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Workshop Qooling: How to use data as input to improve my business.

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Workshop Qooling

Last week we hosted a great workshop by Tessa Lange. She is an expert in continuous improvement and shows the participants how to use data as an input to improve business. Some key take-aways will be pointed out in this post.

Why Collect Data?

Lots of companies collect a ton of data without any valid reasons. Data is often described as the new gold and therefore companies just collect tons of it, sometimes without reason. The downside of this is that somebody needs to create that data. In a company setting, this means that the employees need to key in the data into some sort of system. Creating this data costs time and effort by the employees. Hence, collecting the data without any purpose is a huge waste of valuable time and effort. Also, when people need to fill in a ton of forms and they don’t see the benefits of this, they will either get very frustrated or simply stop filling in the data.

With this knowledge, it is even more important to think twice about why you collect certain data. Of course, there can be very good reasons for collecting the data like laws, certain standards or maybe to improve the company. Just think about the reason for every point of data that you are collecting.

How Do I Rate the Data?

When the data is collected, it is important to rate the data and check its reliability and validity.

  • Reliability = If you measured it again, would the outcome be the same?
  • Validity = Do you really measure what you want to measure?

To be on the same page with your employees, it is important set certain norms on what data is right and what data is wrong. Data is often used as a yardstick to measure performance and in order to do this sensibly, you need to know if the data is usable.

Interpreted the Data

Interpreting data isn’t as easy as it may sound. To draw conclusions out of data you need to understand the context. Context can be the unit of the data point or the relation of the data to data in a previous period or even a benchmark you work against. Simply concluding if the company is doing a good or bad job based on the data doesn’t make sense. Make sure you have context. When the context is clear and you are measuring the same data over and over again, you can check for anomalies in the data. Look for spikes or drops in the data, or points that break the pattern.

Start Improvement

When all the data is collected and you start to see trends that aren’t according to the plan, you can dive into these trends to see what goes wrong. When you really start to see a negative trend, you can start an improvement process to try to break the trend. This can be relatively easy, just for example, by talking with suppliers when they make too many mistakes. On the other hand, these plans can be pretty elaborate like complete safety awareness programs. The results of these improvements should be measured on an acceptable timeline. You cannot expect to see safety changes in a couple of weeks. While improvements from a supplier should be seen relatively quickly.

Conclusion

The most important lesson is “context”. Always have a context before you analyze the data. Before you know it, you are improving the wrong processes.

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