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Experts Blog: Fern Halper

Fern HalperFern Halper, Ph.D., is well known in the analytics community, having published hundreds of articles, research reports, speeches, Webinars, and more on data mining and information technology over the past 20 years. Halper is also co-author of several “Dummies” books on cloud computing, hybrid cloud, and big data. She is the director of TDWI Research for advanced analytics, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and “big data” analytics approaches. 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 fhalper@tdwi.org, on Twitter @fhalper, and on LinkedIn at linkedin.com/in/fbhalper.



Fern Halper

By Fern Halper


3 Interesting Results from the Big Data Maturity Assessment

Almost a year has passed since the launch of the TDWI Big Data Maturity Model and assessment tool, which I co-authored with Krish Krishnan. To date, more than 600 respondents have participated in the assessment.

We asked questions in five categories relevant to big data:

  1. Organization: To what extent does your organizational strategy, culture, leadership, and funding support a successful big data program? What value does your company place in analytics?
  2. Infrastructure: How advanced and coherent is your architecture in support of a big data initiative? To what extent does your infrastructure support all parts of the company and potential users? How effective is your big data development approach? What technologies are in place to support a big data initiative, and how are they integrated into your existing environment?
  3. Data Management: How extensive is the variety, volume, and velocity of data used for big data analytics, and how does your company manage its big data in support of analytics? (This includes data quality and processing as well as data integration and storage issues.)
  4. Analytics: How advanced is your company in its use of big data analytics? (This includes the kinds of analytics utilized, how the analytics are delivered in the organization, and the skills to make analytics happen.)
  5. Governance: How coherent is your company’s data governance strategy in support of its big data analytics program?
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Posted on October 16, 20140 comments


Big Data and the Public Cloud

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:

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Posted on March 3, 20140 comments


Four Ways to Illustrate the Value of Predictive Analytics

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.

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Posted on January 20, 20141 comments


Three Starting Points for Big Data Initiatives

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.

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Posted on November 18, 20130 comments


Four Big Data Challenges

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.

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Posted on October 29, 20130 comments


Deathtrap: Overlearning in Predictive Analtyics

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.

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Posted on September 11, 20130 comments


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