R is Hot: Part 5

[This article was first published on Revolutions, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

This the final installment of a five-part article series. You can download the complete article from the Revolution Analytics website.

Building a Business 

The value of R to business is borne out by the experiences of John Lucker and his team of advanced analytics professionals at Deloitte Consulting LLP. John is a Deloitte Consulting Principal and leads the firm’s Advanced Analytics and Modeling (AAM) practice, one of the leading analytics groups in the professional services industry.

When the group was launched fourteen years ago, its main focus was solving vexing business problems for clients in the insurance industry. One of the challenges facing the industry was the lack of robust analytic processes for supporting critical underwriting decisions. This challenge was particularly acute in the rapidly growing commercial insurance industry. “The commercial insurance underwriting process was rigorous but also quite subjective and based on intuition,” says John. 

Convincing experienced underwriting management to change their time-honored traditions was not an easy task.  That’s where R proved exceptionally helpful in recent years. “R enables us to communicate our analytic results in appealing and innovative ways to non-technical audiences through rapid development lifecycles,” says John. “Sometimes people know they have a problem, but they don’t know how to fix it. And sometimes they don’t even know they have a problem. R helps us show our clients how they can improve their processes and effectiveness by enabling our consultants to conduct analyses efficiently.”

The group’s success with clients in the insurance industry became a blueprint for expanding into new markets. Today, the Deloitte Consulting Advanced Analytics and Modeling practice also serves clients in healthcare, banking and financial services, retail, consumer products, telecomm, automotive, media, hospitality, public/state/federal sector and other major industries. R played an important role in growing the practice by allowing it to offer robust analytics addressing the specific needs of clients in a variety of markets.

“I find the diversity of R solutions and add-ons very appealing,” says John. “R has served as a catalyst in the marketplace. It forces everyone to raise their game, and it incents software developers to enhance their offerings. Everybody becomes more competitive and users of analytic tools benefit.”

Changing, transforming and evolving

With thousands of contributors and two million users worldwide, R is a truly global phenomenon. Unlike traditional commercial software for data analysis, R is both flexible and extensible. Supported by an active community of users and developers, R is constantly changing to meet the changing needs of our rapidly shifting global economy. 

The popularity of R is no fluke or fad. R has become the common language of data analysis because it was designed – from the ground up – as a practical system for handling the real-world challenges of complex data sets. R-based programs are applied routinely to solve problems in real-time trading, finance, risk assessment, forecasting, biotechnology, drug development, social networking and more.

But the wide acceptance of R as the lingua franca of statistics is based on its unique ability to change, to transform and to evolve. When new techniques in statistical analysis are discovered, they tend to emerge as R packages first – years before those innovations are incorporated in traditional enterprise software products.

Thanks to its open-source roots, R has spread virally across the map. It has become both ubiquitous and indispensable. The R community supports development, innovation and continuous improvement. New players are welcome and encouraged. The R eco-system has become a fertile breeding ground for novel ideas and original ways of thinking about numbers.

No one can foretell the future of quantitative analytics, but it’s safe to wager that a good deal of it will be written in R.

Read all parts of this series

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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)