parabolic arc (quadratic fit with R-squared=.995).


Figure 10. Number of R packages available on its main distribution site for the last version released in each year.

To put this astonishing growth in perspective, let us compare it to the most dominant commercial package, SAS. In version, 9.3, SAS contained around 1,200 commands that are roughly equivalent to R functions (procs, functions, etc. in Base, Stat, ETS, HP Forecasting, Graph, IML, Macro, OR, and QC). In 2015, R added 1,357 packages, counting only CRAN, or approximately 27,642 functions. During 2015 alone, R added more functions/procs than SAS Institute has written in its entire history.

Of course while SAS and R commands solve many of the same problems, they are certainly not perfectly equivalent. Some SAS procedures have many more options to control their output than R functions do, so one SAS procedure may be equivalent to many R functions. On the other hand, R functions can nest inside one another, creating nearly infinite combinations. SAS is now out with version 9.4 and I have not repeated the arduous task of recounting its commands. If SAS Institute would provide the figure, I would include it here. While the comparison is far from perfect, it does provide an interesting perspective on the size and growth rate of R.

As rapid as R’s growth has been, these data represent only the main CRAN repository. R has eight other software repositories, such as Bioconductor, that are not included in Fig. 10. A program run on 4/19/2016 counted 11,531 R packages at all major repositories, 8,239 of which were at CRAN. (I excluded the GitHub repository since it contains duplicates to CRAN that I could not easily remove.) So the growth curve for the software at all repositories would be approximately 40% higher on the y-axis than the one shown in Figure 10.

As with any analysis software, individuals also maintain their own separate collections available on their web sites. However, those are not easily counted.

What’s the total number of R functions? The Rdocumentation site shows the latest counts of both packages and functions on CRAN, Bioconductor and GitHub. They indicate that there is an average of 19.78 functions per package. Given the package count of 11,531, as of 4/19/2016 there were approximately 228,103 total functions in R. In total, R has approximately 190 times as many commands as its main commercial competitor, SAS.


Advanced Analytics Software’s Most Important Feature? Gartner Says it’s VCF

The IT research firm, Gartner, Inc. has released its February 2016 report, Magic Quadrant for Advanced Analytics Platforms. The report’s main graph shows the completeness of each company’s vision plotted against its ability to achieve that vision (Figure 1.) I include this plot each year in my continuously updated article, The Popularity of Data Analysis Software, along with a brief summary of its major points. The full report is always interesting reading and, if you act fast, you can download it free from RapidMiner’s web site.

Figure 1. Gartner Magic Quadrant for 2016. What’s missing?

If you compare Figure 1 to last year’s plot (Figure 2), you’ll see a few noteworthy changes, but you’re unlikely to catch the radical shift that has occurred between the two. Both KNIME and RapidMiner have increased their scores slightly in both dimensions. KNIME is now rated as having the greatest vision within the Leaders quadrant. Given how much smaller KNIME Inc. is than IBM and SAS Institute, that’s quite an accomplishment. Dell has joined them in the Leaders quadrant through its acquisition of Statistica. Microsoft increased its completeness of vison, in part by buying Revolution Analytics. Accenture joined the category through its acquisition of i4C Analytics. LavaStorm and Megaputer entered the plot in 2016, though Gartner doesn’t specify why. These are all interesting changes, but they don’t represent the biggest change of all.

The watershed change between these two plots is hinted at by two companies that are missing in the more recent one: Salford Systems and Tibco. The important thing is why they’re missing. Gartner excluded them this year, “…due to not satisfying the [new] visual composition framework [VCF] inclusion criteria.” VCF is the term they’re using to describe the workflow (also called streams or flowcharts) style of Graphical User Interface (GUI). To be included in the 2016 plot, companies must have offered software that uses the workflow GUI. What Garter is saying is, in essence, advanced analytics software that does not use the workflow interface is not worth following!


Figure 2. Gartner Magic Quadrant for 2015.

Though the VCF terminology is new, I’ve long advocated its advantages (see What’s Missing From R). As I described there:

“While menu-driven interfaces such as R Commander, Deducer or SPSS are somewhat easier to learn, the flowchart interface has two important advantages. First, you can often get a grasp of the big picture as you see steps such as separate files merging into one, or several analyses coming out of a particular data set. Second, and more important, you have a precise record of every step in your analysis. This allows you to repeat an analysis simply by changing the data inputs. Instead, menu-driven interfaces require that you switch to the programs that they create in the background if you need to automatically re-run many previous steps. That’s fine if you’re a programmer, but if you were a good programmer, you probably would not have been using that type of interface in the first place!”

As a programming-oriented consultant who works with many GUI-oriented clients, I also appreciate the blend of capabilities that workflow GUIs provide. My clients can set up the level of analysis they’re comfortable with, and if I need to add some custom programming, I can do so in R or Python, blending my code right into their workflow. We can collaborate, each using his or her preferred approach. If my code is widely applicable, I can put it into distribution as a node icon that anyone can drag into their workflow diagram.

The Gartner report offers a more detailed list of workflow features. They state that such interfaces should support:

  • Interactive design of workflows from data sources to visualization, modeling and deployment using dragging and dropping of building blocks on a visual pallet
  • Ability to parameterize the building blocks
  • Ability to save workflows into files and libraries for later reuse
  • Creation of new building blocks by composing sets of building blocks
  • Creation of new building blocks by allowing a scripting language (R, JavaScript, Python and others) to describe the functionality of the input/output behavior

I would add the ability to color-code and label sections of the workflow diagram. That, combined with the creation of metanodes or supernodes (creating one new building block from a set of others) help keep a complex workflow readable.


If Gartner’s shift in perspective resulted in them dropping only two companies from their reports, does this shift really amount to much of a change? Hasn’t it already been well noted and dealt with? No, the plot is done at the company level. If it were done at the product level, many popular packages such as SAS (with its default Display Manager System interface) and SPSS Statistics would be excluded.

The fields of statistics, machine learning, and artificial intelligence have been combined psychologically by their inclusion into broader concepts such as advanced analytics or data science. But the separation of those fields is still quite apparent in the software tools themselves. Tools that have their historical roots in machine learning and artificial intelligence are far more likely to have implemented workflow GUIs.  However, while they have a more useful GUI, they tend to still lack a full array of common statistical methods. For example, KNIME and RapidMiner can only handle very simple analysis of variance problems. When such companies turn their attention to this deficit, the more statistically oriented companies will face much stiffer completion. Recent versions of KNIME have already made progress on this front.

SPSS Modeler can access the full array of SPSS Statistics routines through its dialog boxes, but the two products lack full integration. Most users of SPSS Statistics are unaware that IBM offers control of their software through a better interface. IBM could integrate the Modeler interface into SPSS Statistics so that all its users would see that interface when they start the software. Making their standard menu choices could begin building a workflow diagram. SPSS Modeler could still be sold as a separate package, one that added features to SPSS Statistics’ workflow interface.

A company that is on the cutting edge of GUI design is SAS Institute. Their SAS Studio is, to the best of my knowledge, unique in its ability to offer four major ways of working. Its program editor lets you type code from memory using features far advanced from their aging Display Manager System. It also offers a “snippets” feature that lets you call up code templates for common tasks and edit them before execution. That still requires some programming knowledge, but users can depend less on their memory. The software also has a menu & dialog approach like SPSS Statistics, and it even has a workflow interface. Kudos to SAS Institute for providing so much flexibility! When students download the SAS University Edition directly from SAS Institute, this is the only interface they see.

SAS Studio currently supports a small, but very useful, percent of SAS’ overall capability. That needs to be expanded to provide as close to 100% coverage as possible. If the company can eventually phase out their many other GUIs (Enterprise Guide, Enterprise Miner, SAS/Assist, Display Manager System, SAS/IML Studio, etc.), merging that capability into SAS Studio, they might finally earn a reputation for ease of use that they have lacked.

In conclusion, the workflow GUI has already become a major type of interface for advanced analytics. My hat is off to the Gartner Group for taking a stand on encouraging its use. In the coming years, we can expect to see the machine learning/AI software adding statistical features, and the statistically oriented companies continuing to add more to their workflow capabilities until the two groups meet in the middle. The companies that get there first will have a significant strategic advantage.


Thanks to Jon Peck for suggestions that improved this post.


Using Discussion Forum Activity to Estimate Analytics Software Market Share

I’m finally getting around to overhauling the Discussion Forum Activity section of The Popularity of Data Analysis Software. To save you the trouble of reading all 43 pages, I’m posting just this section below.

Discussion Forum Activity

Another way to measure software popularity is to see how many people are helping one another use each package or language. While such data is readily available, it too has its problems. Menu-driven software like SPSS or workflow-driven software such as KNIME are quite easy to use and tend to generate fewer questions. Software controlled by programming requires the memorization of many commands and requiring more support. Even within languages, some are harder to use than others, generating more questions (see Why R is Hard to Learn).

Another problem with this type of data is that there are many places to ask questions and each has its own focus. Some are interested in a classical statistics perspective while others have a broad view of software as general-purpose programming languages. In recent years, companies have set up support sites within their main corporate web site, further splintering the places you can go to get help. Usage data for such sites is not readily available.

Another problem is that it’s not as easy to use logic to focus in on specific types of questions as it was with the data from job advertisements and scholarly articles discussed earlier. It’s also not easy to get the data across time to allow us to study trends.  Finally, the things such sites measure include: software group members (a.k.a. followers), individual topics (a.k.a. questions or threads), and total comments across all topics (a.k.a. total posts). This makes combining counts across sites problematic.

Two of the biggest sites used to discuss software are LinkedIn and Quora. They both display the number of people who follow each software topic, so combining their figures makes sense. However, since the sites lack any focus on analytics, I have not collected their data on general purpose languages like Java, MATLAB, Python or variants of C. The results of data collected on 10/17/2015 are shown here:


We see that R is the dominant software and that moving down through SAS, SPSS, and Stata results in a loss of roughly half the number of people in each step. Lavastorm follows Stata, but I find it odd that there was absolutely zero discussion of Lavastorm on Quora. The last bar that you can even see on this plot is the 62 people who follow Minitab. All the ones below that have tiny audiences of fewer than 10.

Next let’s examine two sites that focus only on statistical questions: Talk Stats and Cross Validated. They both report the number of questions (a.k.a. threads) for a given piece of software, allowing me to total their counts:


We see that R has a 4-to-1 lead over the next most popular package, SPSS. Stata comes in at 3rd place, followed by SAS. The fact that SAS is in fourth place here may be due to the fact that it is strong in data management and report writing, which are not the types of questions that these two sites focus on. Although MATLAB and Python are general purpose languages, I include them here because the questions on this site are within the realm of analytics. Note that I collected data on as many packages as were shown in the previous graph, but those not shown have a count of zero. Julia appears to have a count of zero due to the scale of the graph, but it actually had 5 questions on Cross Validated.

If you found this interesting, you can read about the results of other surveys and several other ways to measure software popularity here.

Is your organization still learning R?  I’d be happy to stop by and help. I also have a workshop, R for SAS, SPSS and Stata Users, on DataCamp.com. If you found this post useful, I invite you to follow me on Twitter.


Rexer Analytics Survey Results

Rexer Analytics has released preliminary results showing the usage of various data science tools. I’ve added the results to my continuously-updated article, The Popularity of Data Analysis Software. For your convenience, the new section is repeated below.

Surveys of Use

One way to estimate the relative popularity of data analysis software is though a survey. Rexer Analytics conducts such a survey every other year, asking a wide range of questions regarding data science (previously referred to as data mining by the survey itself.) Figure 6a shows the tools that respondents reported using in 2015.

Figure 6a. Analytics tools used.

Figure 6a. Analytics tools used by respondents to the Rexer Analytics Survey. In this view, each respondent was free to check multiple tools.

We see that R has a more than 2-to-1 lead over the next most popular packages, SPSS Statistics and SAS. Microsoft’s Excel Data Mining software is slightly less popular, but note that it is rarely used as the primary tool. Tableau comes next, also rarely used as the primary tool. That’s to be expected as Tableau is principally a visualization tool with minimal capabilities for advanced analytics.

The next batch of software appears at first to be all in the 15% to 20% range, but KNIME and RapidMiner are listed both in their free versions and, much further down, in their commercial versions. These data come from a “check all that apply” type of question, so if we add the two amounts, we may be over counting. However, the survey also asked,  “What one (my emphasis) data mining / analytic software package did you use most frequently in the past year?”  Using these data, I combined the free and commercial versions and plotted the top 10 packages again in figure 6b. Since other software combinations are likely, e.g. SAS and Enterprise Miner; SPSS Statistics and SPSS Modeler; etc. I combined a few others as well.

Figure 6b. The percent of survey respondents who checked each package as their primary tool.

Figure 6b. The percent of survey respondents who checked each package as their primary tool. Note that free and commercial versions of KNIME and RapidMiner are combined. Multiple tools from the same company are also combined. Only the top 10 are shown.

In this view we see R even more dominant, with over a 3-to-1 advantage compared to the software from IBM SPSS and SAS Institute. However, the overall ranking of the top three didn’t change. KNIME however rises from 9th place to 4th. RapidMiner rises as well, from 10th place to 6th. KNIME has roughly a 2-to-1 lead over RapidMiner, even though these two packages have similar capabilities and both use a workflow user interface. This may be due to RapidMiner’s move to a more commercially oriented licensing approach. For free, you can still get an older version of RapidMiner or a version of the latest release that is quite limited in the types of data files it can read. Even the academic license for RapidMiner is constrained by the fact that the company views “funded activity” (e.g. research done on government grants) the same as commercial work. The KNIME license is much more generous as the company makes its money from add-ons that increase productivity, collaboration and performance, rather than limiting analytic features or access to popular data formats.

If you found this interesting, you can read about the results of other surveys and several other ways to measure software popularity here.

Is your organization still learning R?  I’d be happy to stop by and help. I also have a workshop, R for SAS, SPSS and Stata Users, on DataCamp.com. If you found this post useful, I invite you to follow me on Twitter.

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