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An organization's success depends upon how it delivers on its processes. Before Black Belts can begin to improve an organization's processes, they must collect data to measure current processes using appropriate methods and tools. Successful data collection starts with careful planning and a knowledge of various data types, measurement methods, and sampling techniques. Black Belts also need to be aware of best practices for ensuring data accuracy and integrity. As Six Sigma team leaders, Black Belts help to oversee careful data collection efforts during the Measure phase of the Six Sigma DMAIC process.
This course prepares Black Belts for successful data collection by surveying the types of data, measurement methods, and scales; sampling techniques; and collection methods available. It offers guidance for ensuring data integrity, pointing to different collection methods for different informational needs, and recommending best practices for front-line data collectors. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in 's ASQ-aligned Green Belt curriculum.

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Objectives

Data Types, Measurement Methods, and Scales determine what type of data to collect in a given scenario match measurement tool categories to descriptions recognize an example of the correct application of the rule of ten match measurement scales to associated statistical analysis tools Sampling, Data Collection Plans, and Methods match sampling methods with applications suitable to their use recognize appropriate applications of subgroup and block sampling label types of measurement system studies according to whether they test accuracy or precision recognize the use of best practices for ensuring data accuracy and integrity in data collection recognize the use of best practices for ensuring data accuracy and integrity in data collection sequence the steps in a process for cleaning data identify the advantages of automated data collection sequence the steps in the data mining process