New analytical tools to watch out for


Here is a list of new and upcoming analytical tools to watch out for in the coming year. It is based on actual corporate announcements as well as a forecast based on version upgrades by existing products in analytics.

New Tools Old Players

· Revolution Analytics GUI for Enterprise R I am looking forward to the new Graphical User Interface that Revolution Analytics has announced within 2011. Revolution Analytics is the leading vendor for enterprise solutions based on the open source analytics platform called R. Some of the pioneering work by Revolution Analytics has been helping R break the big data barrier with its RevoScaleR package,parallel computing with foreach and web services with RevoDeployR among other contributions. The new GUI for R is expected to further cut down on learning and adoption time for R, which is the last remaining drawback in that platform.


SAP HANA is the in-memory analytics software by SAP, and is the latest product by the veteran Business Intelligence leader. It is supposed to make analytical processing very fast by a combination of custom software and hardware and is more of an appliance than a tool.

-New Tools New Players This is a list of newer tools by relatively newer entrants and players in the business analytics space.

· R Studio– The initial reaction to the integrated development environment by RStudio has been very positive. R Studio also has a very convenient server version and the next upgrade in this software is likely to be an exciting one

· Platfora– This is a promising tool to use analytics with Hadoop for data exploration purposes using graphical interfaces, a step that is likely to gather steam as more business analysts navigate to big data analytics without needing to know the coding aspects and nitty-gritties of the data handling in Hadoop. More Hadoop based analytic tools should also be a logical next step.

· Machine Generated Data Analysis – Using tools like splunk we can now collect and index a lot of machine generated data, adding in analytics to this would be the next step. Analysis of mobile generated logs would be even more interesting

· BI based tools on R– While R has one of the largest libraries in advanced analytics, the relatively simple tasks of building reporting dashboards, pulling data for Business Intelligence purposes can help R crack the lucrative BI tools market.

· Python– Python could be the analytic platform for statistical computing by expanding its library of analytical and graphical packages. Just like R packages have helped it navigate and leverage the efficiencies of algorithms originally written in other languages, Python hackers can help execute and port data visualization and analytics package from R. Data Visualization tools and business analytics GUIs and Business Intelligence Dashboards in Python could be a game changer given the faster speeds of access.

Old Tools New Upgrades IBM

Though IBM has been on tear to acquire companies, much greater cohesive synergy and cooperation in its product suite in Analytics and Optimization tools is expected to be executed in the coming months. If SPSS Modeller/Clementine, Netezza, Cognos and Base SPSS can be integrated in cohesive intuitive interfaces , they can help cut down processing and execution time of a lot of analytics.

SAS Institute– Greater integration with R. While SAS Institute is big and strong enough to tackle both R as an alternative to analytics licensing and IBM to higher margin analytics, with SAP to lead in the visualization space and Oracle to continue to dominate the data storage paradigms, there exists a latent and formidable product portfolio in the SAS Institute Stack. The SAS Enterprise Miner new version is expected to make it a leading GUI based data mining tool , while same ix expected in the next version of Enterprise Guide. It would also be prudent to expect better co-bundling of JMP (for Data Discovery) , Enterprise Miner (for Data Mining) ,SAS ETS ( for Forecasting) and SAS SPDS or equivalent (for big Data)

Cloud Tools In cloud computing the following are expected to make some impact in analytics. = Cloud library for Pyhton based computing – Cloud Computing using browser , R, Python and customizable stack Google Predictive API- Making this API , easier to use for the corporate analytics customer with any data security apprehensions and hygiene removed, and by co-bundling or investing in R /Python libraries/interfaces can help make the truly impressive Google Predictive API much more mainstream. This is a simple summary of tools in analytics that can change the world of analytics and maybe even change the world of analytics employment. The analytical tools to watch out for!

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Image courtesy of patrisyu at
Image courtesy of patrisyu at
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