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UPDATED POST- Some Models I use for Business Strategy- to analyze the huge reams of qualitative and uncertain data that business generates. I have added a bonus the Business canvas Model (number 2)
- Porters 5 forces Model-To analyze industries
- Business Canvas
- BCG Matrix- To analyze Product Portfolios
- Porters Diamond Model- To analyze locations
- McKinsey 7 S Model-To analyze teams
- Gernier Theory- To analyze growth of organization
- Herzberg Hygiene Theory- To analyze soft aspects of individuals
- Marketing Mix Model- To analyze marketing mix.
Analytics 2012 Conference
SAS and more than 1,000 analytics experts gather at
Analytics 2012 Conference Details
Pre-Conference Workshops – Oct 7
Conference – Oct 8-9
Post-Conference Training – Oct 10-12
Caesars Palace, Las Vegas
The following are confirmed keynote speakers for Analytics 2012. Since he co-founded SAS in 1976, Jim Goodnight has served as the company’s Chief Executive Officer.
Dr. William Hakes is the CEO and co-founder of Link Analytics, an analytical technology company focused on mobile, energy and government verticals.
Tim Rey has written over 100 internal papers, published 21 external papers, and delivered numerous keynote presentations and technical talks at various quantitative methods forums. Recently he has co-chaired both forecasting and data mining conferences. He is currently in the process of co-writing a book, Applied Data Mining for Forecasting.
Plan to come to Analytics 2012 a day early and participate in one of the pre-conference workshops or take a SAS Certification exam. Prices for all of the preconference workshops, except for SAS Sentiment Analysis Studio: Introduction to Building Models and the Business Analytics Consulting Workshops, are included in the conference package pricing. You will be prompted to select your pre-conference training options when you register.
Sunday Morning Workshop
SAS Sentiment Analysis Studio: Introduction to Building Models
This course provides an introduction to SAS Sentiment Analysis Studio. It is designed for system designers, developers, analytical consultants and managers who want to understand techniques and approaches for identifying sentiment in textual documents.
Sunday, Oct. 7, 8:30a.m.-12p.m. – $250
Sunday Afternoon Workshops
Business Analytics Consulting Workshops
This workshop is designed for the analyst, statistician, or executive who wants to discuss best-practice approaches to solving specific business problems, in the context of analytics. The two-hour workshop will be customized to discuss your specific analytical needs and will be designed as a one-on-one session for you, including up to five individuals within your company sharing your analytical goal. This workshop is specifically geared for an expert tasked with solving a critical business problem who needs consultation for developing the analytical approach required. The workshop can be customized to meet your needs, from a deep-dive into modeling methods to a strategic plan for analytic initiatives. In addition to the two hours at the conference location, this workshop includes some advanced consulting time over the phone, making it a valuable investment at a bargain price.
Sunday, Oct. 7; 1-3 p.m. or 3:30-5:30 p.m. – $200
Demand-Driven Forecasting: Sensing Demand Signals, Shaping and Predicting Demand
This half-day lecture teaches students how to integrate demand-driven forecasting into the consensus forecasting process and how to make the current demand forecasting process more demand-driven.
Sunday, Oct. 7; 1-5 p.m.
Forecast Value Added Analysis
Forecast Value Added (FVA) is the change in a forecasting performance metric (such as MAPE or bias) that can be attributed to a particular step or participant in the forecasting process. FVA analysis is used to identify those process activities that are failing to make the forecast any better (or might even be making it worse). This course provides step-by-step guidelines for conducting FVA analysis – to identify and eliminate the waste, inefficiency, and worst practices from your forecasting process. The result can be better forecasts, with fewer resources and less management time spent on forecasting.
Sunday, Oct. 7; 1-5 p.m.
SAS Enterprise Content Categorization: An Introduction
This course gives an introduction to methods of unstructured data analysis, document classification and document content identification. The course also uses examples as the basis for constructing parse expressions and resulting entities.
Sunday, Oct. 7; 1-5 p.m.
Introduction to Data Mining and SAS Enterprise Miner
This course serves as an introduction to data mining and SAS Enterprise Miner for Desktop software. It is designed for data analysts and qualitative experts as well as those with less of a technical background who want a general understanding of data mining.
Sunday, Oct. 7, 1-5 p.m.
Modeling Trend, Cycles, and Seasonality in Time Series Data Using PROC UCM
This half-day lecture teaches students how to model, interpret, and predict time series data using UCMs. The UCM procedure analyzes and forecasts equally spaced univariate time series data using the unobserved components models (UCM). This course is designed for business analysts who want to analyze time series data to uncover patterns such as trend, seasonal effects, and cycles using the latest techniques.
Sunday, Oct. 7, 1-5 p.m.
SAS Rapid Predictive Modeler
This seminar will provide a brief introduction to the use of SAS Enterprise Guide for graphical and data analysis. However, the focus will be on using SAS Enterprise Guide and SAS Enterprise Miner along with the Rapid Predictive Modeling component to build predictive models. Predictive modeling will be introduced using the SEMMA process developed with the introduction of SAS Enterprise Miner. Several examples will be used to illustrate the use of the Rapid Predictive Modeling component, and interpretations of the model results will be provided.
Sunday, Oct. 7, 1-5 p.m
Awesomely informative post on sascom magazine (whose editor I have I interviewed before here at http://www.decisionstats.com/interview-alison-bolen-sas-com/ – )
Great piece by Michael Ames ,SAS Data Integration Product Manager.
Also see SAS’s big data thingys here at
Solutions and Capabilities Using SAS® In-Memory Analytics
- High-Performance Analytics – Get near-real-time insights with appliance-ready analytics software designed to tackle big data and complex problems.
- High-Performance Risk – Faster, better risk management decisions based on the most up-to-date views of your overall risk exposure.
- High-Performance Liquidity Risk Management – Take quick, decisive actions to secure adequate funding, especially in times of volatility.
- High-Performance Stress Testing – Make faster, more precise decisions to protect the health of the firm.
- Visual Analytics – Explore big data using in-memory capabilities to better understand all of your data, discover new patterns and publish reports to the Web and iPad®.
(Ajay- I liked the Visual Analytics piece especially for Big Data )
Here is an interview with James G Kobielus, who is the Senior Program Director, Product Marketing, Big Data Analytics Solutions at IBM. Special thanks to Payal Patel Cudia of IBM’s communication team,for helping with the logistics for this.
Ajay -What are the specific parts of the IBM Platform that deal with the three layers of Big Data -variety, velocity and volume
James-Well first of all, let’s talk about the IBM Information Management portfolio. Our big data platform addresses the three layers of big data to varying degrees either together in a product , or two out of the three or even one of the three aspects. We don’t have separate products for the variety, velocity and volume separately.
Let us define these three layers-Volume refers to the hundreds of terabytes and petabytes of stored data inside organizations today. Velocity refers to the whole continuum from batch to real time continuous and streaming data.
Variety refers to multi-structure data from structured to unstructured files, managed and stored in a common platform analyzed through common tooling.
For Volume-IBM has a highly scalable Big Data platform. This includes Netezza and Infosphere groups of products, and Watson-like technologies that can support petabytes volume of data for analytics. But really the support of volume ranges across IBM’s Information Management portfolio both on the database side and the advanced analytics side.
For real time Velocity, we have real time data acquisition. We have a product called IBM Infosphere, part of our Big Data platform, that is specifically built for streaming real time data acquisition and delivery through complex event processing. We have a very rich range of offerings that help clients build a Hadoop environment that can scale.
Our Hadoop platform is the most real time capable of all in the industry. We are differentiated by our sheer breadth, sophistication and functional depth and tooling integrated in our Hadoop platform. We are differentiated by our streaming offering integrated into the Hadoop platform. We also offer a great range of modeling and analysis tools, pretty much more than any other offering in the Big Data space.
Attached- Jim’s slides from Hadoop World
Ajay- Any plans for Mahout for Hadoop
Jim- I cant speak about product plans. We have plans but I cant tell you anything more. We do have a feature in Big Insights called System ML, a library for machine learning.
Ajay- How integral are acquisitions for IBM in the Big Data space (Netezza,Cognos,SPSS etc). Is it true that everything that you have in Big Data is acquired or is the famous IBM R and D contributing here . (see a partial list of IBM acquisitions at at http://www.ibm.com/investor/strategy/acquisitions.wss )
Jim- We have developed a lot on our own. We have the deepest R and D of anybody in the industry in all things Big Data.
For example – Watson has Big Insights Hadoop at its core. Apache Hadoop is the heart and soul of Big Data (see http://www-01.ibm.com/software/data/infosphere/hadoop/ ). A great deal that makes Big Insights so differentiated is that not everything that has been built has been built by the Hadoop community.
We have built additions out of the necessity for security, modeling, monitoring, and governance capabilities into BigInsights to make it truly enterprise ready. That is one example of where we have leveraged open source and we have built our own tools and technologies and layered them on top of the open source code.
Yes of course we have done many strategic acquisitions over the last several years related to Big Data Management and we continue to do so. This quarter we have done 3 acquisitions with strong relevance to Big Data. One of them is Vivisimo (http://www-03.ibm.com/press/us/en/pressrelease/37491.wss ).
Vivisimo provides federated Big Data discovery, search and profiling capabilities to help you figure out what data is out there,what is relevance of that data to your data science project- to help you answer the question which data should you bring in your Hadoop Cluster.
We also did Varicent , which is more performance management and we did TeaLeaf , which is a customer experience solution provider where customer experience management and optimization is one of the hot killer apps for Hadoop in the cloud. We have done great many acquisitions that have a clear relevance to Big Data.
Netezza already had a massively parallel analytics database product with an embedded library of models called Netezza Analytics, and in-database capabilties to massively parallelize Map Reduce and other analytics management functions inside the database. In many ways, Netezza provided capabilities similar to that IBM had provided for many years under the Smart Analytics Platform (http://www-01.ibm.com/software/data/infosphere/what-is-advanced-analytics/ ) .
There is a differential between Netezza and ISAS.
ISAS was built predominantly in-house over several years . If you go back a decade ago IBM acquired Ascential Software , a product portfolio that was the heart and soul of IBM InfoSphere Information Manager that is core to our big Data platform. In addition to Netezza, IBM bought SPSS two years back. We already had data mining tools and predictive modeling in the InfoSphere portfolio, but we realized we needed to have the best of breed, SPSS provided that and so IBM acquired them.
Cognos- We had some BI reporting capabilities in the InfoSphere portfolio that we had built ourselves and also acquired for various degrees from prior acquisitions. But clearly Cognos was one of the best BI vendors , and we were lacking such a rich tool set in our product in visualization and cubing and so for that reason we acquired Cognos.
There is also Unica – which is a marketing campaign optimization which in many ways is a killer app for Hadoop. Projects like that are driving many enterprises.
Ajay- How would you rank order these acquisitions in terms of strategic importance rather than data of acquisition or price paid.
Jim-Think of Big Data as an ecosystem that has components that are fitted to particular functions for data analytics and data management. Is the database the core, or the modeling tool the core, or the governance tools the core, or is the hardware platform the core. Everything is critically important. We would love to hear from you what you think have been most important. Each acquisition has helped play a critical role to build the deepest and broadest solution offering in Big Data. We offer the hardware, software, professional services, the hosting service. I don’t think there is any validity to a rank order system.
Ajay-What are the initiatives regarding open source that Big Data group have done or are planning?
Jim- What we are doing now- We are very much involved with the Apache Hadoop community. We continue to evolve the open source code that everyone leverages.. We have built BigInsights on Apache Hadoop. We have the closest, most up to date in terms of version number to Apache Hadoop ( Hbase,HDFS, Pig etc) of all commercial distributions with our BigInsights 1.4 .
We have an R library integrated with BigInsights . We have a R library integrated with Netezza Analytics. There is support for R Models within the SPSS portfolio. We already have a fair amount of support for R across the portfolio.
Ajay- What are some of the concerns (privacy,security,regulation) that you think can dampen the promise of Big Data.
Jim- There are no showstoppers, there is really a strong momentum. Some of the concerns within the Hadoop space are immaturity of the technology, the immaturity of some of the commercial offerings out there that implement Hadoop, the lack of standardization for formal sense for Hadoop.
There is no Open Standards Body that declares, ratifies the latest version of Mahout, Map Reduce, HDFS etc. There is no industry consensus reference framework for layering these different sub projects. There are no open APIs. There are no certifications or interoperability standards or organizations to certify different vendors interoperability around a common API or framework.
The lack of standardization is troubling in this whole market. That creates risks for users because users are adopting multiple Hadoop products. There are lots of Hadoop deployments in the corporate world built around Apache Hadoop (purely open source). There may be no assurance that these multiple platforms will interoperate seamlessly. That’s a huge issue in terms of just magnifying the risk. And it increases the need for the end user to develop their own custom integrated code if they want to move data between platforms, or move map-reduce jobs between multiple distributions.
Also governance is a consideration. Right now Hadoop is used for high volume ETL on multi structured and unstructured data sources, or Hadoop is used for exploratory sand boxes for data scientists. These are important applications that are a majority of the Hadoop deployments . Some Hadoop deployments are stand alone unstructured data marts for specific applications like sentiment analysis like.
Hadoop is not yet ready for data warehousing. We don’t see a lot of Hadoop being used as an alternative to data warehouses for managing the single version of truth of system or record data. That day will come but there needs to be out there in the marketplace a broader range of data governance mechanisms , master data management, data profiling products that are mature that enterprises can use to make sure their data inside their Hadoop clusters is clean and is the single version of truth. That day has not arrived yet.
One of the great things about IBM’s acquisition of Vivisimo is that a piece of that overall governance picture is discovery and profiling for unstructured data , and that is done very well by Vivisimo for several years.
What we will see is vendors such as IBM will continue to evolve security features inside of our Hadoop platform. We will beef up our data governance capabilities for this new world of Hadoop as the core of Big Data, and we will continue to build up our ability to integrate multiple databases in our Hadoop platform so that customers can use data from a bit of Hadoop,some data from a bit of traditional relational data warehouse, maybe some noSQL technology for different roles within a very complex Big Data environment.
That latter hybrid deployment model is becoming standard across many enterprises for Big Data. A cause for concern is when your Big Data deployment has a bit of Hadoop, bit of noSQL, bit of EDW, bit of in-memory , there are no open standards or frameworks for putting it all together for a unified framework not just for interoperability but also for deployment.
There needs to be a virtualization or abstraction layer for unified access to all these different Big Data platforms by the users/developers writing the queries, by administrators so they can manage data and resources and jobs across all these disparate platforms in a seamless unified way with visual tooling. That grand scenario, the virtualization layer is not there yet in any standard way across the big data market. It will evolve, it may take 5-10 years to evolve but it will evolve.
So, that’s the concern that can dampen some of the enthusiasm for Big Data Analytics.
You can read more about Jim at http://www.linkedin.com/pub/james-kobielus/6/ab2/8b0 or
follow him on Twitter at http://twitter.com/jameskobielus
You can read more about IBM Big Data at http://www-01.ibm.com/software/data/bigdata/
Here is an interview with Jason Kuo who works with SAP Analytics as Group Solutions Marketing Manager. Jason answers questions on SAP Analytics and it’s increasing involvement with R statistical language.
Ajay- What made you choose R as the language to tie important parts of your technology platform like HANA and SAP Predictive Analysis. Did you consider other languages like Julia or Python.
Jason- It’s the most popular. Over 50% of the statisticians and data analysts use R. With 3,500+ algorithms its arguably the most comprehensive statistical analysis language. That said,we are not closing the door on others.
Ajay- When did you first start getting interested in R as an analytics platform?
Jason- SAP has been tracking R for 5+ years. With R’s explosive growth over the last year or two, it made sense for us to dramatically increase our investment in R.
Ajay- Can we expect SAP to give back to the R community like Google and Revolution Analytics does- by sponsoring Package development or sponsoring user meets and conferences?
Will we see SAP’s R HANA package in this year’s R conference User 2012 in Nashville
Jason- Yes. We plan to provide a specific driver for HANA tables for input of the data to native R. This planned for end of 2012. We’ll then review our event strategy. SAP has been a sponsor of Predictive Analytics World for several years and was indeed a founding sponsor. We may be attending the year’s R conference in Nashville.
Ajay- What has been some of the initial customer feedback to your analytics expansion and offerings.
Jason- We have completed two very successful Pilots of the R Integration for HANA with two of SAP’s largest customers.
Jason has over 15 years of BI and Data Warehousing industry experience. Having worked at Oracle, Business Objects, and now SAP, Jason has been involved in numerous technical marketing roles involving performance management dashboards, information management, text analysis, predictive analytics, and now big data. He has a bachelor’s of science in operations research from the University of Michigan.
Just got the email-more software is good news!
Revolution R Enterprise 6.0 for 32-bit and 64-bit Windows and 64-bit Red Hat Enterprise Linux (RHEL 5.x and RHEL 6.x) features an updated release of the RevoScaleR package that provides fast, scalable data management and data analysis: the same code scales from data frames to local, high-performance .xdf files to data distributed across a Windows HPC Server cluster or IBM Platform Computing LSF cluster. RevoScaleR also allows distribution of the execution of essentially any R function across cores and nodes, delivering the results back to the user.
Detailed information on what’s new in 6.0 and known issues:
and from the manual-lots of function goodies for Big Data
- IBM Platform LSF Cluster support [Linux only]. The new RevoScaleR function, RxLsfCluster, allows you to create a distributed compute context for the Platform LSF workload manager.
- Azure Burst support added for Microsoft HPC Server [Windows only]. The new RevoScaleR function, RxAzureBurst, allows you to create a distributed compute context to have computations performed in the cloud using Azure Burst
- The rxExec function allows distributed execution of essentially any R function across cores and nodes, delivering the results back to the user.
- functions RxLocalParallel and RxLocalSeq allow you to create compute context objects for local parallel and local sequential computation, respectively.
- RxForeachDoPar allows you to create a compute context using the currently registered foreach parallel backend (doParallel, doSNOW, doMC, etc.). To execute rxExec calls, simply register the parallel backend as usual, then set your compute context as follows: rxSetComputeContext(RxForeachDoPar())
- rxSetComputeContext and rxGetComputeContext simplify management of compute contexts.
- rxGlm, provides a fast, scalable, distributable implementation of generalized linear models. This expands the list of full-featured high performance analytics functions already available: summary statistics (rxSummary), cubes and cross tabs (rxCube,rxCrossTabs), linear models (rxLinMod), covariance and correlation matrices (rxCovCor),
binomial logistic regression (rxLogit), and k-means clustering (rxKmeans)example: a Tweedie family with 1 million observations and 78 estimated coefficients (categorical data)
took 17 seconds with rxGlm compared with 377 seconds for glm on a quadcore laptop
and easier working with R’s big brother SAS language
RevoScaleR high-performance analysis functions will now conveniently work directly with a variety of external data sources (delimited and fixed format text files, SAS files, SPSS files, and ODBC data connections). New functions are provided to create data source objects to represent these data sources (RxTextData, RxOdbcData, RxSasData, and RxSpssData), which in turn can be specified for the ‘data’ argument for these RevoScaleR analysis functions: rxHistogram, rxSummary, rxCube, rxCrossTabs, rxLinMod, rxCovCor, rxLogit, and rxGlm.
you can analyze a SAS file directly as follows:
# Create a SAS data source with information about variables and # rows to read in each chunk
sasDataFile <- file.path(rxGetOption(“sampleDataDir”),”claims.sas7bdat”)
sasDS <- RxSasData(sasDataFile, stringsAsFactors = TRUE,colClasses = c(RowNum = “integer”),rowsPerRead = 50)
# Compute and draw a histogram directly from the SAS file
rxHistogram( ~cost|type, data = sasDS)
# Compute summary statistics
rxSummary(~., data = sasDS)
# Estimate a linear model
linModObj <- rxLinMod(cost~age + car_age + type, data = sasDS)
# Import a subset into a data frame for further inspection
subData <- rxImport(inData = sasDS, rowSelection = cost > 400,
varsToKeep = c(“cost”, “age”, “type”))
The installation instructions and instructions for getting started with Revolution R Enterprise & RevoDeployR for Windows: http://www.revolutionanalytics.com/downloads/instructions/windows.php