Today’s data pipelines use transformations to convert raw data into meaningful insights. Yet, ensuring the accuracy and reliability of these transformations is no small feat – tools and methods to test the variety of data and transformation can be daunting.
Transformations generally involve changing raw data that has been cleansed and validated for use by individuals or systems. Data transformations are essential for data management, integration, migration, wrangling, and warehousing. Data transformation can be:
Constructive – Data is replicated, added, or copied
Destructive – Tables, records, or fields are deleted
Aesthetic – Standardization of data is changed to meet requirements
Structural – Renaming, combining, and moving columns is a method to reorganize data
AI models, for example, require large volumes canada rcs data of data, some unstructured and ungoverned. Many enterprise architectures lack a modern data quality strategy and are unprepared for AI workloads’ complexity and high computing demands. As a result, the quality and integrity of the underlying data provide outcomes that are frequently untrustworthy, unpredictable, and outdated.
Addressing the prevalent issues causing poor data quality – such as inadequate source data profiling and cleansing, poorly designed or error-prone transformations, ineffective testing, and insufficient validation – can significantly improve the reliability of data pipelines. Understanding the impact of transformation errors on data quality emphasizes the necessity for meticulous planning and execution of tests. By focusing on these areas, organizations can mitigate risks and enhance the accuracy and consistency of their data.