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US Congress cedes cyber-war to Executive Branch

From–

http://www.nytimes.com/2012/06/01/world/middleeast/obama-ordered-wave-of-cyberattacks-against-iran.html?_r=2

Obama Order Sped Up Wave of Cyberattacks Against Iran

By
Published: June 1, 2012

WASHINGTON — From his first months in office, President Obama secretly ordered increasingly sophisticated attacks on the computer systems that run Iran’s main nuclear enrichment facilities, significantly expanding America’s first sustained use of cyberweapons,

From–

http://www.politico.com/news/stories/0612/76973.html

Can the White House declare a cyberwar?

By JENNIFER MARTINEZ and JONATHAN ALLEN | 6/1/12
“When we see the results it’s pretty clear they’re doing it without anybody except a very few people knowing about it, much less having any impact on whether it’s happening or not,” said Rep. Jim McDermott (D-Wash.).

McDermott is troubled because “we have given more and more power to the president, through the CIA, to carry out operations, and, frankly, if you go back in history, the reason we have problems with Iran is because the CIA brought about a coup.”

 

From–

http://www.house.gov/house/Constitution/Constitution.html

Article. I.

Section 1.

All legislative Powers herein granted shall be vested in a Congress of the United States, which shall consist of a Senate and House of Representatives.

Section. 8.

The Congress shall have Power

Clause 11: To declare War, grant Letters of Marque and Reprisal, and make Rules concerning Captures on Land and Water;

 

Related-

http://www.huffingtonpost.com/2009/10/09/obama-wins-nobel-peace-pr_n_314907.html

Obama Wins Nobel Peace Prize

KARL RITTER and MATT MOORE   10/ 9/09 11:02 PM ET

http://www.law.uchicago.edu/media

Statement Regarding Barack Obama 

The Law School has received many media requests about Barack Obama, especially about his status as “Senior Lecturer.”

From 1992 until his election to the U.S. Senate in 2004, Barack Obama served as a professor in the Law School. He was a Lecturer from 1992 to 1996. He was a Senior Lecturer from 1996 to 2004, during which time he taught three courses per year.

 

New RCommander with ggplot #rstats

 

My favorite GUI (or one of them) R Commander has a relatively new plugin called KMGGplot2. Until now Deducer was the only GUI with ggplot features , but the much lighter and more popular R Commander has been a long champion in people wanting to pick up R quickly.

 

http://cran.r-project.org/web/packages/RcmdrPlugin.KMggplot2/

RcmdrPlugin.KMggplot2: Rcmdr Plug-In for Kaplan-Meier Plot and Other Plots by Using the ggplot2 Package

 

As you can see by the screenshot- it makes ggplot even easier for people (like R  newbies and experienced folks alike)

 

This package is an R Commander plug-in for Kaplan-Meier plot and other plots by using the ggplot2 package.

Version: 0.1-0
Depends: R (≥ 2.15.0), stats, methods, grid, Rcmdr (≥ 1.8-4), ggplot2 (≥ 0.9.1)
Imports: tcltk2 (≥ 1.2-3), RColorBrewer (≥ 1.0-5), scales (≥ 0.2.1), survival (≥ 2.36-14)
Published: 2012-05-18
Author: Triad sou. and Kengo NAGASHIMA
Maintainer: Triad sou. <triadsou at gmail.com>
License: GPL-2
CRAN checks: RcmdrPlugin.KMggplot2 results

 

----------------------------------------------------------------
NEWS file for the RcmdrPlugin.KMggplot2 package
----------------------------------------------------------------

----------------------------------------------------------------

Changes in version 0.1-0 (2012-05-18)

 o Restructuring implementation approach for efficient
   maintenance.
 o Added options() for storing package specific options (e.g.,
   font size, font family, ...).
 o Added a theme: theme_simple().
 o Added a theme element: theme_rect2().
 o Added a list box for facet_xx() functions in some menus
   (Thanks to Professor Murtaza Haider).
 o Kaplan-Meier plot: added confidence intervals.
 o Box plot: added violin plots.
 o Bar chart for discrete variables: deleted dynamite plots.
 o Bar chart for discrete variables: added stacked bar charts.
 o Scatter plot matrix: added univariate plots at diagonal
   positions (ggplot2::plotmatrix).
 o Deleted the dummy data for histograms, which is large in
   size.

----------------------------------------------------------------

Changes in version 0.0-4 (2011-07-28)

 o Fixed "scale_y_continuous(formatter = "percent")" to
   "scale_y_continuous(labels = percent)" for ggplot2
   (>= 0.9.0).
 o Fixed "legend = FALSE" to "show_guide = FALSE" for
   ggplot2 (>= 0.9.0).
 o Fixed the DESCRIPTION file for ggplot2 (>= 0.9.0) dependency.

----------------------------------------------------------------

Changes in version 0.0-3 (2011-07-28; FIRST RELEASE VERSION)

 o Kaplan-Meier plot: Show no. at risk table on outside.
 o Histogram: Color coding.
 o Histogram: Density estimation.
 o Q-Q plot: Create plots based on a maximum likelihood estimate
   for the parameters of the selected theoretical distribution.
 o Q-Q plot: Create plots based on a user-specified theoretical
   distribution.
 o Box plot / Errorbar plot: Box plot.
 o Box plot / Errorbar plot: Mean plus/minus S.D.
 o Box plot / Errorbar plot: Mean plus/minus S.D. (Bar plot).
 o Box plot / Errorbar plot: 95 percent Confidence interval
   (t distribution).
 o Box plot / Errorbar plot: 95 percent Confidence interval
   (bootstrap).
 o Scatter plot: Fitting a linear regression.
 o Scatter plot: Smoothing with LOESS for small datasets or GAM
   with a cubic regression basis for large data.
 o Scatter plot matrix: Fitting a linear regression.
 o Scatter plot matrix: Smoothing with LOESS for small datasets
   or GAM with a cubic regression basis for large data.
 o Line chart: Normal line chart.
 o Line chart: Line char with a step function.
 o Line chart: Area plot.
 o Pie chart: Pie chart.
 o Bar chart for discrete variables: Bar chart for discrete
   variables.
 o Contour plot: Color coding.
 o Contour plot: Heat map.
 o Distribution plot: Normal distribution.
 o Distribution plot: t distribution.
 o Distribution plot: Chi-square distribution.
 o Distribution plot: F distribution.
 o Distribution plot: Exponential distribution.
 o Distribution plot: Uniform distribution.
 o Distribution plot: Beta distribution.
 o Distribution plot: Cauchy distribution.
 o Distribution plot: Logistic distribution.
 o Distribution plot: Log-normal distribution.
 o Distribution plot: Gamma distribution.
 o Distribution plot: Weibull distribution.
 o Distribution plot: Binomial distribution.
 o Distribution plot: Poisson distribution.
 o Distribution plot: Geometric distribution.
 o Distribution plot: Hypergeometric distribution.
 o Distribution plot: Negative binomial distribution.

Interview Prof Benjamin Alamar , Sports Analytics

Here is an interview with Prof Benjamin Alamar, founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA.

Ajay – The movie Moneyball recently sparked out mainstream interest in analytics in sports.Describe the role of analytics in sports management

Benjamin- Analytics is impacting sports organizations on both the sport and business side.
On the Sport side, teams are using analytics, including advanced data management, predictive anlaytics, and information systems to gain a competitive edge. The use of analytics results in more accurate player valuations and projections, as well as determining effective strategies against specific opponents.
On the business side, teams are using the tools of analytics to increase revenue in a variety of ways including dynamic ticket pricing and optimizing of the placement of concession stands.
Ajay-  What are the ways analytics is used in specific sports that you have been part of?

Benjamin- A very typical first step for a team is to utilize the tools of predictive analytics to help inform their draft decisions.

Ajay- What are some of the tools, techniques and software that analytics in sports uses?
Benjamin- The tools of sports analytics do not differ much from the tools of business analytics. Regression analysis is fairly common as are other forms of data mining. In terms of software, R is a popular tool as is Excel and many of the other standard analysis tools.
Ajay- Describe your career journey and how you became involved in sports management. What are some of the tips you want to tell young students who wish to enter this field?

Benjamin- I got involved in sports through a company called Protrade Sports. Protrade initially was a fantasy sports company that was looking to develop a fantasy game based on advanced sports statistics and utilize a stock market concept instead of traditional drafting. I was hired due to my background in economics to develop the market aspect of the game.

There I met Roland Beech (who now works for the Mavericks) and Aaron Schatz (owner of footballoutsiders.com) and learned about the developing field of sports statistics. I then changed my research focus from economics to sports statistics and founded the Journal of Quantitative Analysis in Sports. Through the journal and my published research, I was able to establish a reputation of doing quality, useable work.

For students, I recommend developing very strong data management skills (sql and the like) and thinking carefully about what sort of questions a general manager or coach would care about. Being able to demonstrate analytic skills around actionable research will generally attract the attention of pro teams.

About-

Benjamin Alamar, Professor of Sport Management, Menlo College

Benjamin Alamar

Professor Benjamin Alamar is the founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA. He has published academic research in football, basketball and baseball, has presented at numerous conferences on sports analytics. He is also a co-creator of ESPN’s Total Quarterback Rating and a regular contributor to the Wall Street Journal. He has consulted for teams in the NBA and NFL, provided statistical analysis for author Michael Lewis for his recent book The Blind Side, and worked with numerous startup companies in the field of sports analytics. Professor Alamar is also an award winning economist who has worked academically and professionally in intellectual property valuation, public finance and public health. He received his PhD in economics from the University of California at Santa Barbara in 2001.

Prof Alamar is a speaker at Predictive Analytics World, San Fransisco and is doing a workshop there

http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#day2-17

2:55-3:15pm

All level tracks Track 1: Sports Analytics
Case Study: NFL, MLB, & NBA
Competing & Winning with Sports Analytics

The field of sports analytics ties together the tools of data management, predictive modeling and information systems to provide sports organization a competitive advantage. The field is rapidly developing based on new and expanded data sources, greater recognition of the value, and past success of a variety of sports organizations. Teams in the NFL, MLB, NBA, as well as other organizations have found a competitive edge with the application of sports analytics. The future of sports analytics can be seen through drawing on these past successes and the developments of new tools.

You can know more about Prof Alamar at his blog http://analyticfootball.blogspot.in/ or journal at http://www.degruyter.com/view/j/jqas. His detailed background can be seen at http://menlo.academia.edu/BenjaminAlamar/CurriculumVitae

Predictive analytics in the cloud : Angoss

I interviewed Angoss in depth here at http://www.decisionstats.com/interview-eberhard-miethke-and-dr-mamdouh-refaat-angoss-software/

Well they just announced a predictive analytics in the cloud.

 

http://www.angoss.com/predictive-analytics-solutions/cloud-solutions/

Solutions

Overview

KnowledgeCLOUD™ solutions deliver predictive analytics in the Cloud to help businesses gain competitive advantage in the areas of sales, marketing and risk management by unlocking the predictive power of their customer data.

KnowledgeCLOUD clients experience rapid time to value and reduced IT investment, and enjoy the benefits of Angoss’ industry leading predictive analytics – without the need for highly specialized human capital and technology.

KnowledgeCLOUD solutions serve clients in the asset management, insurance, banking, high tech, healthcare and retail industries. Industry solutions consist of a choice of analytical modules:

KnowledgeCLOUD for Sales/Marketing

KnowledgeCLOUD solutions are delivered via KnowledgeHUB™, a secure, scalable cloud-based analytical platform together with supporting deployment processes and professional services that deliver predictive analytics to clients in a hosted environment. Angoss industry leading predictive analytics technology is employed for the development of models and deployment of solutions.

Angoss’ deep analytics and domain expertise guarantees effectiveness – all solutions are back-tested for accuracy against historical data prior to deployment. Best practices are shared throughout the service to optimize your processes and success. Finely tuned client engagement and professional services ensure effective change management and program adoption throughout your organization.

For businesses looking to gain a competitive edge and put their data to work, Angoss is the ideal partner.

—-

Hmm. Analytics in the cloud . Reduce hardware costs. Reduce software costs . Increase profitability margins.

Hmmmmm

My favorite professor in North Carolina who calls cloud as a time sharing, are you listening Professor?

SAS Institute Financials 2011

SAS Institute has release it’s financials for 2011 at http://www.sas.com/news/preleases/2011financials.html,

Revenue surged across all solution and industry categories. Software to detect fraud saw a triple-digit jump. Revenue from on-demand solutions grew almost 50 percent. Growth from analytics and information management solutions were double digit, as were gains from customer intelligence, retail, risk and supply chain solutions

AJAY- and as a private company it is quite nice that they are willing to share so much information every year.

The graphics are nice ( and the colors much better than in 2010) , but pie-charts- seriously dude there is no way to compare how much SAS revenue is shifting across geographies or even across industries. So my two cents is – lose the pie charts, and stick to line graphs please for the share of revenue by country /industry.

In 2011, SAS grew staff 9.2 percent and reinvested 24 percent of revenue into research and development

AJAY- So that means 654 million dollars spent in Research and Development.  I wonder if SAS has considered investing in much smaller startups (than it’s traditional strategy of doing all research in-house and completely acquiring a smaller company)

Even a small investment of say 5-10 million USD in open source , or even Phd level research projects could greatly increase the ROI on that.

That means

Analyzing a private company’s financials are much more fun than a public company, and I remember the words of my finance professor ( “dig , dig”) to compare 2011 results with 2010 results.

http://www.sas.com/news/preleases/2010financials.html

The percentage invested in R and D is exactly the same (24%) and the percentages of revenue earned from each geography is exactly the same . So even though revenue growth increased from 5.2 % to 9% in 2011, both the geographic spread of revenues and share  R&D costs remained EXACTLY the same.

The Americas accounted for 46 percent of total revenue; Europe, Middle East and Africa (EMEA) 42 percent; and Asia Pacific 12 percent.

Overall, I think SAS remains a 35% market share (despite all that noise from IBM, SAS clones, open source) because they are good at providing solutions customized for industries (instead of just software products), the market for analytics is not saturated (it seems to be growing faster than 12% or is it) , and its ability to attract and retain the best analytical talent (which in a non -American tradition for a software company means no stock options, job security, and great benefits- SAS remains almost Japanese in HR practices).

In 2010, SAS grew staff by 2.4 percent, in 2011 SAS grew staff by 9 percent.

But I liked the directional statement made here-and I think that design interfaces, algorithmic and computational efficiencies should increase analytical time, time to think on business and reduce data management time further!

“What would you do with the extra time if your code ran in two minutes instead of five hours?” Goodnight challenged.

Interview Dan Steinberg Founder Salford Systems

Here is an interview with Dan Steinberg, Founder and President of Salford Systems (http://www.salford-systems.com/ )

Ajay- Describe your journey from academia to technology entrepreneurship. What are the key milestones or turning points that you remember.

 Dan- When I was in graduate school studying econometrics at Harvard,  a number of distinguished professors at Harvard (and MIT) were actively involved in substantial real world activities.  Professors that I interacted with, or studied with, or whose software I used became involved in the creation of such companies as Sun Microsystems, Data Resources, Inc. or were heavily involved in business consulting through their own companies or other influential consultants.  Some not involved in private sector consulting took on substantial roles in government such as membership on the President’s Council of Economic Advisors. The atmosphere was one that encouraged free movement between academia and the private sector so the idea of forming a consulting and software company was quite natural and did not seem in any way inconsistent with being devoted to the advancement of science.

 Ajay- What are the latest products by Salford Systems? Any future product plans or modification to work on Big Data analytics, mobile computing and cloud computing.

 Dan- Our central set of data mining technologies are CART, MARS, TreeNet, RandomForests, and PRIM, and we have always maintained feature rich logistic regression and linear regression modules. In our latest release scheduled for January 2012 we will be including a new data mining approach to linear and logistic regression allowing for the rapid processing of massive numbers of predictors (e.g., one million columns), with powerful predictor selection and coefficient shrinkage. The new methods allow not only classic techniques such as ridge and lasso regression, but also sub-lasso model sizes. Clear tradeoff diagrams between model complexity (number of predictors) and predictive accuracy allow the modeler to select an ideal balance suitable for their requirements.

The new version of our data mining suite, Salford Predictive Modeler (SPM), also includes two important extensions to the boosted tree technology at the heart of TreeNet.  The first, Importance Sampled learning Ensembles (ISLE), is used for the compression of TreeNet tree ensembles. Starting with, say, a 1,000 tree ensemble, the ISLE compression might well reduce this down to 200 reweighted trees. Such compression will be valuable when models need to be executed in real time. The compression rate is always under the modeler’s control, meaning that if a deployed model may only contain, say, 30 trees, then the compression will deliver an optimal 30-tree weighted ensemble. Needless to say, compression of tree ensembles should be expected to be lossy and how much accuracy is lost when extreme compression is desired will vary from case to case. Prior to ISLE, practitioners have simply truncated the ensemble to the maximum allowable size.  The new methodology will substantially outperform truncation.

The second major advance is RULEFIT, a rule extraction engine that starts with a TreeNet model and decomposes it into the most interesting and predictive rules. RULEFIT is also a tree ensemble post-processor and offers the possibility of improving on the original TreeNet predictive performance. One can think of the rule extraction as an alternative way to explain and interpret an otherwise complex multi-tree model. The rules extracted are similar conceptually to the terminal nodes of a CART tree but the various rules will not refer to mutually exclusive regions of the data.

 Ajay- You have led teams that have won multiple data mining competitions. What are some of your favorite techniques or approaches to a data mining problem.

 Dan- We only enter competitions involving problems for which our technology is suitable, generally, classification and regression. In these areas, we are  partial to TreeNet because it is such a capable and robust learning machine. However, we always find great value in analyzing many aspects of a data set with CART, especially when we require a compact and easy to understand story about the data. CART is exceptionally well suited to the discovery of errors in data, often revealing errors created by the competition organizers themselves. More than once, our reports of data problems have been responsible for the competition organizer’s decision to issue a corrected version of the data and we have been the only group to discover the problem.

In general, tackling a data mining competition is no different than tackling any analytical challenge. You must start with a solid conceptual grasp of the problem and the actual objectives, and the nature and limitations of the data. Following that comes feature extraction, the selection of a modeling strategy (or strategies), and then extensive experimentation to learn what works best.

 Ajay- I know you have created your own software. But are there other software that you use or liked to use?

 Dan- For analytics we frequently test open source software to make sure that our tools will in fact deliver the superior performance we advertise. In general, if a problem clearly requires technology other than that offered by Salford, we advise clients to seek other consultants expert in that other technology.

 Ajay- Your software is installed at 3500 sites including 400 universities as per http://www.salford-systems.com/company/aboutus/index.html What is the key to managing and keeping so many customers happy?

 Dan- First, we have taken great pains to make our software reliable and we make every effort  to avoid problems related to bugs.  Our testing procedures are extensive and we have experts dedicated to stress-testing software . Second, our interface is designed to be natural, intuitive, and easy to use, so the challenges to the new user are minimized. Also, clear documentation, help files, and training videos round out how we allow the user to look after themselves. Should a client need to contact us we try to achieve 24-hour turn around on tech support issues and monitor all tech support activity to ensure timeliness, accuracy, and helpfulness of our responses. WebEx/GotoMeeting and other internet based contact permit real time interaction.

 Ajay- What do you do to relax and unwind?

 Dan- I am in the gym almost every day combining weight and cardio training. No matter how tired I am before the workout I always come out energized so locating a good gym during my extensive travels is a must. I am also actively learning Portuguese so I look to watch a Brazilian TV show or Portuguese dubbed movie when I have time; I almost never watch any form of video unless it is available in Portuguese.

 Biography-

http://www.salford-systems.com/blog/dan-steinberg.html

Dan Steinberg, President and Founder of Salford Systems, is a well-respected member of the statistics and econometrics communities. In 1992, he developed the first PC-based implementation of the original CART procedure, working in concert with Leo Breiman, Richard Olshen, Charles Stone and Jerome Friedman. In addition, he has provided consulting services on a number of biomedical and market research projects, which have sparked further innovations in the CART program and methodology.

Dr. Steinberg received his Ph.D. in Economics from Harvard University, and has given full day presentations on data mining for the American Marketing Association, the Direct Marketing Association and the American Statistical Association. After earning a PhD in Econometrics at Harvard Steinberg began his professional career as a Member of the Technical Staff at Bell Labs, Murray Hill, and then as Assistant Professor of Economics at the University of California, San Diego. A book he co-authored on Classification and Regression Trees was awarded the 1999 Nikkei Quality Control Literature Prize in Japan for excellence in statistical literature promoting the improvement of industrial quality control and management.

His consulting experience at Salford Systems has included complex modeling projects for major banks worldwide, including Citibank, Chase, American Express, Credit Suisse, and has included projects in Europe, Australia, New Zealand, Malaysia, Korea, Japan and Brazil. Steinberg led the teams that won first place awards in the KDDCup 2000, and the 2002 Duke/TeraData Churn modeling competition, and the teams that won awards in the PAKDD competitions of 2006 and 2007. He has published papers in economics, econometrics, computer science journals, and contributes actively to the ongoing research and development at Salford.

Updated Interview Elissa Fink -VP Tableau Software

Here is an interview with Elissa Fink, VP Marketing of that new wonderful software called Tableau that makes data visualization so nice and easy to learn and work with.

Elissa Fink, VP, Marketing

Ajay-  Describe your career journey from high school to over 20 plus years in marketing. What are the various trends that you have seen come and go in marketing.

Elissa- I studied literature and linguistics in college and didn’t discover analytics until my first job selling advertising for the Wall Street Journal. Oddly enough, the study of linguistics is not that far from decision analytics: they both are about taking a structured view of information and trying to see and understand common patterns. At the Journal, I was completely captivated analyzing and comparing readership data. At the same time, the idea of using computers in marketing was becoming more common. I knew that the intersection of technology and marketing was going to radically change things – how we understand consumers, how we market and sell products, and how we engage with customers. So from that point on, I’ve always been focused on technology and marketing, whether it’s working as a marketer at technology companies or applying technology to marketing problems for other types of companies.  There have been so many interesting trends. Taking a long view, a key trend I’ve noticed is how marketers work to understand, influence and motivate consumer behavior. We’ve moved marketing from where it was primarily unpredictable, qualitative and aimed at talking to mass audiences, where the advertising agency was king. Now it’s a discipline that is more data-driven, quantitative and aimed at conversations with individuals, where the best analytics wins. As with any trend, the pendulum swings far too much to either side causing backlashes but overall, I think we are in a great place now. We are using data-driven analytics to understand consumer behavior. But pure analytics is not the be-all, end-all; good marketing has to rely on understanding human emotions, intuition and gut feel – consumers are far from rational so taking only a rational or analytical view of them will never explain everything we need to know.

Ajay- Do you think technology companies are still predominantly dominated by men . How have you seen diversity evolve over the years. What initiatives has Tableau taken for both hiring and retaining great talent.

Elissa- The thing I love about the technology industry is that its key success metrics – inventing new products that rapidly gain mass adoption in pursuit of making profit – are fairly objective. There’s little subjective nature to the counting of dollars collected selling a product and dollars spent building a product. So if a female can deliver a better product and bigger profits faster and better, then that female is going to get the resources, jobs, power and authority to do exactly that. That’s not to say that the technology industry is gender-blind, race-blind, etc. It isn’t – technology is far from perfect. For example, the industry doesn’t have enough diversity in positions of power. But I think overall, in comparison to a lot of other industries, it’s pretty darn good at giving people with great ideas the opportunities to realize their visions regardless of their backgrounds or characteristics.

At Tableau, we are very serious about bringing in and developing talented people – they are the key to our growth and success. Hiring is our #1 initiative so we’ve spent a lot of time and energy both on finding great candidates and on making Tableau a place that they want to work. This includes things like special recruiting events, employee referral programs, a flexible work environment, fun social events, and the rewards of working for a start-up. Probably our biggest advantage is the company itself – working with people you respect on amazing, cutting-edge products that delight customers and are changing the world is all too rare in the industry but a reality at Tableau. One of our senior software developers put it best when he wrote “The emphasis is on working smarter rather than longer: family and friends are why we work, not the other way around. Tableau is all about happy, energized employees executing at the highest level and delivering a highly usable, high quality, useful product to our customers.” People who want to be at a place like that should check out our openings at http://www.tableausoftware.com/jobs.

Ajay- What are most notable features in tableau’s latest edition. What are the principal software that competes with Tableau Software products and how would you say Tableau compares with them.

Elissa- Tableau 6.1 will be out in July and we are really excited about it for 3 reasons.

First, we’re introducing our mobile business intelligence capabilities. Our customers can have Tableau anywhere they need it. When someone creates an interactive dashboard or analytical application with Tableau and it’s viewed on a mobile device, an iPad in particular, the viewer will have a native, touch-optimized experience. No trying to get your fingertips to act like a mouse. And the author didn’t have to create anything special for the iPad; she just creates her analytics the usual way in Tableau. Tableau knows the dashboard is being viewed on an iPad and presents an optimized experience.

Second, we’ve take our in-memory analytics engine up yet another level. Speed and performance are faster and now people can update data incrementally rapidly. Introduced in 6.0, our data engine makes any data fast in just a few clicks. We don’t run out of memory like other applications. So if I build an incredible dashboard on my 8-gig RAM PC and you try to use it on your 2-gig RAM laptop, no problem.

And, third, we’re introducing more features for the international markets – including French and German versions of Tableau Desktop along with more international mapping options.  It’s because we are constantly innovating particularly around user experience that we can compete so well in the market despite our relatively small size. Gartner’s seminal research study about the Business Intelligence market reported a massive market shift earlier this year: for the first time, the ease-of-use of a business intelligence platform was more important than depth of functionality. In other words, functionality that lots of people can actually use is more important than having sophisticated functionality that only specialists can use. Since we focus so heavily on making easy-to-use products that help people rapidly see and understand their data, this is good news for our customers and for us.

Ajay-  Cloud computing is the next big thing with everyone having a cloud version of their software. So how would you run Cloud versions of Tableau Server (say deploying it on an Amazon Ec2  or a private cloud)

Elissa- In addition to the usual benefits espoused about Cloud computing, the thing I love best is that it makes data and information more easily accessible to more people. Easy accessibility and scalability are completely aligned with Tableau’s mission. Our free product Tableau Public and our product for commercial websites Tableau Digital are two Cloud-based products that deliver data and interactive analytics anywhere. People often talk about large business intelligence deployments as having thousands of users. With Tableau Public and Tableau Digital, we literally have millions of users. We’re serving up tens of thousands of visualizations simultaneously – talk about accessibility and scalability!  We have lots of customers connecting to databases in the Cloud and running Tableau Server in the Cloud. It’s actually not complex to set up. In fact, we focus a lot of resources on making installation and deployment easy and fast, whether it’s in the cloud, on premise or what have you. We don’t want people to have spend weeks or months on massive roll-out projects. We want it to be minutes, hours, maybe a day or 2. With the Cloud, we see that people can get started and get results faster and easier than ever before. And that’s what we’re about.

Ajay- Describe some of the latest awards that Tableau has been wining. Also how is Tableau helping universities help address the shortage of Business Intelligence and Big Data professionals.

Elissa-Tableau has been very fortunate. Lately, we’ve been acknowledged by both Gartner and IDC as the fastest growing business intelligence software vendor in the world. In addition, our customers and Tableau have won multiple distinctions including InfoWorld Technology Leadership awards, Inc 500, Deloitte Fast 500, SQL Server Magazine Editors’ Choice and Community Choice awards, Data Hero awards, CODiEs, American Business Awards among others. One area we’re very passionate about is academia, participating with professors, students and universities to help build a new generation of professionals who understand how to use data. Data analysis should not be exclusively for specialists. Everyone should be able to see and understand data, whatever their background. We come from academic roots, having been spun out of a Stanford research project. Consequently, we strongly believe in supporting universities worldwide and offer 2 academic programs. The first is Tableau For Teaching, where any professor can request free term-length licenses of Tableau for academic instruction during his or her courses. And, we offer a low-cost Student Edition of Tableau so that students can choose to use Tableau in any of their courses at any time.

Elissa Fink, VP Marketing,Tableau Software

 

Elissa Fink is Tableau Software’s Vice President of Marketing. With 20+ years helping companies improve their marketing operations through applied data analysis, Elissa has held executive positions in marketing, business strategy, product management, and product development. Prior to Tableau, Elissa was EVP Marketing at IXI Corporation, now owned by Equifax. She has also served in executive positions at Tele Atlas (acquired by TomTom), TopTier Software (acquired by SAP), and Nielsen/Claritas. Elissa also sold national advertising for the Wall Street Journal. She’s a frequent speaker and has spoken at conferences including the DMA, the NCDM, Location Intelligence, the AIR National Forum and others. Elissa is a graduate of Santa Clara University and holds an MBA in Marketing and Decision Systems from the University of Southern California.

Elissa first discovered Tableau late one afternoon at her previous company. Three hours later, she was still “at play” with her data. “After just a few minutes using the product, I was getting answers to questions that were taking my company’s programmers weeks to create. It was instantly obvious that Tableau was on a special mission with something unique to offer the world. I just had to be a part of it.”

To know more – read at http://www.tableausoftware.com/

and existing data viz at http://www.tableausoftware.com/learn/gallery

Storm seasons: measuring and tracking key indicators
What’s happening with local real estate prices?
How are sales opportunities shaping up?
Identify your best performing products
Applying user-defined parameters to provide context
Not all tech companies are rocket ships
What’s really driving the economy?
Considering factors and industry influencers
The complete orbit along the inside, or around a fixed circle
How early do you have to be at the airport?
What happens if sales grow but so does customer churn?
What are the trends for new retail locations?
How have student choices changed?
Do patients who disclose their HIV status recover better?
Closer look at where gas prices swing in areas of the U.S.
U.S. Census data shows more women of greater age
Where do students come from and how does it affect their grades?
Tracking customer service effectiveness
Comparing national and local test scores
What factors correlate with high overall satisfaction ratings?
Fund inflows largely outweighed outflows well after the bubble
Which programs are competing for federal stimulus dollars?
Oil prices and volatility
A classic candlestick chart
How do oil, gold and CPI relate to the GDP growth rate?

 

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