Is it Possible to Make Predictive Analytics Pervasive?

I just got back from the IBM Information on Demand (IOD) conference in Las Vegas.  A key message was that the future is in analytics and predictive analytics at that.  IBM has already invested $12B ($8B acquisitions, $4B organic growth) in analytics since 2005.  Its recent purchase of SPSS has enabled the company to put a stake in the ground regarding leading the analytics charge.

Predictive analytics uses historical data to try to predict what might happen in the future.  There are different technologies that can help you to do this including data mining and statistical modeling.  For example, a wireless telecommunications company might try to predict churn by analyzing the historical data associated with customers who disconnected the service vs. those that did not.  Attributes that might serve as predictors include dropped calls, calling volume (in network, out of network), demographic information, and so on.  An insurance company might try to predict future fraud using past claims that where the outcome is known.  Adam Gartenberg’s blog describes more examples of this.  IBM plans to make predictive analytics more pervasive in several ways. 

  • Making models easier to build. It will make predictive modeling tools easier to use for those who build the models.  A good example of this is the SPSS PASW Modeler product that uses a visual paradigm to build various kinds of models.  I stopped by the SPSS booth at the show and saw the software at the demo area and it is nice with lots of feature/functionality built into it.  Training is available (and I would argue necessary), for example, to understand when you might want to use a certain kind of model. 
  • Embedding the predictive model in a process.  Here, the predictive model would become part of a business process. For example, a predictive model might be built into a claims analysis process.  The model determines the characteristics and predictors of claims that might be classified as fraudulent.  As the claims come through the process, those that are suspicious, based on the model, would get kicked out for further examination.  

So, given these two approaches, can predictive analytics become pervasive? 

In the case of making predictive modeling tools easier to use, the question isn’t whether someone can use a tool, but whether he or she can use it correctly.   The goal of a tool like PASW is to enable business users to build advanced models. Could a BI power user who is accustomed to slicing and dicing and shaking and baking data effectively use a tool like this?  Possibly, if they have the right thought process and they pay attention to the part of the training that describes what type of technique to use for what type of problem.  It is a good goal.  Time will be the judge.

As for embedding predictive analytics in business processes; this is already starting to happen and here is where the possibility of making prediction more pervasive gets exciting.  For example, telecommunications companies can embed predictive analytics into a call center application to understand an action that a customer might take.  A call center representative can make use of the results of the model (without understanding the model or what it does).  He or she is simply fed information, from the model, (in real time) to help service a customer most effectively.   The model can be created by a skilled analytics person, but deployed in such a way that it can help a lot of other people across an organization.  One key will be the ability to integrate a model into the actual code and culture behind a business process.

Look, I don’t have a crystal ball (little predictive modeling humor there), but I am very excited about the possibilities of predictive modeling.  I did this kind of modeling for years at Bell Laboratories, way back when, and it is great to see it finally gaining traction in the marketplace.  Predictive analytics can be a truly powerful weapon in the right hands.

Four reasons why the time is right for IBM to tackle Advanced Analytics

IBM has dominated a good deal of the news in the business analytics world, recently. On Friday, it completed the purchase of SPSS and solidified its position in predictive analytics.  This is certainly the biggest leg of a recent three-prong attack on the analytics market that also includes:

  • Purchasing Red Pill.  Red Pill is a privately-held company headquartered in Singapore that provides advanced customer analytics services –  especially in the business process outsourcing arena.  The company has talent in the area of advanced data modeling and simulation for various verticals such as financial services and telecommunications. 
  • Opening a series of solutions centers focused on advanced analytics.  There are currently four centers operating now: in New York (announced last week), Berlin, Beijing, and Tokyo.  Others are planned for Washington D.C. and London. 

Of course, there is a good deal of organizational (and technology) integration that needs to be done to get all of the pieces working together (and working together) with all of the other software purchases IBM has made recently.  But what is compelling to me is the size of the effort that IBM is putting forth.  The company obviously sees an important market opportunity in the advanced analytics market.  Why?  I can think of at least four reasons:

  • More Data and different kinds of data.  As the amount of data continues to expand, companies are finally realizing that they can use this data for competitive advantage, if they can analyze it properly.  This data includes traditional structured data as well as data from sensors and other instruments that pump out a lot of data, and of course, all of that unstructured data that can be found both within and outside of a company.
  • Computing power.  The computing power now exists to actually analyze this information.  This includes analyzing unstructured information along with utilizing complex algorithms to analyze massive amounts of structured data. And, with the advent of cloud computing, if companies are willing to put their data into the cloud, the compute power increases.
  • The power of analytics.  Sure, not everyone at every company understands what a predictive model is, much less how to build one.  However, a critical mass of companies have come to realize the power that advanced analytics, such as predictive analysis can provide.  For example, insurance companies are predicting fraud, telecommunications companies are predicting churn.  When a company utilizes a new technique with success, it is often more willing to try other new analytical techniques. 
  • The analysis can be operationalized.  Predictive models have been around for decades.  The difference is that 1) the compute power exists and 2) the results of the models can be utilized in operations.  I remember developing models to predict churn many years ago, but the problem was that it was difficult to actually put these models in to operation.  This is changing.  For example, companies are using advanced analytics in call centers.  When a customer calls, an agent knows if that customer might be likely to disconnect a service.  The agent can utilize this information, along with recommendations for new service to try to retain the customer. 

 So, as someone who is passionate about data analysis, it is good to see that it is finally gaining the traction it deserves.

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