Recent Work: Fern Halper

Fern Halper

Fern Halper, Ph.D.

Vice President and Senior Director of TDWI Research for advanced analytics

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, social media analysis, text analytics, 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.



  • Modern Data Architectures to Support Modern Analytics

    Many organizations today are scrambling to meet the needs of new data types and analytics. TDWI research shows that companies are often analyzing data from multiple sources, including structured data, unstructured data, real-time streaming data, location data, and transactional data. They are making use of new techniques such as text analytics and machine learning, and they are moving towards self-service analytics. The traditional data warehouse or data mart is often limited in its ability to support these modern analytics in a fast and friendly way. July 12, 2018 View Now

  • Practical Predictive Analytics – Results of New TDWI Best Practices Research

    Predictive analytics is now part of the analytics fabric of organizations. TDWI research indicates that it is in the early mainstream phase of adoption. Yet, even as organizations continue to adopt predictive analytics and machine learning, many are struggling to make it stick. Challenges include lack of skills, executive and organizational support, and data infrastructure issues. June 21, 2018 View Now

  • Achieving Business Value Using Hybrid Analytics

    As companies progress in their analytics efforts, they often look to leverage a hybrid cloud analytics model—one where data from both on-premises and cloud sources is analyzed seamlessly. This approach makes sense especially when analyzing data from diverse sources using more advanced analytics such as machine learning and predictive analytics. Data that is generated both in the cloud and on-premises often needs to be analyzed together. June 20, 2018 View Now

  • Analytics Everywhere: Building Analytics Applications for Driving Business Value

    Analytics has become mainstream, and TDWI research indicates that the vast majority of organizations have adopted technologies such as dashboards and visual analytics. However, as organizations mature along their analytics journey, they are looking to embed their analytics into devices, applications, and systems. Embedding analytics layers analytics into another application or process and brings the results of analysis to the decision maker through applications that run the business. The result is opening up analytics to more users and making analytics relevant, actionable, and more valuable. May 30, 2018 View Now

Upside Articles