At A Glance
Industry – Entertainment, Media, & Broadcasting
Capability – Data Engineering & Platform Architecture
Solution – Centralised data platform and shared enterprise data models
The Challenge – Improve consistency and reliability across disconnected reporting and analytics environments.
The Outcome –
- Established shared business logic and standardised data models
- Reduced duplication across teams and reporting processes
- Improved confidence in analytics and operational reporting
Background
This major entertainment company operates across numerous business units, each had independently developed their own reporting models and data approaches over time. With no shared platform or agreed business logic, teams worked in isolation – building parallel versions of the same logic and producing metrics that were difficult to maintain, compare or trust.
The Challenge
The customer needed a more consistent way to structure and manage data across the business. Without a shared platform and agreed data models, reporting outcomes varied between teams, analytics work was repeatedly duplicated, and scaling future AI or advanced analytics initiatives would become increasingly difficult.
What KJR delivered
Our Role:
KJR established and led the data transformation capability, building a shared platform and structured modelling approach that gave teams a more reliable and consistent view of operational data.
Key Contributions:
- Designed and implemented a centralised BigQuery data transformation platform with a complete CI/CD pipeline
- Designed and implemented a generalised, configuration-driven modelling methodology to standardise all data model build process
- Implemented a data-driven quality framework embedded directly into modelling workflows
- Designed and implemented bespoke job orchestration, monitoring, and alerting infrastructure to support reliable platform operations (this includes integration with Microsoft Teams, Email Notifications and Data Studio Dashboards)
- Supported partner teams through hands-on delivery guidance and targeted capability uplift and technical training
How We Did It
Discover & Assess
- Reviewed existing reporting approaches and team workflows
- Identifying limitations of legacy data system
- Designing a platform with focus on extensibility and transparency
Assure & Validate
- Standardise data modelling and quality practices
- Integrate quality practice directly into modelling workflows
- Partnering with internal teams and subject matter experts inside the organisation to ensure business ownership of the data model
Release with Confidence
- Adopting robust devops methodologies throughout delivery
- Built platform that is transparent, accessible, secure andextensible
- Reduce operational complexity and maintenance burden through CI/CD, automation and AI
Impact and Business Outcomes
- Improved consistency of reporting and analytics outputs across teams
- Reduced duplicated effort in data preparation and modelling
- Faster turnaround for operational reporting and analysis
- Increased confidence in decision-making data
- Established a clearer foundation for future analytics and AI initiatives
Why KJR ?
- Ability to simplify complex data environments without over-engineering
- Strong mix of engineering, delivery, and operational assurance capability
- Focus on usable data outcomes rather than architecture for its own sake
Technologies Validated
The following technologies were validated as part of the engagement:
Google Cloud Platform:
- BigQuery
- Data Studio (dashboards)
- Cloud Run Functions
Terraform (CI/CD
