Syncler Use Cases
Improve Data Quality: The Importance of DQM for Companies
Every company uses various software applications to process or store data. This often results in the challenge that there is no or too little data exchange between the systems - this is referred to as isolated solutions. Data exchange can be implemented via individual interfaces or integration platforms. The aim is to capture all of the company's data and make it visible. In order to generate as much added value as possible from this information, it is necessary to work with high-quality data.
The topics at a glance:
- What is data quality management?
- The importance of DQM
- Consequences of poor data quality
- How does DQM work?
- Dimensions of data quality
- Conclusion
What is data quality management?
Data quality management (DQM) encompasses all measures that ensure that a company's data is always correct, relevant and reliable. Data quality depends on the purpose that the data is intended to fulfill, which can vary depending on the company and area of application. DQM continuously monitors the quality of the data. The aim is to manage and maintain the data as a valuable resource and make it available in high quality on a permanent basis.
The importance of DQM
These are some of the most important reasons why DQM is essential for companies:
- Accuracy and reliability:
Calculations are made and decisions are taken on the basis of available data. Incorrect or inaccurate data leads to incorrect results. - Efficiency:
Poor data quality leads to increased effort in error correction and data cleansing. - User-friendliness:
Users expect software applications to provide accurate and up-to-date information. All data should be centralized and easy to find. - Security:
Incomplete or incorrect data can also pose security risks. For example, incorrect user data can create vulnerabilities in the software. - Regulatory requirements:
Some industries have strict regulations regarding data management and security. DQM ensures that these requirements are met.
Consequences of poor data quality
Poor data quality brings with it many challenges, such as incomplete or outdated information, duplicate records and inconsistent formats. The consequences are far-reaching: wrong decisions and inefficient processes impact business performance, while negative customer experiences diminish trust. Furthermore, companies can expect higher IT costs and missed business opportunities if data quality is not systematically improved.
How does DQM work?
The approach to improving data quality is based on a clearly structured model. At the beginning, modeling is used to determine what data there is and what it should look like. For example, a zip code should have five characters. Profiling compares the actual state with the target state in order to identify data that does not meet the requirements. The results are then analyzed. What measures need to be taken to improve data quality? The next step is to prepare the data. A decision is made manually or automatically as to which data record should remain or be deleted. One example of this is the duplicate check, in which algorithms help to identify and merge duplicate data records. Finally, the cleansed and optimized data is transferred to the target system.
Dimensions of data quality
Effective DQM starts at several points in the data evaluation process and helps to identify and correct errors. It pursues the goal of increasing data quality by checking for:
- Completeness:
Are all relevant fields of data set filled in? - Accuracy:
Is the information correct and does it correspond to reality (e.g. correct address)? - Validity:
Is the data plausible and does it correspond to a business rule or template (e.g. spelling of an e-mail, zip code or country code)? - Availability:
Is the data available to the processor (e.g. data on the process from different peripheral systems)?
Conclusion
Data quality management is indispensable for companies. It creates the basis for well-founded decisions and efficient processes. Especially when integrating different systems, it is crucial to work with high-quality data. This enables companies to extract the full added value from their data.
With Syncler's new DQM module, we will be able to do both in the future: seamlessly integrate your systems and improve data quality. In this way, we ensure that you get the maximum added value from your data. Stay tuned - the module will be available soon and we will inform you about the new functionalities in detail shortly.