ABCtech #17 25May14 Predictive Analytics

ABCech #17 25MAY14 Predictive Analytics

Quik Tech Notes

25-MAY-14

“The Next Question”

Predictive Analytics – by Jen Cruz and Stuart Randell, Bow River Solutions

Everyone is talking about predictive analytics and it is sounding like it is a new and shiny toy, a fancy emerging technology that is going to change our lives.  The radical change for the business world is that it gives executives the ability to use their past data to predict future events and optimal courses of action. This technology may be shiny but it’s definitely not new.

This niche area of analytics that was once only for the hardcore mathematicians or statisticians, has been put it into the hands of everyday business users.  Advancements in technology have made processing faster with intelligent algorithms to do complex mathematics while end users interact with simplistic user interfaces. This opens up a world of possibilities as business users with little or no understanding of mathematics can enjoy its benefits.

Today, every time we go online we are consumers of predictive analytics – sites like Amazon.com read data in our web browser history and then display items based on what they think we want to see.  Amazon is predicting items that we might be interested in and displaying this under the category: “Customers Who Bought This Item Also Bought“.  In many industries predictive analytics is being used to process information such as weather, temperature, humidity and determine its impact on sales or on technical processes like manufacturing.

The interesting points about weather and sales is the impact on different weather patterns in different geographies, cold weather prompting immediate outer wear purchases versus after five days of cold in areas that are more conditioned to colder weather.  Other companies have been analyzing their data and finding how even slight changes in the environment can have large impacts. Such as a manufacturing company which found that afternoon sun coming in through windows in the plant was affecting the quality of their process.  This subtle difference was not obvious without the use of predictive analytics but suddenly became apparent once plugged into a model.

The examples of predictive analytics are endless, from the small cupcake maker that uses weather data to determine the best volume and flavor for certain days, to large mining companies identifying what machines need maintenance before they break down.  A car manufacturer has increased customer satisfaction by detecting problems earlier, and a rugby team in Australia has predicted and prevented player injury. IBM is even looking at using SPSS the technology behind the Jeopardy! winning Watson computer to predict and diagnose diseases.

Yet despite the benefits, uptake has been slow because of the belief that all of the data had to be brought into a data warehouse first before any analytics can be done.  It was thought that data needed to be perfect.  However, a lot can be done with imperfect data, valuable insights can be lost while waiting for the perfect situation.  The tools are getting so usable that anyone that has a spreadsheet of data can easily bring it into a modeling tool and start to see patterns emerge.

Predictive analytics has finally become a reality. The data is certainly there and now so is the technology.  The Next Question is what the community of users and enthusiasts will look like and how they will continue to not only advance the technology but also incorporate it into our personal lives and businesses.

Also Read HERE  Is there really a “STEM Crisis” – a frank discussion of the shortage of scientists, technicians, engineers, and mathematicians.

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