Fern Halper
Fern Halper is director of TDWI Research for advanced analytics, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and other “big data” analytics approaches. She has more than 20 years of experience in data and business analysis, and has published numerous articles on data mining and information technology. Halper is co-author of “Dummies” books on cloud computing, hybrid cloud, service-oriented architecture, and service management, and big data. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for AT&T Bell Labs. Her Ph.D. is from Texas A&M University.
Predictive analytics, a technology that has been around for decades has gotten a lot of attention over the past few years, and for good reason. Companies understand that looking in the rear-view mirror is not enough to remain competitive in the current economy. Today, adoption of predictive analytics is increasing for a number of reasons including a better understanding of the value of the technology, the availability of compute power, and the expanding toolset to make it happen. In fact, in a recent TDWI survey at our Chicago World Conference earlier this month, more than 50% of the respondents said that they planned to use predictive analytics in their organization over the next three years. The techniques for predictive analytics are being used on both traditional data sets as well as on big data.
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With all of the research I’ve been doing around cloud computing over the past few years, I’ve noticed something very disturbing about how people use the word premises. I’ve blogged about this before but it merits repeating on my TDWI blog. Maybe it’s because I come from a telecommunications background that this bothers me so much – but has anyone else noticed that people are misusing the words premise/premises when describing aspects of the cloud? The proper term is generally premises, people, as in – on your premises (see below).
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Last week I attended the IBM Big Data at the Speed of Business Event at IBM’s Research facility in Almaden. At the event IBM announced multiple capabilities around its big data initiative including its new BLU Acceleration and IBM PureData System for Hadoop. Additionally, new versions of Infosphere Big Insights and Infosphere Streams (for data streams) were announced as enhancements to IBM’s Big Data Platform. A new version of Informix that includes time series acceleration was also announced.
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I’ve recently spent a lot of time talking to companies about how they’re using text analytics. By far, one of the biggest use cases for text analytics centers on understanding customer feedback and behavior. Some companies are using internal data such as call center notes or emails or survey verbatim to gather feedback and understand behavior, others are using social media, and still others are using both.
What are these end users saying about how to be successful with text analytics? Aside from the important best practices around defining the right problem, getting the right people, and dealing with infrastructure issues, I’ve also heard the following:
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I am excited to join TDWI as the Research Director for Advanced Analytics. Of course, different people have different definitions for advanced analytics. Here’s how I define it. Advanced analytics provides algorithms for complex analysis of either structured or unstructured data. It includes sophisticated statistical models, machine learning, neural networks, text analytics and other advanced data mining techniques. Among its many use cases, it can be deployed to find patterns in data, prediction, optimization, forecasting, and stream mining. It typically does not include simple database query and reporting or OLAP cubes.
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