Speaker 1: Welcome to your Research Business Daily Report sponsored today and all this week by 4i, the growth and foresight analytics leader.

Speaker 2: Hi, I'm Steve Adolph, former VP of Marketing for Cot Beverages. In putting together our five-year strategic plan, we considered 15 different growth opportunities. 4i Consulting focused us in on the four to five very best, and it had a positive effect on our business. I highly recommend you check out 4i Consulting.

Speaker 1: Yesterday, we introduced the issue of our language to our audience, and decision analyst, Senior VP John Collius spelled out some of the basics. If you missed the program on Wednesday, you should go back and take a look, because if you missed just a little bit of RVDR, you could be missing quite a lot. Today, John's going to share why our language is growing in popularity, some of its benefits, and how and when it could become that potential serious competitor for SPSS and SAS.

Speaker 3: One benefit is, as I mentioned, the access to a large variety of programs. So there's this cross-pollination across fields that happens, which is really a benefit of having access to these programs developed in other industries, too. Another benefit is that there's an effort in big data, analytics to make things work with big data and run faster, run things on clusters of machines, softwares being developed in the R language to do that, and arguably, perhaps more rapidly than in some of the paid-for software. So that provides an incentive to move in that. If your company is interested in moving in that direction, working with big data, work with lots of data and faster, that's another area where the R language could help a lot. And the other is just the knowledge. I mentioned the access to packages from other fields and the cross-pollination. But if you, as an individual, want to develop a package in your own industry, like the marketing research industry, and you want that package to do some things that you have an idea, but you don't necessarily have all the time in the world to spend on it, then you have an idea, but you don't necessarily have all the time in the world to spend on it. But if there are more people working on it, that code can be shared and those packages can be developed more quickly.

Speaker 1: There are a handful of models that the R language, right now, really provides a superior type capability. And so what are those?

Speaker 3: Taking advantage of choice modeling, taking advantage of being able to estimate a model in, say, two hours rather than four hours. That kind of a thing is an area where the R language could be of benefit. Okay. In predictive modeling, predictive analytics, where you're doing database modeling and traditional modeling in that area, that would be an area where you can go and get a package and do any kind of model you'd want to do and do it relatively efficiently. And it's off the shelf. It's open source, so there's no cost to doing that. So if you're a company that's expanding into that area, for example, you can have access to an open source software before paying more money for the existing paid-for software.

Speaker 1: With so many organizations out there providing an R language software of their own, it doesn't sound to me like this is a terribly expensive thing to put together.

Speaker 3: Yes and no. I mean, it's open source, so free. But on the other hand, if you're developing your own package, it takes time to do it. And, you know, all of us in the research industry are always working on deadlines and so forth. And so there is an opportunity cost there to spending the time to do it. And then there's the time and training the staff to use the R language. That cost is becoming less over time because more and more people are coming out of bachelor's degrees and master's degrees already. Having had exposure to the R language in their coursework. That is not uncommon these days.

Speaker 1: Can you see into the near future, or maybe it's further out than that, a time when R language software is going to really compete with SPSS and SAS? And if so, how far off might that be?

Speaker 3: I would say five to ten years. For the more advanced methods, I think that people who are interested in the R language and who like working with analytics and like working with software will get in. And there will be a group of people who are doing that even now and next year. So the timeframe for a certain group of the people is earlier.

Speaker 1: John wanted us to pass on to our audience the fact that decision analysts is looking for partners to continue working on their R language work. It could be of great benefit to everybody in the industry. That's your Research Business Daily Report sponsored by 4i, the growth and foresight analytics leader. Have a great research day, a great rest of your research week, and plan on joining us again after your weekend come Monday. Thank you.