How do you handle security and governance during a Fabric migration, especially for regulated industries?

Security and governance are built into every phase of our Fabric engagements—not bolted on at the end. We start with a comprehensive assessment of your existing security posture, including Entra ID configuration, network isolation, and data classification. During migration, we implement Microsoft Purview governance from the outset—sensitivity labels, data classification, lineage tracking, and policy enforcement. Access controls are designed using RBAC, row-level security (RLS), and object-level security (OLS) mapped to your organizational roles. Workspace hierarchy and naming conventions are designed to enforce data domain boundaries. For regulated industries, we configure private endpoints, network isolation, and audit logging to meet compliance requirements before any production data moves.

What kind of ROI can we expect from consolidating onto Microsoft Fabric?

The ROI comes from multiple sources. Infrastructure cost savings are often the most immediate win. Beyond cost reduction, organizations typically see significant improvement in Power BI report render times through DirectLake optimization, dramatic reductions in operational complexity (from managing five or more separate Azure services to a single governed platform), and the ability to enable self-service analytics and production AI capabilities that were impossible on the fragmented estate. Our phased approach means you start seeing returns on high-value workloads early—not just at the end of a multi-year project.

We have hundreds of SSIS packages and Synapse stored procedures. How complex is the migration to Fabric?

This is the hardest part of any Fabric migration—and the part most firms underestimate. SSIS packages can't be lifted and shifted into Fabric. They require fundamental re-architecture from procedural ETL to ELT patterns using Fabric Pipelines, Dataflow Gen2, or Spark Notebooks. Each package needs to be assessed for complexity, dependencies, and the optimal Fabric-native replacement. Synapse dedicated SQL pool workloads require DDL refactoring (distribution keys, indexes, and workload management settings don't exist in Fabric Warehouse), data extraction via CETAS to staging, and T-SQL compatibility remediation. Azure Data Factory pipeline migrations need a feature-parity gap assessment since Fabric Data Factory differs from ADF in key ways—no SSIS Integration Runtime, different connection handling, and no datasets concept. Pythian has done this work across complex, multi-workload environments and understands where the hidden compatibility gaps live.

Is Microsoft Fabric mature enough for enterprise-scale production workloads?

Fabric has matured significantly since its general availability in November 2023. As of mid-2025, over 28,000 paying organizations use it, including 80 percent of the Fortune 500. Microsoft is treating Fabric as its flagship data and analytics platform with monthly feature updates, and is actively steering customers from legacy services—retiring Power BI Premium P-SKUs, feature-freezing Synapse dedicated SQL pools, and migrating ADF users toward Fabric Data Factory. That said, features still move between preview and GA, and capacity-based pricing requires careful management to avoid unexpected costs. This is exactly where Pythian adds value. We know where the sharp edges are—which features are production-ready, which need workarounds, and how to architect around current limitations. We've helped dozens of organizations take Fabric from prototype to production, and we design every implementation with the operational discipline to handle enterprise-scale workloads reliably.

Data-Centric AI Workshop

Audit enterprise data debt.

Uncover hidden architectural blockers, brittle pipelines, and unorganized data silos that cause models to fail. Establish high-integrity data assets that serve as a reliable foundation for enterprise AI scale.

Eliminate data debt ->

Unify fragmented data estates.

Connect legacy and multi-cloud environments across Google Cloud, AWS, Azure, or Oracle. Ground models in one secure source of truth for accurate, hallucination-free outcomes.

Unify data estates ->

Enforce strict data governance.

Establish strict data security boundaries, compliance requirements, and privacy guardrails before model deployment. Protect corporate intellectual property while enabling safe workforce adoption.

Enforce data governance ->

Evaluate current data infrastructure and identify high-value use cases.

Receive a comprehensive diagnostic assessment analyzing your overarching business objectives, operational workflows, and current data stack. Collaborate with our senior CTO team to discover high-impact AI opportunities and map existing technical reality directly to enterprise growth targets.

Discover AI opportunities ->

Prioritize opportunities based on speed to market and technical feasibility.

Participate in hands-on exercises led directly by Pythian's Field CTO team to evaluate candidate AI opportunities. Map core legacy and cloud systems to determine exact integration points across your architecture while eliminating unoptimized compute costs.

Evaluate technical feasibility ->

Systematically resolve technical debt to prepare pipelines for AI execution.

Identify the exact database, landing zone, and pipeline modernization prerequisites required to support advanced AI models. Secure complete data clarity and lineage audits upfront to guarantee underlying systems remain resilient, safe, and ready for deployment.

Audit data readiness ->

Hardwire corporate guardrails to guarantee safe model adoption.

Establish clear governance frameworks, security boundaries, and ethical guardrails to protect enterprise data assets. Ensure production AI models operate deterministically, eliminating compliance risks, hallucinations, and ungoverned data access.

Establish governance guardrails ->

Walk away with a validated, risk-mitigated AI implementation roadmap.

Obtain a concrete, phased tactical timeline mapping immediate quick-wins to long-term operational integration. Secure a full technical prerequisite map, data readiness assessment, and engineering strategy to move AI out of the sandbox and into production.

Schedule your session ->

Paul Lewis

CTO, Pythian

Karen Pfiefer

Field CAIO and CDO, Pythian

Ernest Solomon

Field CISO and CTO, Pythian

Jeff DeVerter

Field CTO, Pythian

Frequently asked questions (FAQ) about Data-Centric AI Workshop

General management consultancies deliver theoretical slide decks outlining high-level business concepts without addressing technical execution. Pythian brings nearly three decades of operational data engineering heritage to evaluate your actual data pipelines, database architecture, and infrastructure feasibility, delivering a concrete execution blueprint built on operational reality.

You receive a comprehensive AI readiness diagnostic assessment, a prioritized use case matrix balancing technical feasibility against speed to market, and a complete technical blueprint detailing system landing zones, data prerequisites, and a phased deployment roadmap.

We bring together executive sponsors (CIO, CTO, CDO), technical decision-makers (Directors of Data Engineering, Enterprise Architects), and operational line-of-business leads (COO, VP of Operations). This cross-functional participation collapses months of internal planning debate into actionable execution alignment in one to three days.

Pythian conducts a preliminary alignment and discovery phase before the on-site engagement. We distribute a diagnostic survey to your key business and IT stakeholders to analyze current tool usage, data bottlenecks, and operational goals, ensuring the live session is meticulously tailored to your specific environment.

Pythian’s workshops are executed directly by our Field CTO Team—seasoned, executive-level practitioners. Your leadership team collaborates directly with C-suite and engineering experts—including Field CTOs, Chief Field AI Officers, and CISOs—bringing real technical, operational, financial, and compliance perspectives into the room to align your strategy with your growth targets.

We measure workshop success through three precise lenses:

Operational Efficiency (targeting a 30-50% reduction in manual task times),
Strategic Clarity (delivering a finalized roadmap with measurable KPIs)
Team Upskilling (evaluating your organization's capability to safely flag and manage future AI opportunities via pre- and post-workshop assessments).

We offer customized, department-specific modules that are available globally via interactive digital whiteboarding or in-person sessions at your corporate headquarters. Depending on your organization's depth of need, you can choose between two main formats:

The Intensive (a 1-day session focused on rapid ideation and initial prioritization)
The Deep Dive (a 2-to-3-day engagement delivering full roadmap development, risk assessments, and data governance).

Data-Centric AI Workshop

Eliminate legacy data debt and build the high-performance data foundation required to deploy production-ready AI.

Audit enterprise data debt.

Unify fragmented data estates.

Enforce strict data governance.

How we work with you

Evaluate current data infrastructure and identify high-value use cases.

Prioritize opportunities based on speed to market and technical feasibility.

Systematically resolve technical debt to prepare pipelines for AI execution.

Hardwire corporate guardrails to guarantee safe model adoption.

Walk away with a validated, risk-mitigated AI implementation roadmap.

Led by our team of CAIO, CDO, CISO, CIO and CTOs.

Walk out of the AI workshop with a validated, production-ready roadmap designed for immediate business impact.

1-3

Day commitment

Build a high-performance data foundation to deploy production-ready AI faster.

Frequently asked questions (FAQ) about Data-Centric AI Workshop