Migrating from Power BI Scorecard Hierarchies

Power BI Scorecard Hierarchies let organisations build multi-level metric structures with auto-generated filtered scorecard views at each level. If you tracked revenue at the company level and then broke it down by region, by product line, and by sales team, the hierarchy connected those views together. The Heatmap view gave a visual overview of performance across the hierarchy.

Microsoft has confirmed there is no replacement. The Fabric roadmap for 2026 contains no scorecard or hierarchy items. For organisations that use Scorecard Hierarchies as the backbone of their performance management, this creates a genuine gap.

1. 1

   Multi-level metric hierarchy

   The ability to nest metrics within metrics, showing how team-level KPIs roll up to department goals and ultimately to company objectives. Without hierarchies, each scorecard goal stands alone.

2. 2

   Auto-generated filtered views

   Scorecard Hierarchies automatically create filtered views at each level of the hierarchy. A regional manager can see their metrics in context without manual configuration. This capability is being removed.

3. 3

   Heatmap performance overview

   The Heatmap view gives executives a colour-coded overview of performance across the entire hierarchy. Red, amber, green at a glance. It is being discontinued alongside Scorecard Hierarchies, with no replacement announced.

4. 4

   Metric Sets

   Metric Sets let you group related metrics together for comparison and monitoring. Creation was disabled on October 25, 2025, and the feature was fully retired on November 15, 2025.

5. 5

   Viva Goals

   Microsoft Viva Goals provided full OKR hierarchy management with Power BI integration for automatic progress updates. It was fully retired on December 31, 2025. Microsoft stated explicitly: "We encourage customers to begin exploring third-party OKR tools."

If you relied on Scorecard Hierarchies, you have three main paths forward. Each involves trade-offs.

Scorecard Hierarchies and metric trees solve the same fundamental problem: connecting metrics so people can see how their numbers relate to the bigger picture. But they approach it differently.

The key difference is this: Scorecard Hierarchies organised metrics by organisational structure. Metric trees organise them by cause and effect. One tells you what your team is responsible for. The other shows you what drives what, and by how much. When a metric drops, a Scorecard Hierarchy told you which team owned it. A metric tree tells you which upstream metrics contributed to the drop, who owns each one, and whether the actions being taken are working.

1. 1

   Export your Scorecard structure

   Before the hierarchy data is fully gone, document your current Scorecard structure: which goals existed at each level, who owned them, and how they were nested. Screenshot the hierarchy and Heatmap views for reference. Export goal details via the Power BI REST API if needed.

2. 2

   Identify your top-level metrics

   Start with the metrics that sat at the top of your hierarchy. These are typically company-level KPIs like revenue, customer count, or margin. In a metric tree, these become your root nodes.

3. 3

   Map the causal relationships

   For each top-level metric, ask: what drives this? Revenue might be driven by customer count multiplied by average revenue per customer. Customer count might be driven by new acquisitions minus churn. This is where metric trees go beyond what Scorecard Hierarchies offered. Instead of nesting by org structure, you are mapping the actual cause-and-effect relationships between your metrics.

4. 4

   Connect your data sources

   Connect KPI Tree to the same data warehouses Power BI uses. If you use Snowflake, BigQuery, or PostgreSQL, KPI Tree connects directly. If you use dbt, sync your entire metric catalogue with our native Semantic Layer integration. Each metric runs a single query to your warehouse, and all computation (aggregation, correlation, comparison) runs in our engine.

5. 5

   Assign RACI ownership

   For every metric in the tree, assign Responsible, Accountable, Consulted, and Informed roles. This goes beyond the single owner that Scorecard Hierarchies supported. RACI ensures that when a metric moves, the right people are notified and the right person is accountable for the response.

6. 6

   Set up subscriptions

   Replace Power BI email alerts with metric subscriptions via Email, Slack, SMS, or WhatsApp. Metric owners can subscribe to their metrics and receive updates without logging into a tool.

7. 7

   Run both systems in parallel

   Keep Power BI for dashboards and ad-hoc analysis. Use KPI Tree for the metric hierarchy, ownership, and action tracking that Scorecard Hierarchies used to provide. Most teams find the two tools complement each other: Power BI for exploration, KPI Tree for the system of understanding and accountability.

Moving from Scorecard Hierarchies to metric trees is not just replacing what is being removed. It is upgrading to a fundamentally different approach to understanding your business. Here is what becomes possible.

This is not about replacing Power BI entirely. Power BI remains one of the most capable dashboard and reporting tools in the market. Its connector library is unmatched. DAX is powerful for complex calculations. The Microsoft 365 integration is deep. Copilot is improving rapidly.

What Power BI will no longer provide after April 15 is a structured way to connect metrics in a hierarchy, assign ownership, and track whether actions move the numbers. That is what Scorecard Hierarchies attempted, and that is exactly what metric trees are purpose-built to do.