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SAS versus R for business analysts

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Over on R4Stats, I replied to Bob Muenchen’s article, Forecast Update: Will 2014 be the Beginning of the End for SAS and SPSS?

Personally, I think SAS is a wonderful application, with my SAS experience starting in SAS programming back in 1989 (mainframes, along with Fortran), SAS Enterprise Guide (I wrote SAS for Dummies, the first two editions with Chris Hemedinger) and SAS Enterprise Miner.  Additionally, I have used JMP, SAS Data Integration Studio, SAS Forecast Studio and several other SAS tools.

On the other hand, I have used R since 2004 on several projects and S (precursor to R) since the 90′s in biopharm. I find R truer to being a modern programming language while SAS is truer to being an analyst programming language. Perhaps I am biased? But, the way I think of attacking problems with data and my typical need to massage the data in a wide range of ways, SAS is simply superior in my opinion. The flow of the language, the ease of readability and the powerful DATA step are still my favorite programming world. However, if I am seeking most any statistical test under the sun, R is clearly superior.

Unfortunately, R doesn’t have a clear, de-facto GUI (graphical user interface) that is well-designed

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Joyful circle charts or informative bar charts? Best practices in visual analytics

Small_packed_bubble_chartStephen Few, noted visual analytics expert and the original inspiration for our work in the field, recently wrote about criticisms of best data visualizations practices by people who should know better. In particular, Amanda Cox of the New York Times said, “There’s a strand of the data viz world that argues that everything could be a bar chart. That’s possibly true but also possibly a world without joy.” And Nathan Yau of Flowing Data wrote, “in visualization you eventually learn that there’s more to the process than efficient graphical perception and avoidance of all things round. Design matters, no doubt, but your understanding of the data matters much more.” These are both people who have a body of work that I admire but I am also surprised at these comments.

This discussion reminds me of a similar problem in marketing and web analytics. Generating traffic that leads to sales is good. Eventually, someone finds a way to generate traffic that leads to not many new sales, but management is misled to think this must be good since traffic leads to sales. This is similar to “look, this chart is beautiful“, but hard to interpret or understand. So, while we delivered fun graphs, minimal information is shared. This may be good for traffic, but not so much for higher sales.

I suspect that part of this recent criticism can be traced back to Stephen’s recent criticism of Tableau, “Tableau Veers from the Path“. In it, he mentions a new graph type in Tableau, packed bubble charts and contrasts them with bar charts. This is an example of the “avoidance of all things circular”. Is Stephen truly anti-joy? Will an example show him to be wrong? Let’s give it a try and you can judge for yourself.

Here’s a packed bubble chart example

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Freakalytics Timeline since 2007

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Since 2007, we have traveled 365,000 miles
to help tens of thousands of people via

8 books,
24 conference talks,
47 public trainings,
26 on-site trainings,
5 conference seminars,
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and 36 consulting projects.

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Alteryx Inspire 2013 Tableau Talk

0 Picture This

Download the presentation Picture This! Your Data in Tableau.

Watch my big data talk here and download the example workbooks here (requires Tableau 8 to open).

Attending this conference was a great experience. Based on multiple customer discussions at the conference, Alteryx is a great product for personal data integration, data enrichment and predictive analytics. Some amazing things we saw in Alteryx includes upcoming direct integration with the Tableau Data Engine, interactive location mapping to generate demographic profiles in Tableau, support of Revolution R 3.0 and integration with social data API’s including Yelp, Google + and Facebook.

Here is a screenshot of one of the marketing performance dashboards built in Tableau. It was based on data built with Alteryx. Alteryx was used for data integration, data enrichment and predictive modelling of customer likelihood to respond to future coupon offers. Enrichment included fuzzy matching of customer demographics using Alteryx-bundled 3rd party data sources from Experian.

Tableau 8 Dashboard where are we performing well

If you are looking for a great compliment to Tableau, I recommend that you consider Alteryx.

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Free Webcast: “Big Data” in US History, Exploring the 1790 US Census

NOTE: This fun review of “big data” was inspired by a recent presentation I gave on behalf of Tableau Software at the Big Data Conference in Chicago. You can find the 1st part of this 3 part webcast here, “Performance to Cost Index & my personal history with “Big Data”. Part 3 is here, “Big Data” on your laptop, fast, informative and at your command.

In this presentation, I share a review of the original big data in US history, the 1790 US Census. Some surprises are found along the way, including data quality issues in the Census reports and a surprising

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Free Webcast: Performance to Cost Index & my personal history with “Big Data”

NOTE: This fun review of “big data” was inspired by a recent presentation I gave on behalf of Tableau Software at the Big Data Conference in Chicago. You can find the 2nd part of this 3 part webcast here, ““Big Data” in US History, Exploring the 1790 US Census”. Part 3 is here, “Big Data” on your laptop, fast, informative and at your command.

Many people ask me, what is “big data”?  For most of them, the right answer is that big data is any data that is difficult to use or understand (yes, I know the official, “correct” answers, which often vary and typically include topics like Hadoop and Cloudera.)

In this presentation, I share my experience with the Commodore 64, the PS/2, DEC Stations, VAX servers, Solaris Servers, PC’s and a MacBook Pro.  Products and languages covered include BASIC, FORTRAN, SAS, Oracle, Teradata and Tableau.

It is truly astonishing

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Tableau customer profile, Eystein from Norway, Banking & Insurance

Tableau customer profile: Eystein from Norway

Customer
Eystein from Norway

Favorite quote speaking with him
“Make it simple and fast, my IT team was cheering on Tableau after trying it and QlikView.  They really hoped we would pick Tableau and I am very happy we did.”

Situation
Eystein recently took over a BI team that wanted to accelerate use of data across every area of the bank and subsidiaries.  The bank and subsidiaries are heavy users of SAS Enterprise Guide, SAS Web Report Studio and SAS Enterprise Miner. 

They have a variety of data sources and data marts throughout the business.  There is also a centralized Enterprise Data Warehouse (EDW) effort that is making headway, but has a long list of items ahead of them.  In the meantime, SAS users are rapidly creating new data pulls that Eystein would like to use in Tableau.  He realizes that the EDW team will never be able to have all the data that is needed by the business, so SAS is a good platform to prep the data for widespread analysis in Tableau.

Status

They recently evaluated fast analytic tools from

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