R-evolutionizing predictive analytics

Date: July 22, 2010
Pages: 13
Price:
US$ 895.00
Publisher: Ovum
Report type: Strategic Report
Delivery: E-mail Delivery (PDF)
ID: R7543B200F4EN
Leaflet:

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R-evolutionizing predictive analytics
The R open-source data mining language is quickly becoming the lingua franca of the budget-constrained data analyst who wants to harness the power of predictive analysis without a steep, complex, and expensive learning curve. Taking advantage of a gap in the market, Revolution Analytics was formed to commercialize R and raise its applicability in commercial settings. The company recently relaunched itself as an open-source commercialization company. It comes armed with an aggressive business strategy and product roadmap for 2010 aimed at bringing professional-grade performance and scalability and greater ease of use and productivity to predictive analytics.

Revolution Analytics is working off a solid foundation. R has always enjoyed a thriving open-source development community. It is now also gaining the serious attention of IT-savvy organizations such as Pfizer, Bank of America, and Google, which use the software for diverse applications such as discovering the next big drug, optimizing financial models, or managing online ad fees.

SUMMARY

Impact
Ovum view
Key messages

PREDICTIVE ANALYTICS – HOT PROPERTY IN BI

Boom time for predictive analytics
R signals a new wave of predictive analytics
Tackling complexity and cost
Rapid uptake

INTRODUCING REVOLUTION ANALYTICS

Revolutionizing predictive analytics
Business strategy – Red Hat for predictive analytics
Product roadmap
Academics program – gaining a stronger foothold at grass-roots level
Predicting a bright future for R

RECOMMENDATIONS

For enterprise IT users
Think about the business, not the code
Get the right GUI in place
R was not originally built with scalability in mind
Consider the true cost of employing predictive analytics
Potential lack of a support safety net
No official links to R’s creators
Potential for community conflict with “open-core” strategy

APPENDIX

Further reading
Methodology
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