• 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.