Experts Blog: 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.
By Fern Halper
The TDWI Big Data Maturity Model and assessment tool is set to launch on November 20. Krish Krishnan and I have been working on this for a while, and we’re very excited about it.
As I mentioned in my previous blog post (see previous post below), there are two parts to the Big Data Maturity Model and assessment tool. TDWI members will be getting an e-mail about the assessment on November 20. We urge you to take the assessment and see where you land relative to your peers regarding your big data efforts. Additionally, it’s important to note that we view this assessment as evolutionary. We know that many companies are in the early stages of their big data journey. Therefore, this assessment is meant to be evolutionary. You can come back and take it more than once. In addition, we will be adding best practices as we learn more about what companies are doing to succeed in their big data efforts.
Posted on November 18, 20130 comments
We are getting ready to launch the TDWI Big Data Maturity Model and assessment tool in the next few weeks. We’re very excited about it, as it has taken a number of months and a lot of work to develop. There are two parts to the Big Data Maturity Model and assessment tool. The first is the actual TDWI Big Data Maturity Model Guide. This is a guide that walks you through the actual stages of maturity for big data initiatives and provides examples and characteristics of companies at different stages of maturity. In each of these stages, we look across various dimensions that are necessary for maturity. These include organizational issues, infrastructure, data management, analytics, and governance.
Posted on October 29, 20130 comments
As I mentioned in my last blog post, I am in the process of gathering survey data for the TDWI Best Practices Report about predictive analytics. Right now, I'm in the data analysis phase. It turns out (not surprisingly) that one of the biggest barriers to adoption of predictive analytics is understanding how the technology works. Education is definitely needed as more advanced forms of analytics move out to less experienced users.
With regard to education, coincidentally I had the pleasure of speaking to Eric Siegel recently about his book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (www.thepredictionbook.com). Eric Siegel is well known in analytics circles. For those who haven’t read the book, it is a good read. It is business focused with some great examples of how predictive analytics is being used today.
Posted on September 11, 20130 comments
I am in the process of collecting data for my TDWI Best Practices Report on predictive analytics. The report will look at trends and best practices for predictive analytics. Some specific issues being investigated in the survey include: Who is using predictive analytics? What skills are needed for it? Is it being used in big data analysis? Is it being used in the cloud? What kind of data is being used for predictive analytics? What infrastructure is supporting it? What is the value that people using it are getting from it? The survey is slated to run another week, so if you haven’t had the chance to take it yet, please do. Here is the link:
Posted on August 12, 20130 comments
Geospatial data can be extremely powerful for a wide variety of use cases. Geospatial analysis – i.e. the practice of incorporating spatial characteristics in various kinds of analysis- has been incorporated in BI and visualization solutions for at least several years. Recently I’ve been hearing a lot from vendors about geospatial applications and using geospatial data in a range of more advanced analytics. In a recent TDWI technology survey, you can see that the geospatial analytics is growing in importance. We asked the respondents, “What kind of analytics are you currently using in your organization today to analyze data? In three years?” and “What kinds of techniques and tools is your organization using for big data analysis both today and in three years?” In the figure below, the 39% of respondents were currently using geospatial analysis and this number jumped to 79% in three years. The number of respondents answering affirmatively that geospatial analysis would be used in their big data solutions in three years was 81%.
Posted on July 1, 20131 comments
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.
Posted on May 22, 20131 comments