Completeness check vs. workflow - when does which make sense?

In the modern data-driven world, efficient data quality management (DQM) is essential to ensure that data is reliable, accurate and usable. Two key strategies in DQM that are often compared are the completeness check and the workflow-based approach. Both have their own strengths and specific areas of application. In this blog post, we take a look at when which method makes sense.

What is the completeness check?

The completeness check in DQM focuses on whether all required data fields are filled and available. It checks whether data records contain all the necessary information for further processing or analysis. This method provides a snapshot of data quality and enables gaps or incomplete entries to be identified quickly.

When is a completeness check useful?

Fast data validation
When companies have to process a large amount of data in a very short time, the completeness check is particularly useful. It helps to identify missing information immediately and ensures that no data records are inadvertently transferred to the next phase of a process.

Simple business processes
In areas where the data requirements are clear and relatively static - e.g. when entering customer information into a CRM system - a completeness check is often sufficient to ensure data quality.

Standardized reports and analyses
For standardized reporting processes where certain fields are always required, the completeness check is key. Example: If you maintain a database for financial reports, all required fields such as account number, date and amount must be present to ensure correct analyses.

Data migration
When transferring data between different systems or databases, the completeness check helps to ensure that all necessary fields have been migrated correctly. This is less about the accuracy of the content and more about checking the structure and the presence of all data elements.

Limits of the completeness check

While the completeness check is indispensable, it also has its limits. It only checks whether data is present, but not whether it is correct or meaningful. A fully completed field could contain incorrect or inappropriate information, which could lead to further problems.

What is a workflow-based approach?

In contrast to a completeness check, a workflow-based approach focuses on the entire life cycle of the data within a process. Not only is the data itself checked, but also the steps through which it is processed. A workflow approach looks at the data in its context and ensures that it takes the right path through all relevant processing steps.

When does a workflow make sense?

Complex business processes
If companies have complex business processes in which data flows through several phases and systems (e.g. supply chain management or enterprise resource planning), the workflow approach is indispensable. Here, it must be ensured that data is processed consistently and correctly and that no steps are omitted.

Quality assurance throughout the entire life cycle
In processes that involve several departments or external partners, the workflow ensures that data is validated at every stage of the process and corrected if necessary. This prevents errors from accumulating or causing problems in later phases.

Automated processes and machine learning
In a data-intensive environment, such as automated machine learning processes, it is crucial that data runs through predefined workflows. Here, validations must be performed not only for completeness, but also for accuracy, consistency and redundancy. A workflow can ensure that data that does not meet certain criteria is automatically sorted out or corrected.

Regulatory compliance
In regulated industries such as finance or healthcare, companies must ensure that data is not only processed correctly, but also in accordance with certain regulations. A workflow makes it possible to monitor all data processing steps and ensure that they comply with regulatory requirements.

Limitations of the workflow-based approach

A workflow-based approach can be resource-intensive and often requires extensive integration and maintenance of systems. It can also be oversized in simple scenarios where data only needs to be checked for completeness. In addition, implementation in existing systems is often associated with considerable costs and effort.

When should both approaches be combined?

In practice, both approaches are often used in combination, as they complement each other perfectly. A typical example is integration into a customer relationship management (CRM) system: the completeness check ensures that basic customer data is complete before processing, while the workflow ensures that this data flows correctly and properly through the various processing steps - from input to validation and use.

Conclusion: Completeness check vs. workflow - when should you use which?

Completeness checks are particularly suitable for simple, standardized processes where the data fields and requirements are clearly defined and the data is only minimally processed.

Workflows are the preferred method in complex, multi-stage processes in which data undergoes a transformation and has to pass through several processing stages.

The decision as to which approach makes more sense depends on the requirements of the respective business process. For simple use cases, a completeness check is often sufficient, whereas in more complex scenarios, a workflow-based approach is essential to ensure high data quality and consistency. Sophisticated data quality management utilizes the strengths of both approaches, ensuring not only data integrity but also business success.