Experts Blog: Fern Halper
Fern Halper, Ph.D., 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, service management, and big data. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her at firstname.lastname@example.org, or follow her on Twitter: @fhalper.
By Fern Halper
TDWI just released my newest Checklist Report, Seven Considerations for Navigating Big Data Cloud Services. The report examines what enterprises should think about when evaluating the use of public cloud services to manage their big data. The cloud can play an important role in the big data world since horizontally expandable and optimized infrastructure can support the practical implementation of big data. In fact, there are a number of characteristics that make the cloud a fit for the big data ecosystem. Four of these include:
Posted on March 3, 20140 comments
My new (and first!) TDWI Best Practices Report was published a few weeks ago. It is called Predictive Analytics for Business Advantage. In it, I use the results from an online survey together with some qualitative interviews to discuss the state of predictive analytics, where it is going, and some best practices to get there. You can find the report here. The Webinar on the topic can be found here.
Posted on January 20, 20141 comments
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