Whatever be the industry or the nature of the business, different quality management tools and programs that range from lean, six sigma, Kaizen, ISO. They have something universally common and that is their dependency on data. That is their fundamental dependency.
Even though the approaches and the techniques, time span and other details of these programs may have a different perspective but the fundamental dependence on data will remain the ultimate authority when one talks about making the right decisions and taking the appropriate actions.
There are invariable two data types that are seen to be applied in most of the business improvement initiatives. Data that is collected with this perspective in mind is either the one that falls in the category of absolute data or continual data. They are also called as attribute or variable data respectively.
The category of data which is the absolute data is obtained by the means of measuring a characteristic which can be actually counted numerically. On the other side of the continuum, variable or continual data deals with measuring those characteristics that do not change abruptly and change gradually over a span of time.
Only when the researcher has a clear understanding about the difference that is there in the two sets of data, can he understand exactly what type is to be measured.
There can be some simple rules to a good data. These rules help to overcome the anxiety and overwhelming feeling of the researcher upon seeing the data spreadsheets. If these rules are kept in mind, it becomes easier to create a good effect.
The data should be:
- Relevant: In context to the process or system we are trying to improve
- Reliable: When one talks about the collection, recording and accuracy
- Representative: A clear and crisp representation of the situation that the researcher is examining or trying t improve
- Readable: It means it should be clear, understandable and usable
