Part six wraps up our Digital Enterprise road map series with a focus on moving insight delivery from descriptive to prescriptive. Throughout this series, I have stressed the importance of analytic excellence to long term success. But current methods such as traditional business intelligence (BI) focus on reporting and analysis that seeks to answer questions related to past events – what happened. Advanced analytics seeks to answer questions such as: why is this happening, what if these trends continue, what will happen next (predict), and what is the best that can happen (prescribe). There is a growing view that prescribing outcomes is the ultimate role of analytics. To accomplish this, analytic initiatives need to leverage an insight-action-outcome framework that starts by defining outcome-enabling insight and ends with a focus on data provisioning.
Tag Archives: Predictive Analytics
The explosion of data and content is not limited to social media and represents a top of mind issue for many companies. The opportunity exists to create unprecedented business value – but there are significant hurdles like greater risk exposure, more complicated risk management, and difficulty extracting relevant insight from large volumes of data.
As volume grows, automation is critical. For example, social media monitoring is a common practice today, one that becomes increasingly ineffective and costly as the social web expands. Monitoring tools that enable the analysis of dialog on social networks like LinkedIn, FaceBook and Twitter provide a basic level of insight. But a deeper level of insight still requires a manual process, where irrelevant content is filtered before finding meaningful insight. Information management is therefore a growing challenge.
This very good Article by Anand S. Rao discusses the growing use of predictive analytics in the Insurance Industry. I believe Mr. Rao is right on the mark – although I continue to emphasize the expanding role of Text Analytics in the analytic value equation. In this article, he identifies some of the drivers of predictive analytics adoption.
Business Analytics refers to the skills, technologies, applications and practices for the continuous exploration of data to gain insight that drive business decisions. Business Analytics is multi-faceted. It combines multiple forms of analytics and applies the right method to deliver expected results. It focuses on developing new insights using techniques including, data mining, predictive analytics, natural language processing, artificial intelligence, statistical analysis and quantitative analysis. In addition, domain knowledge is a key component of the business analytics portfolio. Business Analytics can then be viewed as the combination of domain knowledge and all forms of analytics in a way that creates analytic applications focused on enabling specific business outcomes.
In his “Building a Smarter Planet” Blog, Steve Hamm talks about Analyzing Data in Motion to save stroke victims. There is no better way to appreciate the power of emerging analytic technology than viewing it in the context of life saving applications. This example combines very powerful stream computing technology with predictive analytics and data mining. This combination enables high speed, scalable and complex analytics of data streams in motion. As the author describes, they are using this technology to look for patterns in data that could help them identify patients who are experiencing a severe complication to ruptured brain aneurysms.