The advent of new analytical methods like neural networks and bootstrapped forests has unlocked a range of exciting new possibilities when it comes to large data sets. Implementing these advanced methods, however, is not always as easy as it would seem. And that's where JMP can help.
In this whitepaper, statistician Roger W. Hoerl argues that JMP and JMP Pro provide workflows that help users avoid big data mishaps by getting back to the fundamentals of data quality. Beginning with an outline of several more commonly used methods, Hoerl then makes a case for integrating data quality checks in the analytical process. To assist with this synthesis, he identifies the core elements of a data pedigree and outlines a step-by-step holistic approach that applies traditional quality concepts to modern big data applications.