(to be continued- as I find more stuff I will keep it there, some ideas- database access from R, prominent R consultants, prominent R packages, famous R interviewees 😉 )
ps- The quote from Jerry Rubin seems funny for a while. I turn 34 this year.
Revolution R Enterprise 4.2 is available immediately for 64-bit Red Hat Enterprise Linux systems and both 32-bit and 64-bit Windows systems. Pricing starts at $1,000 per single-user workstation
And its free for academic licenses- so come on guys it is worth atleast one download, and test.
Here is an interesting website by SAS.com – it showcases lots of business analytics content more from a conceptual rather than a tool based perspective- have a glance yourself.
An interesting announcemnet from Revolution Analytics promises to convert your legacy code in SAS language not only cheaper but faster. It’ s a very very interesting challenge and I wonder how SAS users ,corporates, customers as well as the Institute itself reacts
Are you paying for expensive software licenses and hardware to run time-consuming statistical analyses on big data sets?
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Fill out the short form below, and one of our conversion experts will contact you to discuss the SAS code you want to convert. If we think Revolution R Enterprise can get the same results faster than SAS, we’ll convert your code to R free of charge. Our goal is to demonstrate that Revolution R Enterprise will produce the same results in less time. There’s no obligation, but if you choose to convert, we guarantee that your license cost for Revolution R Enterprise will be less than half what you’re currently paying for the equivalent SAS software.**
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SAS is a registered trademark of the SAS Institute, Cary, NC, in the US and other countries.
*Additional statistical algorithms are being rapidly added to Revolution R Enterprise. Custom development services are also available.
**Revolution Analytics retains the right to determine eligibility for this offer. Offer available until March 31, 2011.
Carole-Ann’s 2011 Predictions for Decision Management
For Ajay Ohri on DecisionStats.com
What were the top 5 events in 2010 in your field?
Maturity: the Decision Management space was made up of technology vendors, big and small, that typically focused on one or two aspects of this discipline. Over the past few years, we have seen a lot of consolidation in the industry – first with Business Intelligence (BI) then Business Process Management (BPM) and lately in Business Rules Management (BRM) and Advanced Analytics. As a result the giant Platform vendors have helped create visibility for this discipline. Lots of tiny clues finally bubbled up in 2010 to attest of the increasing activity around Decision Management. For example, more products than ever were named Decision Manager; companies advertised for Decision Managers as a job title in their job section; most people understand what I do when I am introduced in a social setting!
Boredom: unfortunately, as the industry matures, inevitably innovation slows down… At the main BRMS shows we heard here and there complaints that the technology was stalling. We heard it from vendors like Red Hat (Drools) and we heard it from bored end-users hoping for some excitement at Business Rules Forum’s vendor panel. They sadly did not get it
Scrum: I am not thinking about the methodology there! If you have ever seen a rugby game, you can probably understand why this is the term that comes to mind when I look at the messy & confusing technology landscape. Feet blindly try to kick the ball out while superhuman forces are moving randomly the whole pack – or so it felt when I played! Business Users in search of Business Solutions are facing more and more technology choices that feel like comparing apples to oranges. There is value in all of them and each one addresses a specific aspect of Decision Management but I regret that the industry did not simplify the picture in 2010. On the contrary! Many buzzwords were created or at least made popular last year, creating even more confusion on a muddy field. A few examples: Social CRM, Collaborative Decision Making, Adaptive Case Management, etc. Don’t take me wrong, I *do* like the technologies. I sympathize with the decision maker that is trying to pick the right solution though.
Information: Analytics have been used for years of course but the volume of data surrounding us has been growing to unparalleled levels. We can blame or thank (depending on our perspective) Social Media for that. Sites like Facebook and LinkedIn have made it possible and easy to publish relevant (as well as fluffy) information in real-time. As we all started to get the hang of it and potentially over-publish, technology evolved to enable the storage, correlation and analysis of humongous volumes of data that we could not dream of before. 25 billion tweets were posted in 2010. Every month, over 30 billion pieces of data are shared on Facebook alone. This is not just about vanity and marketing though. This data can be leveraged for the greater good. Carlos pointed to some fascinating facts about catastrophic event response team getting organized thanks to crowd-sourced information. We are also seeing, in the Decision management world, more and more applicability for those very technology that have been developed for the needs of Big Data – I’ll name for example Hadoop that Carlos (yet again) discussed in his talks at Rules Fest end of 2009 and 2010.
Self-Organization: it may be a side effect of the Social Media movement but I must admit that I was impressed by the success of self-organizing initiatives. Granted, this last trend has nothing to do with Decision Management per se but I think it is a great evolution worth noting. Let me point to a couple of examples. I usually attend traditional conferences and tradeshows in which the content can be good but is sometimes terrible. I was pleasantly surprised by the professionalism and attendance at *un-conferences* such as P-Camp (P stands for Product – an event for Product Managers). When you think about it, it is already difficult to get a show together when people are dedicated to the tasks. How crazy is it to have volunteers set one up with no budget and no agenda? Well, people simply show up to do their part and everyone has fun voting on-site for what seems the most appealing content at the time. Crowdsourcing applied to shows: it works! Similar experience with meetups or tweetups. I also enjoyed attending some impromptu Twitter jam sessions on a given topic. Social Media is certainly helping people reach out and get together in person or virtually and that is wonderful!
Image via Wikipedia
What are the top three trends you see in 2011?
Performance: I might be cheating here. I was very bullish about predicting much progress for 2010 in the area of Performance Management in your Decision Management initiatives. I believe that progress was made but Carlos did not give me full credit for the right prediction… Okay, I am a little optimistic on timeline… I admit it… If it did not fully happen in 2010, can I predict it again in 2011? I think that companies want to better track their business performance in order to correct the trajectory of course but also to improve their projections. I see that it is turning into reality already here and there. I expect it to become a trend in 2011!
Insight: Big Data being available all around us with new technologies and algorithms will continue to propagate in 2011 leading to more widely spread Analytics capabilities. The buzz at Analytics shows on Social Network Analysis (SNA) is a sign that there is interest in those kinds of things. There is tremendous information that can be leveraged for smart decision-making. I think there will be more of that in 2011 as initiatives launches in 2010 will mature into material results.
Image by Intersection Consulting via Flickr
Collaboration: Social Media for the Enterprise is a discipline in the making. Social Media was initially seen for the most part as a Marketing channel. Over the years, companies have started experimenting with external communities and ideation capabilities with moderate success. The few strategic initiatives started in 2010 by “old fashion” companies seem to be an indication that we are past the early adopters. This discipline may very well materialize in 2011 as a core capability, well, or at least a new trend. I believe that capabilities such Chatter, offered by Salesforce, will transform (slowly) how people interact in the workplace and leverage the volumes of social data captured in LinkedIn and other Social Media sites. Collaboration is of course a topic of interest for me personally. I even signed up for Kare Anderson’s collaboration collaboration site – yes, twice the word “collaboration”: it is really about collaborating on collaboration techniques. Even though collaboration does not require Social Media, this medium offers perspectives not available until now.
Brief Bio-
Carole-Ann is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry. Her claim to fame is the strategy and direction of Blaze Advisor, the then-leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience. She speaks often at Industry conferences and has conducted university classes in France and Washington DC.
Leveraging her Masters degree in Applied Mathematics / Computer Science from a “Grande Ecole” in France, she started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication – as well as conducting strategic consulting gigs around change management.
She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs mostly around change management.
While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).
Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.
She also became addicted to Twitter in the process. She is active on all kinds of social media, always looking for new digital experience!
Outside of work, Carole-Ann loves spending time with her two boys. They grow fruits in their Northern California home and cook all together in the French tradition.
Additional features in R over other analytical packages-
1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature. This means bugs will found, shared and corrected transparently.
2) Wide literature of training material in the form of books is available for the R analytical platform.
3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.
4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.
5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.
6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.
Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.
Specific to the following domains R has the following costs and benefits
Business Analytics
R is free per license and for download
It is one of the few analytical platforms that work on Mac OS
It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
It has open source code for customization as per GPL
It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
Huge library of packages for regression, time series, finance and modeling
High quality data visualization packages
Data Mining
R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
Flexibility in tweaking a standard algorithm by seeing the source code
The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
Business Dashboards and Reporting
Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.
Additional factors to consider in your R installation-
There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R
2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.
3) Operating system sub choice- 32- bit or 64 bit.
4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.
5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?
6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.
7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.
1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.
Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.
Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
MacOS
Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.
Linux
Ubuntu
Red Hat Enterprise Linux
Other versions of Linux
Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.
Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.
Multiple operating systems-
Virtualization vs Dual Boot-
You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.
64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.
Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.
Hardware costs are a significant cost to an analytics environment and are also remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.
Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
Workstation
Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
Amazon
Google
Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
How much resources
RAM-Hard Disk-Processors- for workstation computing
Instances or API calls for cloud computing
Interface Choices
Command Line
GUI
Web Interfaces
Software Component Choices
R dependencies
Packages to install
Recommended Packages
Additional software choices
Additional legacy software
Optimizing your R based computing
Code Editors
Code Analyzers
Libraries to speed up R
citation- R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
I had recently asked some friends from my Twitter lists for their take on 2011, atleast 3 of them responded back with the answer, 1 said they were still on it, and 1 claimed a recent office event.
Anyways- I take note of the view of forecasting from
The most primitive method of forecasting is guessing. The result may be rated acceptable if the person making the guess is an expert in the matter.
Ajay- people will forecast in end 2010 and 2011. many of them will get forecasts wrong, some very wrong, but by Dec 2011 most of them would be writing forecasts on 2012. almost no one will get called on by irate users-readers- (hey you got 4 out of 7 wrong last years forecast!) just wont happen. people thrive on hope. so does marketing. in 2011- and before
and some forecasts from Tom Davenport’s The International Institute for Analytics (IIA) at
Regulatory and privacy constraints will continue to hamper growth of marketing analytics.
(I wonder how privacy and analytics can co exist in peace forever- one view is that model building can use anonymized data suppose your IP address was anonymized using a standard secret Coco-Cola formula- then whatever model does get built would not be of concern to you individually as your privacy is protected by the anonymization formula)
Anyway- back to the question I asked-
What are the top 5 events in your industry (events as in things that occured not conferences) and what are the top 3 trends in 2011.
I define my industry as being online technology writing- research (with a heavy skew on stat computing)
My top 5 events for 2010 were-
1) Consolidation- Big 5 software providers in BI and Analytics bought more, sued more, and consolidated more. The valuations rose. and rose. leading to even more smaller players entering. Thus consolidation proved an oxy moron as total number of influential AND disruptive players grew.
2) Cloudy Computing- Computing shifted from the desktop but to the mobile and more to the tablet than to the cloud. Ipad front end with Amazon Ec2 backend- yup it happened.
3) Open Source grew louder- yes it got more clients. and more revenue. did it get more market share. depends on if you define market share by revenues or by users.
Both Open Source and Closed Source had a good year- the pie grew faster and bigger so no one minded as long their slices grew bigger.
4) We didnt see that coming –
Technology continued to surprise with events (thats what we love! the surprises)
Revolution Analytics broke through R’s Big Data Barrier, Tableau Software created a big Buzz, Wikileaks and Chinese FireWalls gave technology an entire new dimension (though not universally popular one).
people fought wars on emails and servers and social media- unfortunately the ones fighting real wars in 2009 continued to fight them in 2010 too
5) Money-
SAP,SAS,IBM,Oracle,Google,Microsoft made more money than ever before. Only Facebook got a movie named on itself. Venture Capitalists pumped in money in promising startups- really as if in a hurry to park money before tax cuts expired in some countries.
2011 Top Three Forecasts
1) Surprises- Expect to get surprised atleast 10 % of the time in business events. As internet grows the communication cycle shortens, the hype cycle amplifies buzz-
more unstructured data is created (esp for marketing analytics) leading to enhanced volatility
2) Growth- Yes we predict technology will grow faster than the automobile industry. Game changers may happen in the form of Chrome OS- really its Linux guys-and customer adaptability to new USER INTERFACES. Design will matter much more in technology on your phone, on your desktop and on your internet. Packaging sells.
False Top Trend 3) I will write a book on business analytics in 2011. yes it is true and I am working with A publisher. No it is not really going to be a top 3 event for anyone except me,publisher and lucky guys who read it.
3) Creating technology and technically enabling creativity will converge at an accelerated rate. use of widgets, guis, snippets, ide will ensure creative left brains can code easier. and right brains can design faster and better due to a global supply chain of techie and artsy professionals.
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DECISION STATS 