Semantic BI Consolidation: Retire Duplicated Models for 4x Faster Insights

Data Visualization

Enterprise reporting environments rarely become complex overnight. As business needs to evolve, organizations create new dashboards, reports, and semantic models. Over time, many of these assets begin to overlap, rely on inconsistent definitions, or remain available long after they stop delivering value.

For large enterprises, the scale can be significant. Reporting estates may carry approximately 45,000 semantic models behind 60,000 reports, contributing to oversized capacity, unused licenses, duplicated models, and growing maintenance costs. Without sufficient visibility and governance, overlapping assets can remain undetected for years.

Turn complexity into measurable value

Semantic BI Consolidation uses AI-powered accelerators to audit the entire reporting estate, including workspaces, reports, models, fields, metadata, and dependencies. It identifies what should be retained, consolidated, optimized, or retired.

This creates a documented, consolidation-ready view of the BI environment while helping organizations:

Reduce capacity, licensing, monitoring, and maintenance costs
Establish consistent business definitions and trusted sources of truth
Strengthen governance, compliance, access management, and deployment processes
Improve the visibility and adoption of valuable reporting assets

The potential business impact

Testing indicates that organizations may be able to consolidate 40% to 80% of their reporting assets. For an organization managing more than 10,000 reports, conservative estimates indicate:

$1M+ in estimated annual hard savings
$520K+ in yearly AI productivity uplift
13,000 analyst hours saved annually
4× faster time to insights

Actual results will depend on the organization’s reporting estate, licensing model, capacity usage, and commercial agreements.

Build a trusted foundation for AI

Semantic BI Consolidation is more than a cost-reduction exercise. It creates the governed semantic foundation required for reliable AI-powered analytics.

With fewer duplicated models and clearer business definitions, organizations can improve the quality of conversational analytics and talk-to-data experiences. Business users can ask tools such as Copilot agents and Genie questions in plain language while working with more consistent and trustworthy information.

Automated technical and business documentation can also support compliance, onboarding, and training. At the same time, continuous monitoring and automation help maintain the health of the BI ecosystem after the initial consolidation.

Consolidate before you migrate

Semantic BI Consolidation can also serve as an important first step in a BI modernization or migration program.

Instead of transferring every existing report and model into a new platform, organizations can first identify which assets should be migrated, merged, optimized, or retired. This reduces the migration scope, avoids rebuilding unnecessary content, and prevents legacy complexity from being reproduced in the target environment.

The approach can analyze multiple modern BI environments when the necessary metadata and transformation information are accessible, including platforms such as Power BI, Tableau, and Looker.

Move toward a proactive BI ecosystem

The ultimate goal is not simply to have fewer reports. It is to create a proactive BI ecosystem where self-service analytics is discoverable, monitored, governed, and easier to use.

By combining estate rationalization, governance, documentation, adoption support, and AI-enabled assistance, organizations can reduce hidden costs today while building a more scalable foundation for future analytics and AI initiatives.