With Microsoft Fabric, a Canadian charter operator cut monthly reporting by 20 hours and improved flight profitability 5%
Elara unified Business Central and flight operations data in Microsoft Fabric, giving executives daily numbers, faster reporting, and AI ready insight from one governed source of truth.
Executive summary
Every month, the leadership team at a Canadian charter aviation operator waited for the same ritual to play out. Finance exported data from Microsoft Dynamics 365 Business Central, three departments stitched their own Excel workbooks together, and the numbers that finally reached the boardroom were already weeks old. Different teams often arrived with conflicting versions of the same figure, so meetings began with a debate about whose spreadsheet was right.
To close that gap, the operator worked with Elara to unify its data in Microsoft Fabric. Business Central and the flight operations systems now feed a governed lakehouse in OneLake, organized in Bronze, Silver, and Gold layers, with validated data and automated refresh. On that foundation sits an executive Power BI reporting suite led by a single Executive Summary page, with drill through to transaction detail, and AI data agents that answer questions on governed data.
The results reached the bottom line. Clean, trusted data helped lift profitability on flights by 5 percent, four siloed systems were consolidated into one platform, and more than 20 hours of manual reporting effort disappeared every month. Executives now open daily refreshed numbers instead of waiting for month end.
+5%
profitability on flights through clean data management
4
siloed systems consolidated into Fabric
20+
hours of monthly manual reporting eliminated
Daily
refreshed numbers instead of month end lag
The customer
Video coming soon
A walkthrough of the executive reporting suite, shown with sample data.
A Canadian charter and aircraft management operator who runs a diverse fleet on Microsoft Dynamics 365 Business Central, with flight operations, maintenance, and finance each generating their own stream of data.
The operator manages a mixed fleet on behalf of owners while flying charter work across Canada, so every aircraft splits its time between owner trips and revenue flights. Each booking becomes an itinerary broken into legs, each leg carries a pilot in command and a second in command, and each tail number carries its own costs. With a lean office team coordinating all of it, knowing where an aircraft stood on any given day was daily operational work, not a quarterly exercise.
Held back by siloed systems
Leadership needed to see the business as one picture: revenue, aircraft utilization, maintenance cost, and financial performance. Instead, monthly reconciliation and analysis meant exporting Business Central data to Excel, using complex formulas to match workbooks from three departments, and presenting numbers that were rarely accurate or on time. Different teams brought different versions of the same figure to the same meeting.
The detail behind the numbers lived in different places. Crew assignments and flight legs sat in the operations system, while quotes, customer accounts, and invoices sat in Business Central. When leadership asked how a given aircraft performed, schedulers exported leg records, finance exported ledger entries, and both rebuilt the same totals side by side. Records with a missing tail number or an unassigned crew slipped into the counts unnoticed, so two careful people could produce two defensible answers. The lean team spent evenings reconciling exports rather than analyzing them, and decisions waited until the versions finally agreed.
“I used to dig through exports to answer basic questions about who flew what. Now I open one page and it is just there, and I trust what I am looking at.”
Fleet Operations Manager
The decision to move to Fabric
The operator was already running Business Central, so the question was never which ERP to use. It was where the reporting should live. Adding more spreadsheets would have deepened the problem, and a standalone reporting tool would have meant another system for a lean team to maintain. Microsoft Fabric offered one platform that sat naturally beside Business Central, connected the flight operations data, and covered storage, modeling, and reporting without new infrastructure. The operator engaged Elara to build it. The first working sessions focused on a field mapping exercise, tracing every source table and column into agreed report names, so that quotes, accounts, itineraries, legs, and crew assignments would land in the model once and mean the same thing everywhere.
Building the foundation
We connected Microsoft Dynamics 365 Business Central, the operator's ERP, and their flight operations systems to Microsoft Fabric, then built up from there.
Connect the sources
Business Central and the flight operations systems feed Microsoft Fabric directly, with no more manual exports.
Build the lakehouse
A lakehouse in OneLake stages, cleans, and models the data in clear layers, following Fabric best practices.
One governed source of truth
Everything lands in one model with automated refresh and validation, so a figure means the same thing in every report.
Reports and AI on top
Executive Power BI reports and AI data agents sit on the unified model, so the team can ask a question and trust the answer.
The reporting suite covers financial performance by entity and department, charter revenue and utilization by aircraft, and maintenance cost tracking, with page navigation from the executive page to transaction level detail. The reports and the AI agents are the payoff. The foundation underneath is what makes them dependable.
Microsoft Fabric OneLakeBronze raw, Silver cleaned, Gold modeled
One Source of TruthValidated, Automated refresh
DestinationsExecutive Power BI ReportsAI Data Agents
Business Central, Flight Ops System, Data Warehouse and Dataverse feed data in
Microsoft Fabric OneLake refines the data through Bronze, Silver and Gold layers
One Source of Truth, validated with automated refresh
Executive Power BI Reports and AI Data Agents consume the governed data
Executive Power BI Reports
AI Data Agents
The data engineering behind it
Behind the dashboards sits a standard Microsoft Fabric engineering setup, built so the operator's team can run and extend it without specialist help. Each piece has one job, and together they carry data from the source systems to the numbers executives read each morning.
Scheduled pipelines in Fabric ingest Business Central, the flight ops system, the historical warehouse, and Dataverse into OneLake
Raw data lands in Bronze exactly as received, preserving history
Silver applies the field mapping, conforms names and types, and runs validation rules such as excluding records with missing tail numbers or crew
Gold holds business ready tables modeled for reporting
A semantic model feeds Power BI and the AI data agents from the same tables
Refreshes run automatically on schedule with monitoring, and no manual steps
Technologies used
Microsoft Fabric
Power BI
Data Factory
Business Central
AI Foundry
Dataverse
Microsoft Fabric
Power BI
Data Factory
Business Central
AI Foundry
Dataverse
From month end lag to daily visibility
The change showed up quickly. The executive team now opens one dashboard every morning instead of waiting for month end emails, with numbers refreshed daily. Utilization reporting surfaced routing and scheduling opportunities that helped improve profitability on flights by 5 percent. Four siloed systems were consolidated into Fabric (Business Central, the flight ops platform, their historical data warehouse, and Dataverse) and the finance team stopped rebuilding spreadsheets. The monthly reporting grind shrank by more than 20 hours. Every department now works from the same numbers.
Aircraft utilization is a live example. When a review of leg level activity showed where scheduling was leaving revenue on the ground, the team could act on it that week rather than after the next month end. The monthly meeting changed tone as well. With sales context from quotes sitting beside operational results in the same model, the conversation starts at why the numbers moved, because nobody argues anymore about what the numbers are.
