Automations

Identify orphaned data in Power BI

⚡️ Automation

Identify orphaned data in Power BI with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.

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Automations

Identify orphaned data in Power BI

⚡️ Automation

Identify orphaned data in Power BI with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.

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Overview

One important aspect of data cleanup practices is to identify orphaned data from Power BI. Orphaned data refers to data that no longer has an assigned team or user. It is necessary to tag this data for review in order to maintain data integrity, optimize storage use, enhance system performance, and ensure accurate data analysis and reporting processes. By identifying and addressing orphaned data, organizations can effectively manage their data and make informed decisions based on reliable information.

How it works

Integration with Power BI allows for efficient automation with triggers and actions. Triggers can be scheduled based on specific timeframes, such as hourly or daily, to initiate subsequent actions. Actions cover a range of operations, such as filtering and updating metadata. By combining multiple actions, customized workflows can be created to meet the specific requirements of your team. With Secoda, bulk updates can be performed on metadata in Power BI. This integration enables the identification of data in Power BI that lacks an assigned team or user, allowing it to be tagged for further review.

About Secoda

Integrating Power BI with Secoda can be highly beneficial for data teams. Secoda serves as a comprehensive data management platform that consolidates various aspects of your company's data knowledge, such as data catalog, lineage, documentation, and monitoring. By using Power BI along with Secoda, data teams can effectively scale their data enablement practices. This integration simplifies the process of maintaining trust scorecards, allowing data teams to efficiently manage and track the quality and reliability of their data.

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