The data collection methods that you use lay the foundation for the eventual success of your continuous improvement projects.
Simply put, data are the facts of the case. Raw data is then complied and processed into useful information that helps gain more insight into whatever you are trying to learn about. (Note: Data is plural. Datum is the singular form.)
Data collection is the process of getting the data from the real world to some form where you can manipulate it to get information. This information is then used to make decisions, the ultimate goal of data collection.
There are many choices you’ll have to make when collecting data. Will you be sampling or doing 100% data collection? Will the data be self-reported, or will it be collected by an external observer? Will you be eyeballing the measurements or will it require precise measuring devices?
The choices you make about your data collection plan will be influenced by how you intend to use the data. All data should be collected with a purpose in mind. The planned use of your data will shed light on what you need to gather.
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Data is extremely important to continuous improvement. It takes the emotion out of decisions, and peels back the cloud of misperception. The results of a data collection effort can stun a person when it doesn’t match what the person believes to be true. It is surprising how little people really understand about their environment.
I suspect that there are a few reasons for this. First, people tend to think they know more than they really do. Because they are confident in what they believe to be true, they see no point in spending the time and effort to collect data.
As a result, people routinely skip data collection because it takes time, and is hard to do properly. While the actual methods of data collection may be simple (i.e. check sheets), the planning, compilation, and use of this data takes time. And if they do collect it, despite the time it takes, data frequently sits unused.
Make sure to take the time to create a data collection plan. It will minimize the impact on production as well as increase the likelihood that the data will be used.
Data falls into several main categories. The first is whether it is qualitative or quantitative in nature.
In truth, the distinction is actually one more of choice than of the actual nature of the condition you want to evaluate. Most conditions can be measured quantitatively. Color, for example, has a wavelength that is numerical. Emotion has blood pressure changes, skin temperature, perspiration rate, voice pitch, etc. Even gender can be quantified as the number of ‘X’ chromosomes a person possesses.
The challenge is that all of these things are difficult, often prohibitively so, to get numbers for. When that is the case, qualitative data is a reasonable compromise between costly, intrusive data collection and no data at all.
Quantitative data (occasionally also called variable data) falls into two main categories. Continuous data is able to be subdivided infinitely. A person’s height falls into this category. You can record any value that you measure. A person’s height can be recorded as 71.673452 inches, with a sensitive enough measuring device.
Discrete data, on the other hand, is still numerical, but can only take on values from a limited set. The number of wheels on a vehicle, or the number of bad parts in a lot both offer limited options to choose from. The bad parts data is limited to whole, positive numbers less than or equal to the size of the lot. For the vehicle example, the choices can only be 1 (unicycle), 2 (motorcycle or bike), 3 (three-wheeler), and so on up to an 18-wheeler.
Qualitative data has a few more categories than quantitative date. Attribute data is not quantifiable—color, pass/fail, etc. Again, keep in mind that attribute data, in many cases, comes from something that could be measured numerically. For example, ‘Red’ is really a range of wavelengths along the spectrum of visible light. If we want more flexibility down the road, we could measure the wavelength, but that has an added cost to it.
Data collection can feel like a burden because it is done in addition to the rest of your workload. The purpose, though, is to make the operation run more smoothly. If the data is never used, your frustration is certainly warranted, but I urge you to look at data collection as a sign that your leadership team is taking a proactive step to solving the problems that plague you.
In addition to the benefit of leading to problem resolution, data collection also helps manage your relationship with your boss. One of the challenges leaders and teams face is when they assess a situation differently. Data collection helps take the ambiguity out of a situation. Rather than debate knowable facts, they discussion shifts to the heart of the matter. Countless disagreements between management and workers could be avoided if they shared the same understanding of a situation.
Most managers can become markedly better by using more data in their decision making. That’s not to say that experience and ‘gut feel’ has no place in being a leader, but all too often managers make quick decisions with limited or no information.
Sometimes they get it right, but they could be more correct more often if their decisions were grounded in fact. Weigh the risk and the potential benefit of a decision with the effort to collect a bit of data before taking action. In more cases than not, you’ll see an improvement in the results of your problem solving efforts.
Be careful to keep data collection from straining your relationship with your team. Done right, it can dramatically improve how you get along. Done wrong, it can set you back substantially. The most common problems are overdoing data collection, not using data that the teams work to collect, measuring individuals, and using data as a weapon against the team.
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