Data Collection and Measurement in Six Sigma

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 measure those processes with the appropriate data. The crucial steps of data collection and measurement precede process improvement in any Six Sigma initiative. Successful data collection starts with careful planning; a knowledge of various data types, sampling strategies, and measurement methods; and an ongoing awareness of best practices for ensuring data accuracy and integrity.

Only reliable and suitable data will yield dependable analyses that translate into desired process improvements. As Six Sigma team leaders, Black Belts will help to oversee careful data collection efforts during the Measure phase of the Six Sigma DMAIC process. They will determine what should be measured, how data should be collected, and what tools can be employed to gather data as the basis for further improvements. This course prepares Black Belts for successful data collection by surveying the types of data, measurement scales, sampling methods, and collection techniques available.

It offers guidance for ensuring data integrity, pointing to different collecting methods for different informational needs, and recommending best practices for front-line data collectors. It compares the relative advantages of both manual and automated data collection, and surveys the wide variety of tools available for measuring the properties of an organization's products or services. 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 SkillSoft's ASQ-aligned Green Belt curriculum.

• Determine what type of data to collect in a given scenario.
• Recognize how to convert data into a different data type.
• Match measurement scales to associated statistical analysis tools.
• Recognize the use of best practices for ensuring data accuracy and integrity in data collection.
• Match sampling methods with applications suitable to their use.
• Match sampling methods with applications suitable to their use.
• Identify the advantages of automated data collection.
• Sequence the steps in the data mining process.
• Match measurement tool categories to descriptions.
• Recognize an example of the correct application of the rule of ten.