Insights, tips and 11 quality criteria for the correct use of your data
In the increasingly important step of digitizing business processes, more and more companies are introducing a CRM system that is networked with the IT landscape. This leads to faster processes, cost savings and improved data management. This point is very important, because any activity and analysis in CRM is only as good as the quality of its data. Unfortunately, the use of CRM software is not the end of the story: there are a few more aspects to consider. Read here what actually constitutes good data quality and how you can achieve it.
Table of contents
What is data quality in CRM and why is it important?
Data quality indicates the extent to which your data is suitable for its intended use. This is also referred to as "fitness for use". As you can see from this definition, the assessment of data quality depends on the context. A data set may be perfectly suitable for one purpose, but not for another.
Data quality is particularly important where data is used as a resource. This includes in your CRM system, butalso in networked systems. In your company, this could be the following areas:
- Comprehensive analyses à la business analytics
- business intelligence
- Dashboards and reports
- 360° view of your customers
- Newsletter dispatch
- production processes
- Invoicing
are dependent on underlying data collections in the age of digitalization. Poor data quality can lead to costly consequences.
As the saying goes "garbage in, garbage out", we can assume that the best algorithm won't do you much good if you don't feed it with the data it needs. If you do so anyway because too little attention is paid to data quality, this will result inanalysis and process errors. The longer these go unnoticed and are perhaps even passed on, the greater thenegative consequences.
These can ultimately manifest themselves in very different ways. Bad decisions may be madeon the basis of incorrect analysis results. Processes may run incorrectly or incorrectly contacted customers may evensue you because you should no longer be in the system. According to a study by the MIT Sloan Management Review, companies lose around 15-25% of their revenue due topoor data quality. And your IT will almost certainly need some extra work to iron out any errors.
11 criteria for measuring your data quality
Sources of error that reduce your data quality
After looking at the criteria for data quality, you can probably imagine: There are many sources of error in data management. The most common errors occur duringdata entry by employees. This is particularly the case whenseveral departments are involvedthat havedifferent procedures orexpectations of the data analysis. Errors also often occur whenchanging systems ormerging data from different sources.
Such errors are annoying and lead to unreliable results. Last but not least, you will be annoyed by the costs incurred. The longer errors are passed on unnoticed, the higher the costs for subsequent correction, re-evaluation etc. will be.
Avoid errors before they are made
Regular data cleansing isusually necessary, but you save resources with every error that does not occur. It is therefore worth taking measures to prevent errors before they occur.
Here are a few examples:
- System integration & interfaces
The comprehensive integration of your IT landscape is an important factor. We are talking about the single source of truth (or single point of truth):a sourcefrom which theright datais obtained - whether it is your CRM system or the ERP. Nowadays, it is not even that complicated to integrate systems that are actively exchanging data or to link them with each other via interfaces. In this way, data exchange takes place automatically and is much less prone to errors. - Mandatory fields
Define mandatory fields in your input masks. This ensures that the information you need for your data processing is always available. You have already ticked off the completeness criterion. - Automatic check/filling
In many places, it is possible to ensure that the correct format or the correct information is entered in the field by means of automatic checks or filling during input.
Tip: Last but not least,drop-down menus ininput masks are a good way ofrestricting aselection andavoiding typing errors.
What is data quality management?
The measures mentioned above are already part of data quality management, which is responsible for the strategic management of data and the sustainable assurance of data quality in CRM. For data errors that nevertheless creep in from time to time or arise over time, there are a few additional tasks.
How high does your data quality in CRM need to be?
Despite the great importance of data quality, it can be said quite generally that it is almost impossible to achieve 100% data quality in CRM. In addition, the required data quality always depends on the application context and the existing errors. However, if you are really aware of this and take the appropriate measures to heart, you can be confident that your data quality in CRM will be sufficient for your purpose. Because then you can define what data you need, what purpose it will serve and therefore what errors you need to exclude. Once this has been done, your data quality should provide you with the desired reliable and meaningful results from theCRM software.fromthe CRM software.
Conclusion
You can certainly now confirm that you need more data quality in your CRM. Always keep in mind that you need to be clear about the goals of your data processing and the possible errors in order to ensure the high quality of your data. Also, never forget that this isanongoing task andalways keep an eye on your data quality in CRM.
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