Migrating Power BI Dashboards to Metabase

The Challenge

Our client, engaged through a US-based financial consulting firm, needed to migrate analytics dashboards for a materials supplier in the construction and landscaping industry. The existing Power BI dashboards had become a bottleneck: they loaded slowly with large datasets, filtering caused performance issues and timeouts, and the visualization capabilities didn’t meet the team’s analytical needs.

The business impact was significant. Sales teams and financial analysts spent more time waiting for dashboards to load than actually analyzing data. Complex filter combinations — essential for analyzing sales by region, product category, and time period — frequently caused the system to time out. The organization needed a faster, more reliable analytics platform that could handle their data volumes without compromising usability.

The construction materials industry generates high volumes of transactional data — thousands of orders per day across hundreds of product SKUs, multiple warehouses, and diverse customer segments (contractors, landscapers, retail). The Power BI dashboards were designed when data volumes were lower, and they hadn’t been optimized as the business grew. What started as occasional slowness had become persistent — the sales team had effectively abandoned the dashboards and returned to requesting ad-hoc reports from the finance team, creating an additional workload that wasn’t sustainable. The client needed a solution that would handle their current data volume and continue performing as the business scaled.

Our Approach

We executed a systematic migration from Power BI to Metabase, focusing on performance, usability, and analytical accuracy:

• Dashboard Audit: We inventoried all existing Power BI dashboards, documenting the business logic, calculations, filters, and usage patterns for each. This ensured that no analytical capability would be lost in the migration.
• Data Layer Optimization: Rather than simply replicating the Power BI dashboards in Metabase, we optimized the underlying data models. We created pre-aggregated tables for common queries, implemented proper indexing, and designed the data layer specifically for Metabase’s query engine. This addressed the root cause of the performance issues — not just the symptoms.
• Dashboard Recreation: We rebuilt each dashboard in Metabase with improved visual design, faster filtering, and better layout. Key dashboards included: sales performance (by region, product, and time period), financial analytics (revenue, margins, and forecasts), and operational metrics (inventory, fulfillment, and supplier performance).
• User Testing & Iteration: We conducted testing sessions with actual end users — sales reps, financial analysts, and managers — to ensure the new dashboards met their workflow needs. This iterative approach caught usability issues before the full rollout.

We also implemented a set of new analytical capabilities that hadn’t existed in the original Power BI dashboards. These included: customer purchase frequency analysis (identifying at-risk accounts based on declining order patterns), product affinity analysis (which products are frequently purchased together, informing cross-sell strategies), and seasonal demand forecasting that combined historical sales data with industry seasonality patterns. These additions went beyond the migration scope to deliver immediate analytical value that justified the investment in the platform transition. We built all dashboards with embedded documentation — tooltips, definitions, and methodology notes — so that new team members could understand the data without needing a training session.

Results

• Significantly improved load times — dashboards that took 30+ seconds in Power BI now load in under 5 seconds.
• Reliable filtering even with large datasets — no more timeouts when applying complex filter combinations.
• Improved visual clarity and layout making it easier for non-technical users to find the information they need.
• Better alignment between dashboard design and how teams actually analyze sales and financial performance.
• Lower licensing costs — Metabase’s pricing model was more cost-effective for this organization’s scale.
• Comprehensive documentation enabling the client’s team to maintain and extend the dashboards independently.

Technologies Used

Metabase, SQL, data modeling, PostgreSQL, dashboard migration, performance optimization.

Project Screenshots