Data consistency might not have anything to do with quality management. However, because quality management is getting more and more data-driven it is becoming increasingly important. Consistent data allows for easier and better analysis, which leads to more accurate and suitable improvement plans. In the new quality management era, data consistency is just as important as anything else you do.

Force Fields

One way to improve the quality of the data is by making certain questions a requirement. Employees need to answer the questions before they continue. The approach guarantees information but it doesn’t guarantee usable information. Some employees will simply key in some data to check the requirement, which will not help from an analytical point of view. It is important to find a balance in the number of required fields. This is something that can be explored by trial and error in the field—simply make some fields required and see what happens.

Selection Fields

Predefined selections are a great way to keep the data consistent. Employees have to pick one of the selections. When a selection field is used, make sure the different options are clear and self-explanatory. Options that are too complex will confuse the employees and reduce the value of the data coming out. Furthermore, try to prevent options like “other”, “general”, etc. These options are basically a trash bin for undefined situations. Providing this option makes people lazy and could very well become the most used option. The data will not be usable when 20–30% of the answers are one of these options because of the lack of context that is required.

Connecting Solutions

The best strategy to keep data consistent is by integrating your IT solutions. When data is consistent over the different solutions, you can really start identifying trends throughout the company—not just in quality management. When connecting the supplier issues directly to the suppliers in your ERP system, it is possible to instantly grade the suppliers. This connection also allows for benchmarking locations or product lines. In order to do this the data needs to be unambiguous, hence the integration.

Connecting solutions is key when you want to keep the data consistent over different solutions. Make sure you select platforms that are open and allow you to connect to other solutions when you pick your new partner.

Data Cleaning

Despite all the good efforts, things can still get messy. In case this happens it is important that there are options to clean the data. Your solution should be able to allow for data cleaning either manually or automated. This option allows you to keep the consistency in the data and keep on improving processes with accurate data.


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