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.
8 thoughts on “Is it Possible to Make Predictive Analytics Pervasive?”
Fern, thank you for posting this update. I have been quite impressed with IBM’s push into predictive analytics. As part of their investment they have been creating analytical centers of excellence around the world, I expect great things to come in the future.
Coincidentally, a couple of days ago I wrote a few comments about predictive analytics and business process. It does seem this integration is happening more often and with more measurable results.
I believe Predictive Analytics will become more pervasive. A combination of technology improvements (including data explosion) will contribute to greater adoption. As the market matures I think the emphasis will switch a bit from tools to solutions with niche packaged applications that solve specific problems for different business functions/industries beyond marketing or underwriting.
Great post Fern. I blogged it and like you think the future for predictive analytics is an interesting one.
[…] truths/myths earlier today I was reminded that I had not posted about Fern Halper’s post: Is it Possible to Make Predictive Analytics Pervasive?. I enjoyed Fern’s post and meant to blog about it but then got distracted. She makes the key […]
Fern, excellent posting. I absolutely agree that predictive analytics will become pervasive. By embedding predictive decision models at the core of every business process, we will see the real impact of predictive analytics, realize its potential to optimize existing processes, enable intelligent automation, and provide exceptional ROI.
Of interest in this context is the Predictive Model Markup Language (PMML – http://www.dmg.org ) as the de-facto standard for data mining models. Supported by all major vendors and open source tools, it facilitates the deployment and real-time execution of predictive models in operational environments. Please feel free to join our active discussion group for PMML on LinkedIn.
Predictive Model Markup Language (PMML) group:
nice information you provided. I want some more knowledge about predictive modeling. can you suggest me some books or website where i can get some what practical knowledge about this subject.
Thanks a lot in advance
Berry and Linoff write some good books on data mining
Nice post. Informative as usual.
The Internet in particular is a vast repository – like a big bowl of spagetti -that can be set up like a data warehouse. I recently met up with some investors who are excited about using predictive analytics and text mining in the consumer goods space to model buying behaviour. I expect IBM will continue investing…
If data mining searches for clues, predictive analytics delivers answers that guide you to a “what next” action.
But at the end of the day “Truthful data” is what we all should be striving for.