Consolidate data from multiple systems, define shared master data and build a corporate analytical model for business-unit reporting.
This historical airline analytics proof of concept started with three explicit needs: extract data from different systems, define the master data consumed across entities and business functions, and build a corporate data model that could serve different business units.
Power BI was used as the reporting layer over that model.
The case is valuable because the business problem was not “build a dashboard.” It was establish a common data structure that different parts of the organization could use for reporting.
That is a reusable Para analytics pattern for organizations where multiple systems and business units need a shared management-information foundation.
When multiple business units depend on the same information, master data and a shared corporate model matter more than the first dashboard.
Why this matters
Large transportation organizations often operate multiple systems, each optimized for a different function. Reporting becomes difficult when the same business concepts are represented differently across those systems.
The proof-of-concept requirement explicitly recognized that problem. Data had to be extracted from different systems, common master data had to be identified and a corporate data model had to serve multiple business units.
That sequence is significant: the model creates common meaning before Power BI becomes the presentation layer.
For Para, the case demonstrates data architecture thinking as part of BI delivery rather than treating analytics as front-end report design.
- Data extraction from multiple organizational systems.
- Identification of master data shared across entities and business functions.
- Corporate data model serving different business units.
- Power BI reporting over the common analytical structure.
- Reusable pattern for cross-functional analytics in complex organizations.
Airline Analytics POC Pattern
Multiple Source Systems
Master Data
Corporate Data Model
Power BI
Business Unit Insight
What the work looks like
Identify the source systems and shared business concepts
The POC begins by mapping the systems that contribute data and the concepts multiple business units need to interpret consistently.
This distinguishes master data from local operational detail.
Define the master-data layer
Shared entities and reference structures are defined so different business functions can work from the same basic meaning.
Build the corporate data model
The analytical model structures data so multiple business units can consume it without each building a separate interpretation.
This provides the common foundation the reporting layer needs.
Use Power BI as the consumption layer
Power BI exposes the model through business-unit reporting and visualization.
The dashboard therefore sits on top of a defined data architecture rather than becoming the place where data structure is improvised.
Para point of view
Master data is a BI concern when reports share entities
If different business units use the same concepts, the reporting architecture needs common definitions.
A corporate model reduces duplicated interpretation
Each team should not have to independently reconstruct the same relationships from raw source data.
POCs should prove the model, not pretend to be production
The value of a POC is validating the data and reporting approach within a controlled scope.
Power BI is the final layer, not the whole solution
The quality of the dashboard depends on the source and model work underneath it.
What was delivered
Source-system map
The systems and data domains included in the proof-of-concept scope.
Master-data definition
The shared entities and reference concepts needed across business functions.
Corporate data model
A structured analytical model intended to serve multiple business units.
Power BI views
Reporting over the common data model for the approved POC audience.
POC findings
What the prototype demonstrated about data availability, shared definitions and reporting feasibility.
Expansion considerations
The additional governance, engineering and operating work required before broader production scale.
Where it fits
This showcase demonstrates Para's data-architecture approach inside an analytics engagement.
Transportation / Airline
Industry context for multi-system, multi-business-unit reporting.
Data Architecture
Master data and corporate analytical model design.
Data, BI & Analytics
Power BI over a structured data model.
Enterprise Architecture
Cross-system data relationships and business-unit consumption.
Explore related capabilities
Related advisory
- Data & BI Advisory
- Enterprise Architecture & Roadmapping
- Data Governance
Related solutions
Related platforms & assets
- Master-data patterns
- Corporate analytical-model patterns
- Power BI reporting patterns
Need reporting across multiple systems and business units without creating a different data definition for each one?
Talk to Para about the common business concepts, source systems and reporting audiences. A focused proof of concept can validate the data model before a wider analytics program is committed.