Marketing Technology Q & A with Leo Scott: What’s Next with Predictive Analytics


At WealthEngine, we understand that our customers care a lot about understanding data and gleaning the actionable insights necessary to move the needle. We also know that they care a lot about privacy and data security. So does our Chief Technology Officer, Leo Scott.

Recently, Leo took the time to answer questions we’ve heard are top of mind for our customers from a technologist’s point of view. We’re sharing these as a series here on the WealthEngine blog weekly.

This week he’s talking about predictive analytics and what’s next.

Predictive analytics is hot right now. What do you think we’ll be seeing in this space over the next few years?

Yes – everybody talks about predictive analytics.  Much of it to date is often basic correlation analysis. Finding certain attributes that correlate strongly to others.  In the future, there will be more emphasis on continual, dynamic learning models, rather than more static models.  Whether it’s simple static correlations or dynamic learning models – what makes predictive analytics interesting and fun today is the continually growing amount of data that is available today.  It’s becoming easier to gain access to a broader array of data.  This is something that WE specializes in.  Making a large amount of data from many different data sources available and easy to use all in one place.  

As you have more data, more attributes –  you can find some very interesting correlations you would have never guessed existed and this can lead to analytics that that give you insight using a set of data you would have never guessed would have given you that insight.

As the amount of data grows and the speed of incoming data grows, learning algorithms will become more important.  The world is not static, and the amount of resources it takes to manually build a model  is rather large.  Therefore, it will important for models to adjust themselves as data comes in.  

Another important area when it comes to understanding data is natural language processing.  Huge amounts of data that is flowing around is not structured.  Or better said, it’s structured for human consumption, not simple machine consumption.  The research and machine power has reached a point where over the next several years we will see machines starting to understand the endless streams of text and automatically generate its own structured data and quantitative analysis by understanding what is being said and tallying results.  This will go way beyond the rudimentary approaches used today like counting words.

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Leo Scott is the Chief Technology Officer at WealthEngine.  As an accomplished business executive who founded a number of startup ventures and has worked with a variety of large public companies, Leo has spent the last 20 years building technologies that harness massive amounts of data and enable businesses and consumers to more easily interact with that data through Internet and mobile technologies.  Over the years, he has maintained a hands-on level of experience with the latest technologies, not only tracking the latest trends and tools, but working to develop a deeper working knowledge of new technologies allowing him to see technical approaches and opportunities in unique ways rarely seen by other individuals.