If the data source is on-premises (like SQL Server or Oracle), to configure an On-premises Data Gateway. The gateway acts as a secure bridge between Power BI Service and the local database, allowing scheduled refreshes to run automatically. For cloud-based sources like Azure SQL, SharePoint Online, or Salesforce, the connection works directly without a gateway.
For example, in one of my projects, we had a hybrid setup — data from both SQL Server (on-premises) and Azure Synapse. I configured the gateway for SQL and used direct connections for Azure. The refresh schedule was set to run at 6 AM daily so that by the time business users logged in, they always saw fresh data.
I also use email notifications to alert the team in case a refresh fails. This is especially important for production dashboards — sometimes refreshes fail due to expired credentials, changes in source schema, or gateway connection issues.
A challenge I faced once was when multiple large datasets refreshed simultaneously, which caused performance slowdowns. I solved it by staggering the refresh times and using incremental refresh for larger datasets so that only recent data was updated instead of reloading everything.
In Power BI Premium, I’ve also leveraged deployment pipelines to manage refreshes across development, test, and production environments separately, which ensures stability before new data models go live.
As an alternative, for more advanced workflows, I’ve integrated refresh automation using Power Automate or Azure Data Factory — triggering refreshes dynamically after upstream ETL jobs finish, ensuring perfect data synchronization.
So, in summary — scheduling data refresh in Power BI is about automating data updates through the Power BI Service, using gateways for on-premises data, setting refresh frequency wisely, and monitoring for errors — ensuring that users always see accurate, real-time insights with minimal manual effort.