Revolutions

On the relevance of open source analytics

"A growing body of evidence that indicates that the most meaningful way to access predictive analytics and enhance the reputation of Data Science is through open source analytics, which greatly hinges upon the free open source programming language R", according to Dataversity in the recent article "The Relevance of Open Source (Advanced) Analytics". The article also includes several business use cases for R. I was also interviewed for the article, and when asked why companies should invest in R as a data science platform, this was my reply:

“Investing in R, whether from the point of view of an individual Data Scientist or a company as a whole is always going to pay off because R is always available. If you’ve got a Data Scientist new to an organization, you can always use R. If you’re a company and you’re putting your practice on R, R is always going to be available. And, there’s also an ecosystem of companies built up around R including Revolution Enterprise to help organizations implement R into their machine critical production processes.”

You can read the entire article at the link below.

Dataversity: The Relevance of Open Source (Advanced) Analytics

Fast Company explains why R is good for business

In a new article for FastCoLabs, journalist Tina Amirtha has published a follow-up article to last month's piece on R's impact on open science. In her latest article, the focus is on how R is used at companies and displacing legacy statistics software like SAS:

SAS is no match for the open-source language that pioneering data scientists use in academia, which is simply known as R.

Tina interviews R users from companies Facebook and DataSong (as well as me and data scientist Casey Herron from Revolution Analytics) and discovers some cool applications of how R is used:

Facebook, for example, uses a technique called power analysis in order to figure out whether it has collected enough relevant data when it studies how users interact with new features on the site. It is all thanks to research data scientists who have developed the appropriate statistical tools in R and made them available to everyone.

The article points out the obvious reasons why R is good for business: it's open-source, it's great for data visualization, and because new research in statistics is done in R, it has far more capabilities (especially new and powerful algorithms) compared to proprietary tools. I wanted to point out one often-overlooked but, I believe, extremely important reason why R is good for businesses: people.

“I think the number one value to businesses [in using R] is access to talent,” says Smith. “So many businesses now are doing much more with data, especially with the big data revolution and doing much more with analytics. And because they’re hiring people coming out of school. They know R already.”

The data science talent shortage is a real problem for data driven businesses, but those companies that have adopted R as their platform have a supply of ready-trained R users graduating from academia (and who likely already know other cutting-edge open-source technologies to boot).

In other R-in-the-media news, Revolution Analytics' new program providing technical support for R was covered in Datanami, Computerworld, insideBigData and ZDnet.

FastCoLabs: Why The R Programming Language Is Good For Business

NYT uses R to forecast Senate elections

Nate Silver's departure to relaunch FiveThirtyEight.com left a bit of a hole at the New York Times, which The Upshot — the new data journalism practice at the Times — seeks to fill. And they've gotten off to a great start with the new Senate forecasting model, called Leo. Leo was created by Amanda Cox (longtime graphics editor at the NYT) and Josh Katz (creator of the Dialect Quiz), and uses a similar poll-aggregation methodology to that used by Silver. The model itself is implemented in the R language, and the R code is available for inspection at GitHub.

As of this writing, Leo suggests that the Democrats have a 51% chance of retaining the Senate in the 2014 elections. Now, probabilities are subtle things, and some commentators are prone to report from an estimate like this that "Leo predicts the Democrats will win the Senate in 2014". Of course, that would still be a risky bet to make, "essentially the same as a coin flip" as the Leo website says. As long as the probability isn't 0% or 100%, the actual outcome is always in doubt, dependent on factors we can't measure and over which we have no control. (In other words, luck.) The Upshot does a lovely job of demonstrating this variability with a feature that spins roulette wheels, loaded according to the data (mainly polls) for each race, and simulates one possible outcome.

This is a fantastic way to demonstrate the inherent variability in any statistical forecast, and it ties in with the underlying methodology as well. (For the statisticians out there, those wheels don't spin independently; correlations between individual races are taken into account.) Spin those wheels hundreds or thousands of times, count the number of times Democrats or Republicans win, and you have an estimate of the overall probability each party wins. Nicely done!

The UpShot: Senate Forecasts (via Sharon Machlis)

R and the weather in the local news

The Mountain View Voice is a weekly newspaper serving the Silicon Valley area, and is a familiar sight to anyone wandering the streets of Palo Alto or Menlo Park. Angela Hey writes for 'Hey Tech!', an online blog of the Voice, and has just published a feature on R and the local Bay Area User Group (BARUG). It includes a nice history of R, and an in-depth recap of Ram Narasimhan's lightning talk on the weatherData package and his weatherCompare app at the last BARUG meeting. (You can read about other talks at that BARUG meetup in Joe Rickert's recap.)

Read Angela Hay's feature from the Mountain View Voice blog at the link below.

Mountain View Voice: Analyze data yourself with R - a fast growing language for statistics, forecasting and graphs

DM Radio on Data Science

A couple of weeks ago, I participated in a panel discussion for DM Radio: "Still Sexy? How's that Data Scientist Gig Working Out?". The title was provocative, but the discussion mostly revolved around the rise of data science and how advanced analytics (often implemented with R) is changing the way many companies do business today. Also on the panel hosted by Eric Kavanagh were Geoffrey Malafsky of Phasic Systems, John Whittaker of Dell, Chandran Saravana of SAP. The podcast is now available online, which you can listen to at the link below. (And the answer is: Yes, still sexy!)

Information Management / DM Radio: Still Sexy? How's that Data Scientist Gig Working Out? (reg. req.)

R for Open Science

FastCompany magazine recently published an in-depth feature on Open Science, with a focus on the R language and the ROpenSci project. If you're not familiar with ROpenSci, the article gives a nice introduction from Ted Hart, a member of the ROpenSci development team:

A big sea change was the need to meet digital formatting requirements of scientific data. Hart and the rest of the team have created a set of packages that enables researchers to more easily share and store their research in standardized formats. The idea is the more shareable research is, the more science will progress. This is the foundation of the open science movement.

Large scientific publishers, like Nature and its forthcoming Scientific Data publication, are requiring researchers to submit their research data in specific metadata formats. Other scientific organizations also advocate pushing scientific data into various established repositories on the web in standardized formats.

Some of rOpenSci’s R packages can help these scientists streamline their data formats to fit the scientific community’s data standards.

Hart also described his earliest experiences with R as a PhD student in 2005:

“Most people I knew back then used SAS. It was just a giant, old, programming language, kind of like Fortran. It’s analyzed line by line and whatnot,” he says.

But when Hart started his post-doc in 2011, the lab where he did research only used R. “It was taught by this evolutionary biologist, Dolph Schluter. Every grad student I knew used it, as opposed to when I was a grad student. And I think I was the only one [who didn’t use R] in my department. So I’ve seen that growth take off,” says Hart.

Read more about the growth of R here, and read the complete FastCompany feature at the link below.

Fast Co.Labs: How The Rise Of The "R" Computer Language Is Bringing Open Source To Science

R: Free, Popular, Powerful, Flexible and Supported

Francis Smart offers five excellent reasons to use R, in a well-researched post ideal for sharing with anyone thinking about making the switch to R. (You might also share this YouTube video for a quick 90-second introduction to R.)

The post also includes a novel analysis of interest in R, as tracked by Google Trends. Given its single-letter name, comparitive metrics for R can be tricky to come by, but Google Trends lets you focus on interest in R the programming language (and not, say, the R Kelly album), and compare it to SAS the statistical software (not the airline) and other similar products. Google Trends also includes forecasts, and shows that not only does interest in R eclipse all others in Google searches, it's set to continue to grow:

(That's a static screenshot -- click to see an updated Google Trends analysis.)

Econometrics by Simulation: Why use R? Five reasons

Revolution Analytics in the News: Visionary in the Wired Cloud

Since we've had quite a few announcements over the last month or so, I thought I'd take a moment to catch up on some of the media reports mentioning Revolution Analytics.

Last week, Gartner revealed Revolution Analytics as a Visionary in the new Magic Quadrant for Advanced Analytics Platforms. Inside BigData noted that "Alteryx, Revolution Analytics, RapidMiner and Knime are the ones to watch in 2014", while SearchBusinessAnalytics also noted that "Revolution Analytics also received high marks". Enterprise Apps Today notes the prominence of open-source vendors in the Quadrant, and quotes me saying "traditional enterprise tools struggle to match the open-source community's ability to innovate, iterate and evolve rapidly". Meanwhile, Datanami noted that while SAS and IBM  are "King of [the] Analytics Hill" for now, the question is, "But for how long"? and summarized Revolution Analytics' position in the Magic Quadrant:

Revolution Analytics also fared quite well in Gartner's Magic Quadrant. The Mountain View, California-based company is credited with realizing the power of open source R, and has therefore become "the default choice for organizations without an existing provider seeking an R-based solution." High customer satisfaction and a strong sales pipeline are strengths.

Last month we also announced that Revolution R Enterprise is now available in the Amazon Cloud, with instances on Windows and Linux — including RStudio Server Pro — available on a pay-as-you go model for just $0.70 per core per hour (even less than the prices listed in Network World's New Product of the Week slideshow). ComputerWorld says this cloud-based service provides "an easy way for individuals and organizations to start and test their big-data-styled analysis projects". IT BusinessEdge notes that "users of the AWS service can run computations on data sets up to 1TB on Windows and Linux servers". 

Finally, our director of product management Thomas Dinsmore was honored with an article for Wired Innovation Insights, on building smarter organizations that include data scientists, power analysts, business analysts and analytic consumers.

 

Revolution Analytics named a Visionary in the Gartner Magic Quadrant for Advanced Analytics Platforms

The entire team at Revolution Analytics is very proud to announce that Gartner has named Revolution Analytics a Visionary in the inaugural Gartner Magic Quadrant for Advanced Analytics Platforms, published February 19, 2014. The report evaluated 16 vendors through a series of stringent criteria related to the ability to execute and completeness of vision.

Revolution Analytics is positioned the furthest for Completeness of Vision and Ability to Execute in the Visionaries Quadrant. We believe this is a validation of the leading-edge innovations of the open-source R community, and that of our own Revolution R Enterprise development team who continues to complement R with scalability, performance, and enterprise readiness. Here's what CEO Dave Rich has to say:

"It's such a pivotal moment for data scientists and the growing open-source R community that Gartner has embarked on its first ever Magic Quadrant for Advanced Analytics Platforms. Gartner estimates advanced analytics to be a $2 billion market that spans a broad array of industries globally, and ‘Gartner predicts business intelligence and analytics will remain top focus for CIOs Through 2017.’ We believe that this new Magic Quadrant puts a spotlight on big data as the great analytics disruptor and we feel highlights the need for solutions like Revolution Analytics' that are built upon a flexible, open platform, and designed for today's Big Data Big Analytics challenges." — Dave Rich, CEO, Revolution Analytics

Hear, hear! You can read more about the new Gartner Magic Quadrant at the link below:

Revolution Analytics press releases: Revolution Analytics Positioned in the “Visionaries” Quadrant of the First Ever Gartner Magic Quadrant for Advanced Analytics Platforms

About the Gartner Magic Quadrant

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Atlantic Feature on AI and R

A recent feature article in The Atlantic describes the rise of Artificial Intelligence (AI) and how researchers "conduct sophisticated data analyses using cloud resources such as Amazon Web Services and R, an open source application for statistics". (Via JP.)