Online Web Research Services & Data Research, Data mining

Collecting data for analysis is more than a statistical process. All of the math in the world will not compensate for not understanding the behavior of the process you are trying to measure. Not everything is settled in numbers. Some things will be discovered in context. For example, "We really have problems when it is raining."

As a result, data collection plans embody four qualities of collected data that are essential to optimize its usefulness. These qualities have to do with the data's ability to represent the process' performance.

* There must be sufficient data to see the process' behavior.
* The data must be relevant.
* The data must be representative of the process' normal operating conditions.
* The data must be contextual.

There must be sufficient observations to see patterns of variation and shifting central tendency in the process' output. As part of building a data collection plan, the team will seek to understand the process' history so that all expected sources of variation are captured.

Consideration must also be give to the size of the performance gap that the team is trying to measure. As the size of the gap gets smaller, the number of samples needed to measure the gap, with statistical confidence, increases.

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