Checklist for more data quality: Get started with clean data
The economy in German-speaking countries is once again more optimistic about the future: In May, as in June, the Ifo Business Climate Index recorded a significant increase. What do companies need to really step on the gas now? That's right: more data liquidity. A checklist shows SMEs how to do this.
The first half of 2020 has made it abundantly clear: The digitization of business processes makes companies more resilient. The fuel for the digital future is data. In most companies, however, the topic of data quality is extremely unpopular because many doubt the sustainability of a data inspection. Not entirely without reason. The software provider proALPHA advises companies to ask themselves these four central questions:
In which processes does data have a significant influence on productivity?
Not every cog in the big wheel of a company is equally important. It is therefore important to identify those processes in which incorrect or incomplete data are critical to success. After all, inadequate data can lead to a great deal of additional work and thus high costs, for example through the incorrect transfer of parts data from the parts list to the work orders. Or they increase the delivery risk because it is recognized much too late that a customer has ordered not 100 but 1,000 pieces and now the necessary material is not in stock. Part of this initial analysis should also be the question of whether all areas have quick access to the information relevant to them at any time and from anywhere.
2. what is a good data set for us?
The next step is to define quality criteria - tailored to the company and the respective department. It is not only necessary to differentiate between transaction and master data. Even the requirements for customer information and prospect data can differ significantly. For example, communication with customers may require an e-mail address in order to notify them quickly in the event of a callback. For prospects, on the other hand, this does not apply. The ERP manufacturer proALPHA therefore advises companies to give sufficient thought to this point. This is the only way they can be sure not to overlook any important quality aspect.
3. where does the data quality currently leave something to be desired?
Then it's down to the nitty-gritty: The existing data pools should not only be examined for obvious criteria such as completeness and accuracy. There are numerous other aspects to be checked, such as compliance with archiving or deletion obligations. If you analyze these aspects in detail and clean them up consistently, you will directly increase the efficiency of mission-critical processes - and strengthen compliance.
4. how can more data quality be ensured in the long term?
A one-time data tuning is not enough. Both master data and transaction data change continuously. This starts with serial and batch numbers of parts and ends with quotation and order information. Ongoing, preferably automated checks, plausibility checks and workflows help to maintain the quality standard that has been painstakingly achieved. Or even to expand it further.
Checklist for medium-sized businesses
With these four questions, companies can tackle the unpopular topic of data quality in a structured and focused manner. proALPHA, the ERP manufacturer, has developed a practical checklist to get an even better grip on the issue. It includes the 30 most frequently asked questions for clean data. The checklist is here available for download free of charge.
Source and further information: proALPHA
