October 2016 Issue of the Statistics Digest Now Available!

This issue includes the following:
  • Mini-Paper: Big Data Terminology: Key to Predictive Analytics Success by Mark E. Johnson
  • Feature: Agile Teams: A Look at Agile Project Management Methods by L. Allison Jones-Farmer and Timothy C. Krehbiel,
  • Columns by and Bradley Jones, Lloyd S. Nelson, Gordon Clark, Jack B. ReVelle, Laura Freeman, and Mark Johnson
Read it here now!
Mini-Paper: Big Data Terminology: Key to Predictive Analytics Success
Key Words:
big data, business intelligence, and predictive analytics
Abstract:
With all of the hype surrounding big data, business intelligence, and predictive analytics (with the statistics stepchild lurking in the background), quality managers and engineers who wish to get involved in the area may be quickly dismayed by the terminology in use by the various participants. Singular concepts may have multiple names depending on the discipline or problem origin (business analytics, machine learning, neural networks, nonlinear regression, artificial intelligence, and so forth). Hence, there is a pressing need to develop a coherent and comprehensive standardized vocabulary. Subcommittee One of ISO TC69 is currently developing such a terminology standard to reside in the ISO 3534 series. In addition to the technical statistical-type terms, it could also include a discussion of some of the software facilities in use in dealing with massive data sets (HADOOP, Tableau, etc.). A benefit of this future standard is to shorten the learning curve for a big data hopeful. This paper describes the initial steps in addressing the terminology challenges with big data and offers some descriptions of forthcoming products to assist practitioners eager to plunge into this area.
 Feature: Agile Teams: A Look at Agile Project Management Methods
Key Words:
agile, scrum, project managment
Abstract:
This article presents a discussion of agile project management including scrum methodology. We see tremendous value that can be gained by the use of agile methods along with existing project management frameworks. Although agile lacks a systems focus, the agile principles apply directly to managing smaller projects within enterprise-level initiatives. Analytics and data science projects are often exploratory in nature, require cross-functional teams to work together, and the scope is often developed through team discovery. Thus, we see agile methods as particularly suited to moving analytics and data science projects forward, preventing backlogs and roadblocks that can occur due to uncertainty and poor communication.

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