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Enterprise BI Consolidation: Putting your metadata to work

Migration

Why enterprise semantics, metadata and lifecycle management are key assets to consider during consolidation.

Enterprise BI consolidation initiatives are gaining serious momentum across many industries. As we reach the midpoint of 2025 we are seeing more enterprises accelerating efforts to move away from siloed, duplicative analytics environments. There will be winners and losers in this consolidation.

Why are BI migrations and consolidations happening?

The pressure is on, from AI strategies to cost rationalization, to simplify the analytics stack and unify both data and meta-data. Interoperability is key and the quality and reliability of AI co-pilots hinges on data quality and the depth of meta-data.

Rethinking BI in the Age of AI

The role of BI is evolving. Traditional dashboards and fragmented tools are no longer sufficient for enterprises aiming to become truly information driven. Being information driven, once an aspirational term that tech markers used, has materialized into real AI agents.

Agentic initiatives now tight alignment between operations, systems, and decision-makers require data and meta-data access. Business Intelligence analysts is the next logical step where decision sciences with the right meta data, context, and certified data can be mostly automated.

The Role of Semantics

If you’ve been in analytics long enough, you know the term “semantic layer” has been around for decades. At its core, it’s about translating data into business meaning. Assigning labels, rules, and context and logic provides meaning that makes sense to people and AI agents that don’t need to understand the underlying data structures.

AI and automation are raising the stakes. Historically natural language queries required experts to configure additional layers in a highly structured format to create an illusion of understanding in the user experience. A semantic layer isn’t just a convenience for analysts building charts. It’s now one of multiple meta-data assets that AI needs to understand. It is however only one asset that both AI agents need to remove the guesswork.

Metadata is the New Glue

Metadata continuity from source systems to reports and AI models is more important than ever. There are powerful catalog tools out there that understood and innovated ahead of the wave. However, for most organizations, metadata remains fragmented across business applications, ETL pipelines, BI tools, and ML platforms.

Lifecycle Management: Still a Blind Spot

Change management has always been a pain point in BI. Version control, promotion workflows, rollback, dependency tracking are secondary to fast results and insights. Many teams rely on manual processes, custom scripts, or third-party add-ons to bridge the gap.

As BI becomes more embedded in decision-making and automation, lifecycle management can’t be an afterthought. It’s time we treat BI with the same rigor as software development because that’s exactly what it’s becoming as we get to lean on generative AI as an accelerator.

Putting your metadata to Work during BI consolidation

At BIChart, we’re thinking beyond just migrating metadata from Tableau to Power BI. Our focus is on preparing metadata and contextual assets that set the foundation for AI-augmented business intelligence. If you’re looking to modernize your BI stack and unlock the next phase of data-driven insight, let’s talk. Schedule a call with our team to see how we can help.

Ryan Goodman

Ryan Goodman

Ryan Goodman has been in the business of data and analytics for 20 years as a practitioner, executive, and technology entrepreneur. Ryan recently returned to technology after 4 years working in small business lending as VP of Analytics and BI. There he implanted an analytics strategy and competency center for modern data stack, data sciences and governance. From his recent experiences as a customer and now working full time as a fractional CDO / analytics leader, Ryan joined BIChart as CMO.