Identify assets for cleanup in Amazon Quicksight with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.
Get startedOne important aspect of data cleanup is integrating Amazon Quicksight to tag data that has not been accessed within a certain period as 'for review'. This integration aids in the overall improvement of data quality by identifying and highlighting data that requires attention and potential cleanup. By leveraging this feature, organizations can ensure that their datasets remain accurate, reliable, and consistent, facilitating valid and informed analyses and decision-making processes.
Using Secoda's integration with Amazon Quicksight, you can effortlessly automate the process of tagging data that hasn't been accessed within a specific timeframe as 'for review' for cleanup. With Secoda, an automation is composed of two main components: Triggers and Actions. Triggers are responsible for initiating the workflow based on defined schedules such as hourly, daily, or custom intervals. Actions, on the other hand, encompass various operations like filtering and updating metadata. You can create customized workflows by stacking multiple actions, ensuring that the process aligns with your team's particular requirements. By leveraging this integration, you gain the capability to perform bulk metadata updates in Amazon Quicksight efficiently and effectively.
By integrating Amazon Quicksight with Secoda, data teams can enhance their data cleanup practices and efficiently prioritize assets. Secoda acts as a centralized platform that indexes various aspects of your company's data knowledge, including the data catalog, lineage, documentation, and monitoring. This integration allows for seamless access to relevant information, helping data teams scale their operations effectively. Amazon Quicksight provides additional analytical capabilities that further enrich the data insights available within the Secoda platform. Together, these tools empower data teams to optimize their data management processes efficiently.