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TDWI Blog

Fern Halper, Ph.D., is vice president and senior director of TDWI Research for advanced analytics. She is well known in the analytics community, having been published hundreds of times on data mining and information technology over the past 20 years. Halper is also co-author of several Dummies books on cloud computing and big data. She focuses on advanced analytics, including predictive analytics, text and social media analysis, machine-learning, AI, cognitive computing and big data analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead data analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her by email ([email protected]), on Twitter (twitter.com/fhalper), and on LinkedIn (linkedin.com/in/fbhalper).


Growing Use Cases for Learning R and Python

By Fern Halper, VP Research, Advanced Analytics

There was a time when choosing a programming language for data analysis had essentially no choice at all. The tools were few and they were usually developed and maintained by individual corporations that, though they ensured a reliable level of quality, could sometimes be quite difficult to work with and slow to fix bugs or innovate with new features. The landscape has changed, though.

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Posted on July 26, 20170 comments


Emerging Technologies and Methods: Highlights from TDWI’s Forthcoming Best Practices Report

By Fern Halper, TDWI Research Director for Advanced Analytics

Philip Russom, Dave Stodder, and I are in the process of putting together our most recent Best Practices Report: Emerging Technologies for Business Intelligence, Analytics, and Data Warehousing. TDWI refers to new and exciting technologies, vendor tools, team structures, development methods, user best practices, and new sources of big data as emerging technologies and methods (ETMs). For example, tools for data visualization are the most hotly adopted ETM in BI in recent years. In addition to visualization, most of these tools also support other emerging techniques, namely data exploration and discovery, data preparation, analytics, and storytelling. ETMs for analytics involve advanced techniques, including predictive analytics, stream mining, and text analytics, that are progressively applied to emerging data sources, namely social media data, machine data, cloud-generated data, and the Internet of things. A number of emerging data platforms have entered data warehouse (DW) environments, including Hadoop, MapReduce, columnar database management systems (DBMSs), and real-time platforms for event and stream data. The most influential emerging methods are based on agile development or collaborative team structures (e.g., competency centers).

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Posted by Fern Halper, Ph.D. on July 30, 20150 comments


Achieving Analytics Maturity: 3 Tips from the Experts

By Fern Halper, TDWI Research Director for Advanced Analytics

What does it take to achieve analytics maturity? Earlier this week, Dave Stodder and I hosted a webcast with a panel of vendor experts from Cloudera, Microstrategy, and Tableau. These three companies are all sponsors of the Analytics Maturity Model, an analytics assessment tool that measures where your organization stands relative to its peers in terms of analytics maturity.

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Posted by Fern Halper, Ph.D. on February 6, 20150 comments


Next-Generation Analytics: Four Findings from TDWI’s Latest Best Practices Report

I recently completed TDWI’s latest Best Practices Report: Next Generation Analytics and Platforms for Business Success. Although the phrase "next-generation analytics and platforms" can evoke images of machine learning, big data, Hadoop, and the Internet of things (IoT), most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. For some organizations, next generation can simply mean pushing past reports and dashboards to more advanced forms, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis. The market is on the cusp of moving forward.

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Posted by Fern Halper, Ph.D. on December 18, 20140 comments


3 Reasons You Should Take the New TDWI Analytics Maturity Assessment

Analytics is hot—many organizations realize that it can provide an important competitive advantage. If your company wants to build an “analytics culture” where data analysis plays an essential role, your first step is to determine the maturity of your organization's analytics. To help your organizations measure their progress in their analytics efforts, we recently developed the TDWI Analytics Maturity Model and Assessment, which provides a quick way for you to compare your progress to other companies.

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Posted by Fern Halper, Ph.D. on November 6, 20140 comments


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 by Fern Halper, Ph.D. on October 16, 20140 comments