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.
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
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