Enterprise BI Modernization for a Leading Logistics Provider

When a leading supply chain and logistics organization selected Sigma to replace MicroStrategy, they faced a significant modernization challenge.

Its legacy BI ecosystem contained more than 33,000 objects and over 600 reports — many duplicative and inconsistently defined. KPI definitions varied across teams, and the foundational data models were not structured to support a clean migration.

Without intervention, the organization risked transferring years of accumulated technical debt into its new analytics platform.

They needed not just a migration partner, but a strategic data modernization one.

Migration Planning & Rationalization

Aimpoint conducted stakeholder interviews, working sessions, and MicroStrategy metadata extraction via API to evaluate usage and dependencies. The team leveraged Databricks to ingest, analyze, and store the full inventory of legacy asset metadata, providing a structured view of the existing data landscape to help guide migration strategy. This gave the organization a quicker, more informed basis for identifying redundancies, understanding dependencies, and prioritizing assets for migration.  Aimpoint subsequently developed a report rationalization framework in Sigma to track and reduce the number of objects targeted for migration.

Consolidation & KPI Standardization

Aimpoint leveraged the AI capabilities within Databricks to identify clusters of source tables with overlapping logic and to generate recommendations for consolidating and remodeling them with dbt. Databricks actively accelerated delivery of new data models and consolidated reports by transforming what would have been a manual, time-intensive analysis into a faster, more accurate process. As a result, duplicative reports were consolidated into flexible Sigma workbooks built on governed metric definitions. Certain operational extracts were redirected back to source systems, preventing unnecessary analytics platform growth.

Enterprise Data Model in dbt

When discovery revealed foundational gaps, Aimpoint pivoted development efforts toward building a scalable Enterprise Data Model (EDM) using dbt Platform in conjunction with Databricks.

Using bronze, silver, and gold medallion architecture, the joint organization–Aimpoint engineering team began rebuilding and standardizing hundreds of enterprise KPIs.

Within six months, nearly 130 targeted metrics were production-ready — accelerating time to value while establishing long-term scalability.

Proactive Data Quality Monitoring

Aimpoint implemented a dedicated monitoring layer across the data models to proactively detect failed or delayed refreshes and surface data quality issues.

This capability did not previously exist. The new monitoring framework provides continuous visibility into data reliability, strengthens trust in reporting, and enables faster issue resolution.

Sigma Enablement & Co-Development

Approximately 30 Sigma power users and builders were trained through virtual and in-person cohorts. Through structured courses and office hours, Aimpoint embedded governance and best practices to ensure sustainable self-service analytics.