Data quality—regardless of a company’s size, industry, or level of maturity—is notoriously difficult to define, measure, monitor, and improve. Yet it matters profoundly, because the negative consequences of inaccurate or inconsistent information are virtually endless. Poor data quality affects operational efficiency, analytical accuracy, regulatory compliance, customer experience, and ultimately business decisions.

Achieving high data quality requires a holistic, organisation‑wide approach, which is rarely feasible in practice. Company data arrives in every possible format and from every direction. Some systems process millions of records or transactions each day. Some operate in batch mode, others in real time. In addition, companies constantly exchange data with external partners, suppliers, and customers—domains where they have no control over how the data is created or maintained.

For these reasons, data quality management must be embedded directly into the data processing pipeline, across all segments of IT activity. It should not be limited to analytical systems alone. Instead, it should play an active role as close as possible to the source of data creation—where issues can be detected early, corrected efficiently, and prevented from propagating downstream.

Data Quality, in a practical sense, can be organised in many different ways, and there is no universal model that fits every business. However, at a minimum, an effective Data Quality framework must include Data Profiling and Data Validation & Correction.

Data Profiling is not always required if the metadata is already well understood, but it is extremely valuable during the early stages of a project. Depending on the nature of the business, profiling may also be performed periodically or on demand. Its purpose is to analyse new or frequently changing data entering the system and to highlight potential data quality issues. The results of Data Profiling help identify the most likely problem areas and guide the creation of effective Data Validation and Correction rules.

Once the Data Validation Rules are defined, they can be compiled into a Data Validation Specification, which is then implemented as a task within the ongoing production workflow. This ensures that validation becomes a repeatable, automated part of the data processing pipeline.

All Data Profiling results, Data Validation Specifications, and Data Validation Logs are stored in a database for future reference. This historical repository supports long‑term monitoring, trend analysis, auditing, and continuous improvement of data quality processes.