Revolution Analytics Boosts the Adoption of R in the Enterprise

The path to competitive advantage is being able to make predictions from Big Data. Therefore, the more you can build predictive analytics into your business processes, the more successful your organization will become. There is no doubt that open-source R is the programming language of choice for predictive analytics, and thanks to Revolution Analytics, R has the enterprise capabilities needed to drive adoption across the organization and for every employee to make data-driven decisions.

Revolution Analytics is to R what the vendor RedHat is to the Linux operating system—a company devoted to enhancing and supporting open-source software for enterprise deployments. For example, Revolution Analytics recently released R Enterprise 7 to meet the performance demands of Big Data whereby R now runs natively within Hadoop and data warehouses. I spoke with David Smith, VP of Marketing at Revolution Analytics to explain how Revolution Analytics has accelerated the adoption of R in the enterprise.

1.  What benefits do Revolution Analytics provide to organizations over just using open-source R?

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Alpine Data Labs – Making Predictive Analytics Pervasive and Persuasive

Big Data has exposed the need for deeper data insights through predictive analytic techniques such as data mining, machine learning, and modeling. The interesting thing to note is that predictive analytics has been around for a long time, used by a select few, in select organizations. Its value has always been recognized and applauded, but its true potential never fully realized due to lack of widespread adoption, as well as issues around data accessibility, performance, statistical expertise, business sponsorship, cost, and more. In fact, nearly 90 percent of organizations that do employ predictive analytic software agree that it has given them a competitive advantage, according to a new survey.

The advent of Big Data has driven the uptake of predictive analytics due to the curiosity of very capable Data Scientists, along with new tools and technologies from companies such as Alpine Data Labs.  Alpine Data Labs provides next generation predictive analytics to address legacy issues and meet the new demands of Big Data. But more importantly, Alpine Data Labs is mainstream-oriented whereby business users, not just statisticians and Data Scientists, are compelled to mine data.

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Backed by $16M in Series B funding, Alpine Data Labs is getting some serious momentum in the Big Data analytics startup space, offering zero coding for creating and deploying complex predictive models on Hadoop. I spoke with Alpine Data Labs CEO Joe Otto to talk about their game changing approach to predictive analytics for Big Data.

1.  Lets first talk about leading predictive analytics incumbents such as SAS, IBM SPSS, and other analytics vendors who got their start years ago with desktop and server software designed for data mining and advanced analytics. How has Alpine Data Labs overcome the issues around these incumbent technologies and address the new needs of Big Data?

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Big Data Brings Sales and Marketing Closer Together

Over 50% of the Big Data business opportunity comes from better understanding your customers.  As a result, organizations with Sales and Marketing departments are finally aligning together by simply aligning around high value customers.  EMC is a great example of Sales and Marketing teams ending the turf war, as both departments are working together to create a centralized customer analytics database to better identify customer segments for upsell/cross sell opportunities, target prospects who are more likely to bring in more value, and optimize operations.  This Big Data transformation now enables Sales and Marketing to work off of the same customer account data to harmoniously build the EMC brand and business.

I became very interested in documenting the success of Big Data here at EMC so I captured one of the use cases around optimizing operations for EMC Maintenance and Renewals.  Click here for this newly published Big Data success story that details how EMC gained an incremental $113M above revenue goal from a Big Data strategy that involved the right people, processes, and technology.

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Big Data No Longer Lost in Translation

Traditional BI makes it very difficult for people in the business who know the story behind the data to actually gain direct access to the data.  Instead, they submit data requirements to IT and when IT does finally deliver the data, it is typically only a subset or incomplete data, and in the wrong format.  When data gets lost in translation, business users become frustrated, abandon analytics altogether, and operate on hunches and guesses.  Fortunately Tableau solves this problem through its Self Service BI paradigm whereby any user in the organization can quickly gain direct access to the data needed, with flexibility to create any visualization imaginable (goodbye Excel!).  But wait, there is more.  Tableau has partnered with Pivotal to add a social element to these Self Service BI capabilities, whereby people in the business, data scientists, and IT can come together as a team to collaborate around data sets, visualizations, predictive models, and more to uncover new and better insight.  The result – Big Data No Longer Lost in Translation.

Click inside to watch 11 Tableau customers talk about how Self Service BI has changed the way they do business

I spoke with Ted Wasserman, a Product Manager at Tableau to learn more about the value of their technology and partnership with Pivotal.

1.  Let’s first talk about Tableau. Describe what part of the analytical process Tableau fits in and what problems it solves?

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