Defining Rapid Insight

I recently had the opportunity to sit down with Mike Laracy, President and CEO of Rapid Insight to ask him a few questions about analytics in higher education, predictive modeling, and Rapid Insight. I’ll be posting the interview as a two part series here on the blog (with part two located here). The first part is the story of Rapid Insight – how it started, what we do, and where we’re going – enjoy!

Rapid Insight has been around since 2002. Can you tell us a bit of the story on how the company came to be?
I had been doing a lot of work in the analytic space using software tools like SAS and SPSS.  I found predictive modeling to be such a clunky, painful process and I knew there had to be a more efficient way to analyze data and build predictive models.   Working as an analytic consultant, I had the opportunity to see how lots of companies were interacting with their data.  Even the large Fortune 500 companies were struggling to analyze their data and build models.  The problem was that the only tools available were tools that had been developed decades earlier for programmers and academic researchers.

I had been living in Boulder, Colorado when I developed the concept of Rapid Insight.  I spent a lot of time thinking through the predictive modeling process and figuring out how it could be automated and streamlined.  I sat on the concept for a couple of years before actually starting the company.

In 2002 I had moved here to North Conway and decided to rent some office space to start developing the concept of Rapid Insight into an actual software product.  For the first six months it was just me.  I spent that time writing the algorithms and developing a working prototype.  I wasn’t a programmer and I knew that to turn the software into a commercial application, I’d need more help.  I hired a software developer who is still with the company today as our lead engineer.   A year later we hired another developer.  In 2006 we hired our first salesperson, launched Veera Predict, and we’ve been growing ever since.

Do your products focus exclusively on predictive analytics?
Our products also focus on ad hoc analysis and reporting.  In 2008, we launched our second product called Veera Construct.  Whereas Veera Predict automates and streamlines the process of predictive modeling and analysis, Veera Construct focuses on the data.  Data is typically scattered between databases, text files and spreadsheets, with no easy way to organize it and piece it together for modeling and analysis.  Veera Construct solves that problem.  It’s a data agnostic technology that allows access to any database and any file format and makes it easy for people to integrate, cleanse, and organize their data for modeling, reporting, or simply ad hoc analysis.

We initially developed this technology as a tool to organize data for predictive modeling.  We’re now seeing enormous demand for the tool as a standalone technology as well.  Colleges and universities use it for reporting and ad hoc analysis.  Companies like Choice Hotels and Amgen use it for processing analytic datasets with data coming from disparate sources.  Healthcare organizations are using it for reporting and performing ad hoc analyses on their databases.  Defense contractors are using it for cyber security.

What makes your company different from others working in the higher ed space?
In higher ed there are consulting companies that provide predictive modeling services.  You send them your data, and they build a model and send you back the model and a report.  But the institution still has to do the prep work to create the analytic file, which is 90% of the effort.  This process is both expensive and time-consuming, and the knowledge gained from the analysis isn’t always transferred back.   By bringing predictive modeling in-house, changes can be made on the fly without having to send data anywhere and models can be changed and updated very quickly, which is important because modeling is such an iterative process.

We provide schools with a means of doing this analysis and building their own models.   One advantage is that the knowledge is always captured internally.  But the biggest advantage is the ability for institutions to be able to ask questions of their data and answer them on the fly.

As far as other software products that are being used in higher ed, we’re very different from tools like SAS or SPSS in that the users don’t need to be programmers or statisticians to build models using our tools.  I think if you ask the question of our customers you’d find that one of our biggest differentiators from these types of products is our customer support.  Our analysts are available to help our clients with any questions as they build models, analyze data, or create reports.  Whether the questions pertain to using our technology or about interpreting the results, we are always available to help.  We want to ensure that our customers grow their own analytic sustainability.

.click here for Part Two, where Mike shares more about predictive modeling in higher education.

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Decentralize analytics. Harness the power of many.