Baker Tilly’s challenges in scaling and ensuring quality at a high speed

For Baker Tilly’s Data & AI team, the most critical challenges center around scale governance and speed. As they work with large enterprises that rely on complex, rapidly evolving data estates, it’s essential to ensure consistent modeling standards, maintain high performance under heavy workloads, and deliver governed, reusable assets. 

Ultimately, our challenge isn’t just building analytics – it’s enabling analytics to operate as a product that can be trusted, extended, and scaled across the business.

Chris Wagner, Director of Data & AI, Baker Tilly

Before adopting Tabular Editor 3 as part of their BI tool stack, the team struggled with slow iteration cycles, limited visibility into structural issues within their semantic models, and difficulty maintaining consistent modeling best practices across large teams – inefficiencies that naturally emerge in enterprise BI environments, but nonetheless are time-consuming friction points that ultimately lead to a lot of manual and inconsistent work. 

Deciding on Tabular Editor 3 

Three performance bottlenecks in particular motivated Baker Tilly’s Data & AI team to seek out other tools as a solution to their issues: 

1. Slow model iteration and deployment cycles: Especially on large Fabric and Power BI datasets, where even small changes required long periods of waiting. 
2. Lack of automated guardrails: There was no practical way to enforce standards or validate model quality across a distributed consulting team. 
3. Complex performance debugging: Identifying inefficient measures, relationships, or model structures took up time that could’ve been used more effectively elsewhere. 

These challenges directly affected the delivery speed of insights to clients – and longer development loops meant longer feedback loops, ultimately leaving the team in a vicious cycle. It was therefore evident that a more powerful tool was needed: 

In a consulting environment – where speed, consistency, and quality directly impact client outcomes – those barriers made it harder to deliver insights with the agility our clients expect. Tabular Editor eliminated much of this friction, allowing us to focus on delivering value instead of wrestling with tooling limitations.

Chris Wagner, Director of Data & AI, Baker Tilly 

Implementing the solution to their problems 

The team at Baker Tilly chose to adopt Tabular Editor, as it appeared to be perfectly aligned with their need for a professional-grade development environment for their semantic models. As their BI practice scaled and the complexity of their models grew, they needed a tool that could support true engineering workflows.  

Tabular Editor was rolled out in a phased way, starting with the senior architects using it on large, complex engagements where traditional modeling tools were slowing down delivery. The faster iteration, cleaner models, and consistent best practices, made the value undeniable, and they developed internal standards. From there, the adoption grew organically, and today, Tabular Editor is fully embedded into Baker Tilly’s methodology and definition of what great BI engineering looks like. 

Some of the Tabular Editor features Chris and the team highlight as the most transformative for their BI work include: 

• Best Practice Analyzer (BPA): BPA allowed the team to enforce consistent standards across consulting teams and client environments. It allowed for automated governance aligned to their modeling principles, eliminating ambiguity and reducing technical debt. 

• Scripting and automation: The ability to automate repetitive tasks, generate measures, bulk-edit metadata, and refactor models has been essential. It helped reduce development time and ensure consistency at scale. 

• Git/DevOps integration: As a consulting firm delivering enterprise-grade solutions, version control is non-negotiable. With Tabular Editor, that all became seamless. 

• Partition management and performance tooling: For large datasets and real-time workloads, being able to efficiently manage partitions and understand performance bottlenecks made a noticeable difference in final solution quality. 
  

The biggest compliment we receive is that our analytics “just work”– and that’s a direct result of the discipline and consistency Tabular Editor enables behind the scenes.

Chris Wagner, Director of Data & AI, Baker Tilly 

Results of adopting Tabular Editor 

After adopting Tabular Editor, Baker Tilly has experienced substantial results across every aspect of their BI engineering lifecycle. 

Improved model development 

Thanks to scripting, bulk updates, and streamlined metadata management, model development cycles have become significantly faster – Chris and the team assess a 30-50% reduction in development time for new models compared to the time spent before adopting Tabular Editor. They also experience a 60-80% reduction in model design inconsistencies after implementing BPA with firm-wide rule sets. 

Faster, more reliable insights 

Because Tabular Editor standardizes and automates best practices, the consultants at Baker Tilly spend less time fixing structural issues and more time delivering meaningful insights: 

From a business perspective, the impact has been transformational. Faster and more reliable BI development means our clients get insights sooner and with greater confidence. This directly accelerates decision-making, reduces operational inefficiencies, and shortens the time to value for analytics initiatives.

Chris Wagner, Director of Data & AI, Baker Tilly 

Improved model quality and optimized refresh performance reduce compute costs, avoid downstream reporting issues, and ultimately provide executives with trusted, high-performance dashboards, allowing them to receive insights faster, more reliably, and with far fewer surprises. 

Change in organizational mindset 

Implementing Tabular Editor has introduced a level of structure and automation that has elevated data modeling into a software development discipline – ultimately changing their mindsets from “building reports” to engineering semantic models. Instead of treating BI work as a set sequence of manual steps, the team now thinks in terms of reusability, governance, and automation.  

This change in mindset has transformed how the team designs, reviews, and maintains models, enabling them to deliver cleaner architecture, faster iterations, and more scalable solutions across all client engagements. 

Enabling new opportunities 

Tabular Editor has enabled several new opportunities within Baker Tilly, expanding what they can deliver as a consulting organization: 

• Scaling models and teams: Baker Tilly can now support multi-developer modeling at enterprise scale, thanks to DevOps workflows, version control, and automation being built into their process. This allows them to deliver bigger, more complex solutions with higher velocity. 

• Supporting new initiatives and architectures: As their clients move toward Microsoft Fabric and unified Lakehouse patterns, Tabular Editor enables the modeling agility and governance Baker Tilly needs to scale these modern platforms.
  
  
• Improving governance across entire organizations and clients: With BPA rules, structured metadata, and automated validations, Baker Tilly has been able to formalize modeling standards that stretch across departments, business units, and their entire consulting practice.

• Accelerating AI-driven analytics: Cleaner semantic layers with consistent definitions make it significantly easier to integrate Copilot, natural language interfaces, and AI-assisted insights. 

Looking to the future 

When looking to the future, Baker Tilly expects Tabular Editor to become even more embedded in their enterprise BI engineering lifecycle, as Fabric continues to mature and semantic models become more central to AI-driven analytics. 

We plan to continue to expand adoption across all project teams, integrate more automation into our CI/CD pipelines, and evolve our BPA rules to reflect Fabric-first design patterns. We also see Tabular Editor playing a critical role in enabling reusable semantic templates, metadata-driven modeling approaches, and AI-assisted development with tools like Copilot. 

In other words, Tabular Editor isn’t just a tool we use – it's a foundational part of how we’re building the next generation of enterprise BI solutions.

Chris Wagner, Director of Data & AI, Baker Tilly