What CFOs and data leaders need to know about Microsoft's unified data platform; and why it changes the economics of financial data governance.
For most of the past decade, finance teams have had to navigate a fragmented technology landscape. Data from your ERP sat in one system. Reporting lived in another. Reconciliation happened in a third, usually a spreadsheet. Each handoff introduced risk: mismatched schemas, stale extracts, and the slow accumulation of data debt that makes month-end close feel like archaeology.
Microsoft Fabric is Microsoft's answer to that fragmentation. Announced in 2023 and now generally available, Fabric is a unified analytics platform that brings together data engineering, data warehousing, data science, real-time analytics, and business intelligence under a single Software-as-a-Service (SaaS) umbrella. It is built on top of OneLake (a single, organization-wide data lake hosted in your Azure tenant) and it integrates natively with the tools your teams already use: Excel, Power BI, and the broader Microsoft 365 ecosystem.
The headline benefit is deceptively simple: one place where all your data lives, governed consistently, with one set of permissions, one audit trail, and one bill. For finance leaders who have spent years managing the cost and complexity of point solutions, that proposition deserves serious attention.
The key distinction from Azure Synapse or traditional data warehouses: Fabric is not another infrastructure product you hand to IT and wait six months to see results. It is a managed platform with finance-friendly abstractions, connecting your ERP data to governed analytics without requiring a data engineering team to build the plumbing from scratch.
Finance has always been a data-intensive function. Every CFO knows this intuitively. What has changed is the gap between the volume of financial data organizations now produce (across multiple ERPs, cost centres, and geographies) and the tools available to manage it. Spreadsheets and legacy BI tools were not designed for the data volumes or governance requirements that modern finance functions face.
Microsoft Fabric addresses this gap by collapsing three problems simultaneously: data storage cost, data access complexity, and analytical latency. Rather than maintaining separate licences and integrations for a data lake, a warehouse, a BI tool, and an ETL orchestration layer, organizations on Microsoft 365 can consolidate onto Fabric and pay per capacity unit, a model that typically reduces total cost of ownership for mid-market companies by 30 to 50 percent compared with assembling equivalent capability from point solutions.
For PE-backed portfolio companies (where finance teams are often lean and the pressure to report accurately across multiple entities is intense) this consolidation has a compounding effect. Close cycles that once required four days of manual reconciliation across disconnected systems can be reduced to overnight pipelines with live Power BI reporting. That is not a technology upgrade; it is a structural change to how your finance organization operates.
Most mid-market CFOs are already paying for Microsoft 365. Fabric is included in or incrementally priced above existing enterprise agreements, which means the licence conversation is often simpler than organizations expect. More importantly, Fabric connects directly to the collaboration tools finance teams use every day (Teams, Excel, SharePoint) so adoption friction is lower than with greenfield analytics platforms.
Governance is not a technology problem; it is an accountability problem that technology either helps or hinders. Most legacy analytics architectures hinder governance by design: data moves between systems through pipelines that are difficult to audit, permissions are managed inconsistently across tools, and lineage (the ability to trace a number in a board report back to its source transaction) is effectively nonexistent.
Fabric changes the governance equation in three concrete ways that finance leaders should understand.
Because all Fabric workloads read from OneLake, every transformation and every report output is automatically traceable to the source data. When an auditor asks where your AR aging number came from, the answer is not "we think it came from this extract, which was run by someone who is no longer with the company." The answer is a documented, automated lineage graph that shows every step from ERP transaction to board dashboard.
Audit readiness as a by-product: For PE portfolio companies preparing for exit, Fabric's native lineage capability turns audit preparation from a multi-week project into a report generation task. The documentation is produced as a side effect of building the analytics, not as a separate workstream.
In traditional data architectures, access control requires IT involvement for every change. Fabric's permission model is integrated with Microsoft Entra ID (formerly Azure Active Directory), which means finance leadership can manage access to sensitive financial data using the same tools they use to manage email and Teams access. A new VP of Finance can be given access to the correct datasets on their first day, without a help-desk ticket.
Perhaps the most significant governance benefit is architectural. Because Fabric enforces data quality rules at the point of ingestion (before data reaches any reporting layer) errors are caught upstream rather than discovered in a board meeting. For companies running multiple ERP instances, this means cross-system inconsistencies in supplier naming, account codes, or currency handling are surfaced and resolved in the data pipeline, not in a spreadsheet at month-end.
Microsoft Fabric provides the infrastructure for unified, governed financial data. What it does not provide is domain-specific intelligence about the financial problems that cost organizations the most money: duplicate vendor records, erroneous payments, and unreliable working capital metrics. That is the layer DataQubi adds.
DataQubi is deployed entirely within your Azure tenant, running on top of your Fabric environment. There is no external data transfer, no new SaaS vendor to onboard, and no change to your existing ERP systems. The platform reads supplier, invoice, and payment data from your Fabric data warehouse, applies AI-assisted analysis, and writes structured intelligence outputs back into the same environment, making findings immediately available in Power BI and audit-ready for your finance team.
A mid-market manufacturer running SAP and NetSuite across two acquired entities consolidates both ERP environments into Fabric via standard connectors. DataQubi ingests the unified supplier master (14,000 records) and identifies 1,840 duplicate entries created during the acquisition integration. Cross-referencing those duplicates against payment history surfaces $680,000 in recoverable overpayments. The entire process takes 14 days. The output is a structured remediation report and a live Power BI dashboard showing supplier master health, updated in real time as ERP data flows into Fabric.
Supplier Intelligence: Using fuzzy-matching algorithms running in Azure Databricks, DataQubi identifies duplicate vendor records (accounting for variations in legal name, address format, tax ID, and banking details) and creates consolidated "golden records" that give AP teams a single, reliable view of every supplier relationship.
Duplicate Payment Detection: Beyond vendor deduplication, DataQubi cross-references invoice and payment history to flag suspicious transactions: invoices paid twice under different vendor IDs, payments made against voided purchase orders, and remittances that do not match approved payables. Each finding is scored by recovery probability and recovery complexity, giving finance teams a prioritised action list rather than a raw anomaly report.
Spend Classification and Visibility: Accurate spend reporting requires clean supplier data. Once vendor records are deduplicated, DataQubi classifies supplier spend by category (normalising descriptions across ERP systems and business units) so CFOs can see true off-contract spend and consolidation opportunities for the first time.
Working Capital Metrics: Standardised AR aging schedules, DSO calculations, and payment terms analysis are produced as structured Fabric datasets, enabling benchmarking across business units and meaningful comparison against industry peers. For PE-backed companies, these metrics feed directly into the working capital reporting that investors and lenders require.
On the question of data security: CFOs frequently ask whether deploying an analytics layer introduces new data exposure risk. Because DataQubi runs exclusively within your Azure tenant (reading from and writing to your Fabric environment) there is no data transmission to external servers. Your financial data never leaves the security perimeter you already manage.
The practical question for most finance leaders is not whether a unified data platform is strategically valuable; it clearly is. The question is whether the investment of time and internal resource to get there is justified given everything else competing for attention. Our experience with mid-market and PE-backed companies suggests the answer depends on one variable: the quality of your current ERP data.
Companies with clean, well-governed ERP data can deploy Fabric and DataQubi quickly, using the platform primarily to consolidate reporting and accelerate close cycles. Companies with messy data (multiple ERP instances, legacy supplier masters, manual reconciliation processes) benefit most immediately from the data hardening that DataQubi provides, because clean data is the prerequisite for everything else Fabric can do.
Our 14-day engagement is designed to make the value tangible before any infrastructure commitment is made. We extract a read-only snapshot of your supplier, invoice, and payment data, run DataQubi analysis within your existing Azure environment, and return a structured report showing exactly where the money is and what it would take to recover it. Most clients see first value (a quantified list of duplicate vendors and recovery opportunities) by Day 7.
The CFOs we work with did not come to us because they were looking for a technology project. They came because they had a board meeting where a working capital number could not be explained, or an audit that surfaced a duplicate payment, or an acquisition that created supplier master chaos. Fabric and DataQubi do not make those problems disappear; they make them visible, quantifiable, and solvable.
If you are responsible for financial data quality in your organization and have not yet evaluated what a unified Azure-native analytics layer could do for your close cycle, your audit readiness, or your working capital metrics, the 14-day process is a low-risk way to find out exactly what is hiding in your data.
20 minutes. No pitch deck. Just a look at your supplier data and where cash is hiding.