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 central strategies in DQM that are often compared to each other are the completeness check and the workflow-based approach. Both have their own strengths and specific application areas. 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 present. It checks whether data records contain all the necessary information to be further processed or analyzed. 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 need to process a high volume 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 accidentally make it to the next phase of a process.

Simple business processes

In areas where data requirements are clear and relatively static - for example, 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 the necessary fields have been migrated correctly. This is less about the correctness 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 completeness 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 across the entire life cycle

In processes that involve multiple departments or external partners, the workflow ensures that data is validated and, if necessary, cleansed at every level of the process. 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 need to ensure that data is not only processed correctly, but also in compliance 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 often involves considerable cost and effort.