The quality of master and transaction data influences, among other things, how efficient processes work and how successful a company is at the end of the day. But especially with digitization, the challenges in data maintenance are increasing significantly-the susceptibility to errors is increasing. […]
A recent study by the VDMA proves: Almost ” 84 percent of survey participants estimate the […] Effort to enter, search and maintain data as high a.”In the same breath, 34 percent of respondents complain about missing or low – quality prospect and customer master data – a decisive challenge, especially for sales.
Thus, the topics of data quality and IT solutions for optimized data quality are becoming more and more in focus. Because with an individual strategy, costly mistakes can be avoided on the one hand, and on the other hand, confidence in one’s own data increases. All together forms the ideal basis for making better decisions. proALPHA explains how companies design their way to more data quality in 6 steps:
1. Filter out success-critical processes
First of all, companies should understand in which processes incorrect or incomplete data is particularly important. These are the areas in which the fastest added value can be achieved with higher data quality. For example, delivery risks can be minimized through well-maintained replenishment times, supplier addresses and conditions. The correct transfer of part data into the individual work orders can also significantly reduce costs and additional expenses.
In the course of a first analysis, it must also be checked whether all departments have quick access to relevant information at any time and regardless of location.
2. Define quality criteria
Depending on the company and department, the criteria for high data quality can look very different. In particular, differentiation should be made according to different types: Movement data, for example, place different requirements on the necessary information than master data. It is also important to distinguish between customer and prospect data. The question here is: Do I need an extensive data set from a potential customer already at the first contact – or are the name and telephone number of the contact person initially sufficient?
3. Check existing data pools
To control existing data sets, it is important to use various test criteria. The obvious ones include, for example, completeness and correctness. But other aspects can also be taken into account in the evaluation: For example, are the respective archiving times for documents adhered to? And does the company comply with the deletion obligations for information that is no longer needed?
An accurate analysis and the consistent cleaning of their own databases allows companies to act more efficiently in success-critical processes. It also strengthens the company’s compliance – both internally and externally.
4. Eliminate duplicates
When it comes to quality, the data itself is often the key weakness. For automated processes and efficient processes, up-to-date, clear and, above all, complete information is required. Duplicates are a common problem during testing. They unnecessarily increase the amount of data, reduce efficiency and increase the risk of misinterpretation. That is why it is important to eliminate them and stop creating new ones in the future.
5. Generate unique data
Double data storage in several independent systems is common practice. But this entails various disadvantages: On the one hand, the manual transfer into the other program means a high additional effort – on the other hand, inconsistencies and contradictory data sets can arise. With modern integration techniques and professional testing software (Data Quality Manager), such errors can be avoided in a targeted manner.
6. Maintain and check data continuously
There is no end date for the Data Quality project. This is because quotation and order information, as well as serial and batch numbers of parts, must be constantly maintained. This is the only way to improve the quality of information in the long term. Various tools are available for this purpose: regular automated quality controls, plausibility checks, workflows, data cleansing and defined rules for newly collected data.