Data quality - the basis of all analysis.
4 Nov

Data quality - the basis of all analysis

Much is said today about BIG DATA and the resulting opportunities. New opportunities are emerging for small and medium-sized enterprises to analyse their market more precisely and to position their offer better. The speed at which data can be processed is ever increasing. Thanks to technologies like SAPHANA real quantum leaps can be achieved here. Of course, this also applies to SAP Business One. However, there is one prerequisite: the quality of the data!

However, much does not always help much here. If the quality of the data is not good enough, the results cannot be expected to be meaningful.

What makes the quality of the data

Generally speaking: Data quality is when the suitability of multiple data sets are related to determine commonalities and/or discrepancies. Quality aspects include whether the data sets are accurate, complete, up-to-date and relevant.
It is also important that the data is accessible in the same way and retains its consistency.

In practice, this means that one cannot avoid checking one's own database for weaknesses, cleaning it up and/or completing it. Duplicates have to be eliminated, formats standardised and rules updated.

An almost imperative time when the quality of the data should be scrutinised in general terms is the introduction of a business software such as SAP Business One. The structure of the data maintained in a previous system most probably does not correspond to that of the successor system. This means that the data must first be brought into the new structure, usually outside the system, and at the same time checked for quality. A process that is underestimated in many ERP projects. At the same time, one should think carefully about what one wants to learn from the new data in the future. There is by no means only one way to Rome.

Constant maintenance for the quality of the data

Once the data is in the new system, this is often the stage from which things only go downhill. The reasons for this can be a lack of user training. However, it is often not clear who is responsible for the quality of the data in the first place. The latter often leads to uncontrolled growth through more or less arbitrary changes or extensions. Here, too, the rule is: much does not help much. Among other things, creating a new field is no longer a big effort in applications like SAP Business One. However, one should carefully consider whether this is really necessary.

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