A calculated measure using a time-based aggregation function like `YearToDate()` is the correct choice. It dynamically calculates the cumulative total from the start of the year to the current period in the context of the report’s filters, making it ideal for live data scenarios. Restricted measures or manual filters are static and would not automatically adjust with new daily data.

Building a blended dataset is the core, sustainable solution. It allows you to create a unified semantic layer by joining the two models (live or imported) on a common dimension, like a mapped cost element ID or description. This resolves the hierarchy mismatch and enables direct comparison in widgets, unlike separate stories, manual exports, or unions which don’t solve the semantic mapping issue.

Data access control (DAC) applied at the model level is the foundation, restricting data visibility based on user attributes (e.g., region). Delivering the story via Teams ensures users only see the story in their assigned team’s space. This two-tiered approach (model security + delivery control) effectively enforces row-level data isolation. Story filters can be changed by users, and bookmarks or private versions do not prevent initial data access.

Applying a relative date filter (e.g., ‘Last 12 Months’) directly on the ‘Month’ dimension is the most straightforward and efficient method. It dynamically adjusts as time progresses, requiring no manual maintenance. Calculated dimensions or key date offsets are more complex and typically used for different scenarios, like comparing a moving period to a fixed benchmark.

The ‘Navigation’ feature in a story link allows you to configure an action (like clicking a data point) to open another story or application while passing a context filter (like the selected Supplier ID). This creates a seamless drill-through experience. Linking to a URL is static, drill-down works within a hierarchy in the same story, and filter actions apply filters within the current view.

For geo maps to work automatically, the dimension used for location must contain standardized geographic identifiers like ISO country codes (e.g., ‘US’, ‘DE’). Textual country names are often not recognized by the mapping service. The solution is to enrich the ‘Country’ dimension in the model with the correct ISO codes. The connection type, story theme, or map layer choice are not the primary issues here.

A calculated account is designed for this exact purpose. You define a formula (e.g., `#Hire_Count * #Average_Salary`) that derives its value from other input accounts. This prevents manual entry errors, ensures consistency, and automates the calculation. Input-enabled key figures or distribution would not perform the automatic multiplication.

When a story is accessed via a public link, the viewer is treated as an anonymous ‘public user’. Data Access Control (DAC) rules are still enforced. If no specific DAC rights are granted to the ‘PUBLIC’ role or a dedicated role for public access, these users will see no data. This is a common oversight when sharing content externally. Processing status and layout affect all users, not just public ones.

With live connections to sources like BW, you can enhance the model in SAC by creating local calculated measures or dimensions. You can define ‘Profit per Unit’ as a formula (e.g., `[Profit] / [Units Sold]`) using measures from the live query. This provides the custom metric without changing the source system, offering flexibility for end-user analytics. Story calculations are more limited in scope and reuse.

A calculated measure using a time-based aggregation function like `YearToDate()` is the correct choice. It dynamically calculates the cumulative total from the start of the year to the current period in the context of the report’s filters, making it ideal for live data scenarios. Restricted measures or manual filters are static and would not automatically adjust with new daily data.0