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Chapter 9.1: Repetitive Analytics: Some Basics
Chapter 9.1

Repetitive Analytics: Some Basics

Abstract

There are many facets to the analysis of repetitive data. One type of data where
repetitive data are found is in an open-ended continuous system. Another place where
repetitive analytics is done is in a project-based environment. A common practice for
analytics in repetitive analytics is that of looking for patterns. One issue that always

occurs with repetitive pattern analysis is the occurrence of false positives. A useful
approach for doing repetitive analytics is to create what is known as the “sandbox.”
Analysis in the sandbox does not go outside of the corporation. On the other hand, the
analyst is not constrained with regard to the analysis that is done or what data can be
analyzed. Log tapes often provide a basis for repetitive data analytics.

Keywords

Repetitive data; Open-ended continuous system; Project-based system; Pattern analysis;
Outliers; False positives; The “sandbox”; Log tapes

There are some basic concepts and practices regarding analytics that are pretty much
universal. These practices and concepts apply to repetitive analytics and are essential for
the data scientist.

Different Kinds of Analysis

There are two distinct types of analysis—open-ended continuous analysis and project-
based analysis. Open-ended continuous analysis is analysis that is typically found in the
structured corporate world but is occasionally found in the repetitive data world. In open-
ended continuous analysis, the analysis starts with the gathering of data. Once the data
are gathered, the next step is to refine the data and analyze the data. After the data are
analyzed, someone's decision or a set of decisions are made, and the results of those
decisions affect the world. Then, more raw data are gathered, and the process starts over
again.

The process of gathering data, refining it, analyzing it, and then making decisions based
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