Analytic applications are built by data scientists

February 1, 2012

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Ventana Research analyst David Menninger was on the judging panel for the Applications of R in Business contest. In a post on the Ventana research blog, he offers his perspectives on the contest, noting that

R, as a statistical package, includes many algorithms for predictive analytics, including regression, clustering, classification, text mining and other techniques. The contest submissions supported a variety of business cases, including, among others, predicting order amounts to optimize manufacturing processes,  predicting marketing campaign effectiveness to optimize marketing spendingpredicting liquid steel temperatures to optimize steel plant processes and performing sentiment analysis of Twitter data.

(Incidentally, David also has a great riff on the terminology of "predictive analytics" and "big data" out today.) He also notes that these applications are compelling precisely because of the close relationship between the contest entrants and the business problems they demonstrated how to solve:

The entries also demonstrated a best practice: close alignment between the analyst and the underlying business objectives. Predictive analytics is not magic. It requires an understanding of business processes and an understanding of statistical techniques. The judging criteria reflected this requirement as well. One of the three categories we were asked to score was applicability of the submission to business. I think it’s clear how the analyses in the winning entries could provide significant business value.

As David notes, however, the counterpoint to this is that the analyst must combine *both* the . "How many people in your organization could perform those types of analyses", he rightly asks. A combination of statistical tools along with domain expertise (plus the technical skills to implement the solution) is the hallmark of a good data scientist, which exactly why many organizations are looking to build effective data science teams.

By the way, while the concept of "data scientist" is relatively new, this idea of combining statistical analysts with domain expertise is not. Bill Cleveland (yes, that Bill Cleveland) made similar suggestions in a prescient paper back in 2001: "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics". (ISI Review, 69)

David Menninger: Revolution Analytics Hosts Contest on Business Predicting the Future

To leave a comment for the author, please follow the link and comment on their blog: Revolutions. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Tags: , ,

Comments are closed.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training



CRC R books series

Six Sigma Online Training

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)