Insights Press Release BI & Data

Data Quality on the Rise – the Basis for your Company's Success

Reading Time: 1 Minutes 27.07.2021 Currents & Trends

How to develop your strategy for high data quality

At the end of the day, the quality of master files and transaction data influences the efficiency of processes and the success of a company. However, the challenges in updating data are significantly increasing with digitalization because errors are more likely to occur. We have compiled 6 steps to help you continuously improve your data quality in the context of automation.

A recent survey by the VDMA (German Mechanical Engineering Industry Association) shows that almost "84% of the survey participants rate the [...] effort for entering, searching and maintaining data as high . At the same time, 34% of the participants complain about missing or low-quality prospect and customer master files – a crucial challenge, especially for sales.

Thus, the focus is on data quality and related IT solutions. On the one hand, an individual strategy helps to avoid costly mistakes, and on the other hand, it increases confidence in the company's own data. All this combined forms the ideal basis for making better decisions.

Learn how to improve your data quality in 6 steps:

Identify the key processes

First, you should identify the business processes in which erroneous or incomplete data are particularly severe. These are the areas where increased data quality provides the fastest added value. For example, you can minimize delivery risks by updating replenishment lead times, supplier addresses, and conditions. The correct transfer of part data to the individual work orders can significantly reduce costs and extra workload.

As part of an initial analysis, you should also check whether all departments have fast access to relevant information, anytime and anywhere.
Define your quality criteria

The criteria for high data quality can look very different depending on the company and department. You should distinguish between different types: transaction data, for example, require different information than master files. It is also important to differentiate between customer and prospect data. Ask yourself: do you need an extensive record from a potential customer upon first contact – or will the customer's name and phone number be sufficient for now?
Check existing data pools

In order to control your existing records, it is important to consider different criteria. The most obvious ones are, for example, completeness and accuracy. The evaluation may also include further aspects. For instance, are you complying with the respective archiving times for your documents? Are you attending to your obligation to delete information that is no longer needed?

A precise analysis and consistent cleansing of your databases enhance your efficiency in crucial processes. This way, you also strengthen your company's compliance – both internally and externally.
Remove duplicates

When it comes to quality, the data itself is often the actual weak point. This is because automated processes and efficient workflows require up-to-date, unambiguous and, most importantly, complete information. Duplicates are a common problem during checks. They unnecessarily increase the data volume and the risk of misinterpretations, and also reduce efficiency. Hence, it is important to eliminate them and to avoid creating more in the future.
Create unambiguous data

Double data management in several independent systems is common practice. It entails various disadvantages, though. On the one hand, manual transfers to the other program mean a lot of extra workload; on the other hand, it can lead to inconsistencies and contradictory records. Modern integration techniques and verification software (Data Quality Manager) help you get a grip on such errors.
Update and check your data continuously

The data quality project is a never-ending story. Quote and purchase order information as well as serial and lot numbers must be continuously updated. This is the only way to improve information quality in the long term. A number of methods are available for this purpose: regular automated quality checks, plausibility checks, workflows, data cleansing, and defined rules for newly entered data.

Implement these 6 steps in your company and you will surely improve your data quality. Download the checklist by proALPHA now!