Welcome!

Apache Authors: Elizabeth White, Pat Romanski, Liz McMillan, Christopher Harrold, Janakiram MSV

Blog Feed Post

Yes, you need more than just R for Big Data Analytics

Douglas Merrill, former CIO/VP of Engineering at Google, writes in Forbes about using the R language for data analysis: Most folks with math-oriented graduate degrees will have written something in R, a non-commercial option for your big data analysis.  So, great graduates from great graduate schools know great tools. His post is titled 'R Is Not Enough For "Big Data"', and you might be surprised to learn that I agree that title, although for a different reason. Douglas's point — and it's a valid one — is that simply pumping data through any software tool, without an understanding of the problem you're trying to solve and how statistical models apply to it, can lead to getting the wrong answers to the wrong questions: If you ask the wrong question, you will be able to find statistics that give answers that are simply wrong (or, at best, misleading). On net, having a degree in math, economics, AI, etc., isn’t enough. Tool expertise isn’t enough.  You need experience in solving real world problems, because there are a lot of importat limitations to the statistics that you learned in school.  Big data isn’t about bits, it’s about talent. This is a great illustration of why the data science process is a valuable one for extracting information from Big Data, because it combines tool expertise with statistical expertise and the domain expertise required to understand the problem and the data applicable to it. He's right that you need data science talent and software to solve problems with Big Data ... and having software like R that supports the exploratory nature of the data science process is also critical. But I also agree with the title for a different, technical reason: the R software is just one piece of software ecosystem — an analytics stack, if you will — of tools used to analyze Big Data. For one thing R isn't a data store in its own right: you also need a data layer where R can access structured and unstructured data for analysis. (For example, see how you can use R to extract data from Hadoop in the slides from today's webinar by Antonio Piccolboni.) At the analytics layer, you need statistical algorithms that work with Big Data, like those in Revolution R Enterprise. And at the presentation layer, you need the ability to embed the results of the analysis in reports, BI tools, or data apps. So yes, Douglas is right: you need more than just R for Big Data. You also need a data layer, an analytics layer, and a presentation layer (all of which supports Big Data) ... and you need Data Science skills to make sure you're asking the right questions and getting appropriate answers. Forbes: R Is Not Enough For "Big Data"

Read the original blog entry...

More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

IoT & Smart Cities Stories
Every organization is facing their own Digital Transformation as they attempt to stay ahead of the competition, or worse, just keep up. Each new opportunity, whether embracing machine learning, IoT, or a cloud migration, seems to bring new development, deployment, and management models. The results are more diverse and federated computing models than any time in our history.
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
Digital Transformation is much more than a buzzword. The radical shift to digital mechanisms for almost every process is evident across all industries and verticals. This is often especially true in financial services, where the legacy environment is many times unable to keep up with the rapidly shifting demands of the consumer. The constant pressure to provide complete, omnichannel delivery of customer-facing solutions to meet both regulatory and customer demands is putting enormous pressure on...
IoT is rapidly becoming mainstream as more and more investments are made into the platforms and technology. As this movement continues to expand and gain momentum it creates a massive wall of noise that can be difficult to sift through. Unfortunately, this inevitably makes IoT less approachable for people to get started with and can hamper efforts to integrate this key technology into your own portfolio. There are so many connected products already in place today with many hundreds more on the h...
The standardization of container runtimes and images has sparked the creation of an almost overwhelming number of new open source projects that build on and otherwise work with these specifications. Of course, there's Kubernetes, which orchestrates and manages collections of containers. It was one of the first and best-known examples of projects that make containers truly useful for production use. However, more recently, the container ecosystem has truly exploded. A service mesh like Istio addr...
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...