Why Automating ETL Validation Scripts Will Improve Data Validation

Data teams struggle with cleaning and validating incoming data streams using ETL validation scripts due to their costliness, time-consuming nature, and difficulty in scaling. This issue is expected to worsen as enterprises face a projected 3x increase in data growth over the next five years. To maintain control over data operations and ensure effective cleansing and validation of data, an automated approach is necessary. Automated data observability solutions like Acceldata Data Observability Platform can automatically clean and validate incoming data pipelines in real-time, enabling enterprises to make data-driven decisions based on the most current and accurate data available. Manual ETL validation scripts have limitations such as being unable to handle real-time data streams, causing delays in analyzing real-time data, higher data infrastructure costs, and constraints on resources and data quality problems. Automatically validating data streams in real-time can help enterprises avoid paying for incomplete or incorrect records, reduce their overall cost of handling data, and allow data teams to focus more on innovation rather than mundane tasks.