7 tips to optimise data

After the summer break, September also marks the start of a "new" year in many companies. And what better way to start afresh than with a proper tidy-up? In digitalised companies that work on the basis of software, the process of Chaos today more on the servers than in the filing cabinets. But how do you tidy up digitally? Seven tips on how to optimise your data maintenance and, in the process, save Quality of the data.

1. make data optimisation possible through uniform processes

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First, you should check the data entry processes. If, for example, data are filed in different areas in the system so that they are not (can not) be automatically related, the processes should be corrected here. Software that supports most Processes unified within the company, this simplifies things considerably. Only on this basis does it make sense to clean up the data stocks, otherwise a Sisyphean task awaits you.

2. make completeness testable

When is a data set complete? Depending on which processes rely on the data, this question can be answered differently. For example, does the data set for a product need a picture? Once these criteria are defined, the completeness of a data set can be assessed against them and the data can thus be checked for deficiencies.

3. check condition

... and this is the next step: Checking the data for its condition. Prefabricated Analysis tools can provide help here. In this way, faulty or redundant data in particular come to light and can be corrected or archived.

4. archive before

Which brings us to the next step. Often parts of the database have become old or unnecessary. However, these data cannot simply be deleted due to the obligations to provide evidence, but must be archived. This is an important step, as outdated or superfluous data can hinder the processes in the system.

5. automation is data optimisation

Since it is now possible to use various Workflows To automate processes, the data records must be reliable in order to ensure an error-free process flow. Especially at those points where the automated processes take effect, the criteria for data maintenance should be clearly defined in order to make the advantages of automating the processes usable.

6. maintenance of the data on the agenda

All established procedures for data maintenance are of little use if the "data maintainers" concerned do not apply them. You should therefore use the last few hours of the summer break to familiarise your employees with the new Data maintenance-criteria. This also underlines the importance of data maintenance and makes colleagues responsible for it.

7. ... and from the front

The last step and tip is called repetition! Both the data cleaning and the control of the criteria for recording and maintenance should be checked regularly. The reward is not only tidiness, but also faster and simpler processes in the company.

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