#TCC13

In honor of the upcoming Tableau Customer Conference and our recent partnership, I sat down with Rapid Insight President & COO, Ric Pratte, to talk about the partnership, what to expect from Rapid Insight at the conference, and how predictive analytics and data visualization go together. 

Can you talk about the partnership between Rapid Insight and Tableau?

It was an observation that a number of our very successful clients were using Tableau, and it was this observation that led us to build a partnership. We have strengths in massaging, analyzing, and predicting data, and we’ve gained a partner who helps communicate that data to executives and decision makers in a way that’s easily understood. It’s a very complementary relationship. We both share a common position focused on empowering business users to make data-driven decisions by using their data to look forward. Essentially, we’re working together to help people visualize the future.

We now have a Tableau page on our website that we’re constantly updating so that visitors can continue to learn about the power of combining predictive modeling and visualization, and that’s a great place to get more information.

How do the two products interface?

Actually our interfaces focus on the same methodology – no coding, and the user manipulates graphical objects, places them where they need to, and literally connects the dots to perform an analysis. We’re able to natively connect the output of data analysis from our tools directly into the Tableau system as a .tde system, as well as the ability to output to the cloud. The process is very smooth.

How can visualization enhance predictive analytics work?

Visualization provides the end user a better way to see the results of a predictive model applied in the context of a business problem.

For example, a heat map overlay onto a geographic region of customers who are most likely to renew, purchase, or enroll tells a much more powerful story than summary statistics or a table containing the same information. Visualizations are great for storytelling and physically seeing things in your data that you may have missed in a more black and white analysis. By adding a visual component to their predictive analytics work, users can make data-driven decisions faster.

What value does predictive analytics add to your visualizations?

It’s one thing to know where your current customers are, but it’s a different thing to know where your future customers will be coming from. If your visualizations are based on traditional data analytics, you can think of them as looking at what’s in your rearview mirror. Useful, but driving your car while looking in your rearview may not get you where you want to go. Using your data to look at what’s coming down the road will help you set a clear path based on data-driven decisions. Once you’ve predicted the probability of future outcomes, you can focus your resources accordingly.

What can attendees expect to see from Rapid Insight at TCC13?

We’ll be doing some short sessions with attendees so that they can see the entire process – all the way from data extracting and federating through the data cleanup and modeling phase, and ending with how to bring the data into Tableau for visualizations. We’ll show the contrast between predictive and non-predictive visual outputs to demonstrate the power of predictive analytics. We’ll have a few examples and datasets to play with.

We’ll be sending part of our executive team – Mike, Sheryl, and myself – and our booth will be fully stocked with chocolate, which are also great reasons to stop by.

Mike Laracy is co-presenting with Yale at TCC13. What do you expect from their presentation?

Yale has lots of and lots of data and needed to find the most efficient way to analyze it to predict donor behavior. Their presentation, "Fusing Predictive Analytics and Data Visualization", will be on Tuesday September 10th at 3pm in Annapolis 3-4. They’ll be presenting a case study on their successful use of predictive modeling and discuss how they are sharing and communicating the results through visualization. This will be another expanded example of the full process with the added viewpoint of the end customer and their experience in starting this, the iterations they’ve went through, and how they’ve reached success.

How can I learn about the partnership if I’m not attending the conference?

After the conference, we have a follow-up webinar on September 17th for everyone who can’t attend. It’s a co-hosted webinar between Rapid Insight and Tableau where we have some data analysts who will show some new examples of the process. It will be a good way to gain an understanding of how you can put predictive analytics to use to gain a competitive advantage for your business. [For more information, or to register, click here.]

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Ric Pratte, President and COO of Rapid Insight, is a longtime entrepreneur with a history of building innovative software companies. He was previously the CEO/Co-founder of JitterJam, a pioneer of Social CRM that was acquired by the Meltwater Group in 2011. He is a father of two, an avid skier and backpacker, and devotes time and energy to numerous non-profit organizations including Girls, Inc. and the Boy Scouts. You can follow him on Twitter at @ricpratte.

Big Data and New Methods

Guest post by Chuck McClenon, Fundraising Scientist from University of Texas at Austin 

When I went to my first APRA Data Analytics Symposium in 2010, the use of analytics in support of philanthropic fundraising was a novelty.   “Analysis”, for most organizations, consisted of descriptive statistics in Excel.  A few pioneers had built regression models, and the Symposium faculty pretty much consisted of those who could explain the differences between Ordinary Linear and Logistic Regression. 

What a difference three years has made!  At this year’s Symposium in Baltimore we considered keyword analysis, hierarchical linear modeling, visualization, and the use of financial industry formulae for portfolio optimization.  We have progressed beyond regression and now have the critical mass of practitioners throwing ideas at each other.  And at many of our institutions we are also accumulating the critical mass of data to support serious mining, and try these new approaches.

Alan Schwartz, formerly with ESPN and more recently the New York Times, gave the keynote address.  Alan had written a series of article for the Times, over several years, examining the incidence of concussions among NFL players, and their long-term effects, including early-onset dementia.  One retired player with dementia at age 50 does not tell a story and the pushback was that there wasn’t enough data, but most of the data is buried in medical records and team records.  The demand for more data was a case of the “better” being the enemy of the “good”.  This one didn’t really require Big Data, it just needed Enough Data.  Early onset dementia is normally extremely rare.  When you have five cases, in a population of only 2000+ retired NFL players, it’s hardly chance.  Schwartz’ exposition is leading to real changes in how head injuries are being regarded in football, down to college, high school, and youth leagues.  Tenacity with data, that’s what analytics is about.

Divah Yap of the University of Minnesota offered an intriguing presentation on scoring the free text in contact reports for words or phrases which may tend to indicate attitude toward the organization.  We have a lot of usable data around us, if we know how to decompose it and connect dots.  When we have enough data, well-organized, we can understand it in ways we never could before.

Visualization may be coming of age as part of analysis. One of our fundraising projects here at UT which we have mostly failed at so far is to find donors for the Texas Advancement Computing Center (TACC) and its visualization lab.  But if we can’t help them, maybe they can help us.  In a few weeks we’re going to get together with them, and hand them the keys to our data warehouse, and see if they can paint it in colors we never imagined, and help us to see it in ways that the numbers alone don’t tell us.

In my college class on linear methods, we were warned strictly against correlation fishing.  In your typical experiment in human psychology, p < .05 is the standard, and if you run your experiments on twenty or fifty or even a hundred subjects, getting past p < .05 can be a challenge.  And of course the measurement “P = .05” means that there is a one in twenty chance that the conclusion is wrong.  Run ten such studies, and there’s a 40% likelihood that at least one of your conclusions, if not more, will be wrong.  

Taken a different way, and this is where the dictum against correlation fishing comes in, if you have a file with ten independent variables, and you threw it into a correlation matrix, there would be 45 pairs of variables to correlate, and if you set your standard going in as p < .05, then from those 45 pairings you could expect to draw two false conclusions.  Try it on a file of twenty variables or more, with hundreds of combinations to test, and there is a real risk that the apparent correlations are simply the random noise in the sample, and are as much a reflection of tides and astrology as they are of anything causative within the population.   And with more variables thrown into the mix, there is also the increasing risk of multi-collinearity if your variables are in fact numerically related in their derivations. 

But when we study donor behavior in large organizations, we move beyond the realm of the psychology lab and limited sample sizes.  The University of Texas at Austin has a constituent database of over 500,000 alumni and friends.  I have decades of gift history, and I have acquired consumer behavior information, derived from point-of-sale and other sources.   People with cats give more to the arts, people with dogs give more to athletics, but in the end their total giving is similar.  I can say this “with confidence”, when  p < .0001.  Big Data tells us stories, and illustrates them in color.  This doesn’t mean that I can operationalize any strategy dependent on dogs and cats --  especially never depend on cats – but it does give us new insights.

Coming back to the conference, if there are a half-dozen presenters offering totally novel approaches to analysis, then the probability is fairly high that any one of them may be a total waste of time, but there’s a pretty good chance that at least one or two of them contain real nuggets.  That’s the nature of data mining, and it’s also why we go to conferences, to look for new insights, which may or may not be usable.  Coming away from this year’s Symposium, many of us are feeling almost overwhelmed by new ideas, and just wishing we had the time needed to explore all of them. 

Big Data?  How Big is big enough, and how much is too big?  That’s becoming a difficult question, and the boundaries of privacy will be a philosophical argument for years to come.  I’ve reached the unscientific conclusion that market segmentations such as Claritas or PersonicX clusters are dead on the money 85% of the time, a little bit off 10% of the time, and absolutely wrong 5% of the time.  When there’s so much data around, and They seem to have such a complete picture of the individual, is it comforting to know that some of it is probably wrong, and so the picture that They have of us isn’t as accurate as we’re afraid?   When I talk about cat owners and dog owners, should you be shocked that I know so much about my constituents, or shocked that I draw conclusions from such imperfect data?  Perhaps both, but Big Data is becoming reality, and so we will learn to use it for what it is, to use it wisely and respectfully.

Organize, transform, restructure, build a systematic repository.  Mine for connections.  And if a you don’t have a supercomputer for your visualization, Tableau may take you a long way.

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About Chuck:  Chuck McClenon arrived at the University of Texas at Austin in 1975, earned a PhD in linguistics, dabbling in the nascent technology of pattern recognition.  After a year teaching in English in China, he returned to UT to work in administrative information management, searching for patterns and meaning in data ranging from student course registrations to library book titles to the bit-paths of room keys.  He joined the advancement operation as an IT manager in 1996 at the start of UT’s first comprehensive capital campaign. After a brief tour of duty managing the gift processing and donor records operation, he retired to a cave and immersed himself in phonathon results and gift officer contact reports. Now he spends his days acquiring, constructing, managing and analyzing data representing the full spectrum of advancement activity.  Since 2006, he has held the official title of Fundraising Scientist.

#RIUC13

For those of you who weren’t able to attend the 2013 Rapid Insight User Conference, we set a new record for most attendees and largest number of customer presentations. With two full days of dual track programming, the presenters covered a lot of ground. While we wait for some of the video recordings of customer presentations to be formatted, I thought it would be good to do a quick recap here. 

Mike Laracy, Data Geek (at right)

The conference opened with a keynote from our Founder and CEO, Mike Laracy, who talked a bit about the future of predictive analytics. With a mass public education on the value of analytics (from people like Nate Silver and Billy Bean, with a little help from Brad Pitt), as well as significant advances in data storage and processing power, a stronger need for predictive analytics is emerging. The market is shifting towards the view that more data access is better than restricted access, and that given the right tools along with access, smart people – data scientists – can turn raw data into actionable information. Given these changes, the data scientist – that’s you – will be in increasingly higher demand over the next decade and beyond, as will predictive analytics. 

The user presentations covered lots of different topics, and we’ve made all of their slide decks available here; I’d highly recommend checking them out. In addition to what’s there, I’d also recommend checking out some of the interviews we’ve done with customers on building campaign pyramids and using predictive modeling to drive fundraising efforts. The RI staff team also gave a few presentations,  including topics like Tips and Tricks in Veera, Techniques for Improving Your Predictive Models, and An Introduction to Reporting and Dashboarding with Veera.

Another thing worth mentioning is that we announced our partnership with Tableau to provide a complete solution for both predictive modeling and visualization. Now users can use Veera to clean up their data, Analytics to build their predictive models, and Tableau’s visualizations to turbocharge their presentations. For more information, check out our partner page.

I am now a scientist according to @RapidInsightInc. I feel super smart! #RIUC13
— Dustin Mayfield (@dlm0078) June 27, 2013

My favorite part of the User Conference has always been talking to customers about the cool data projects that they’ve been tackling, and this year was no different. Kudos to our users for being so creative and smart with the ways they use our software. We also owe a big thanks to the folks at Yale for hosting us, and to all who were able to attend. Here’s to the best User Conference so far and to making next year’s even better!