CASE STUDY | CHEMICALS: MINING AND AGRICULTURE | MODERN DATA PLATFORM
Transforming fragmented legacy systems into a scalable, trusted foundation for analytics and data-driven decision-making
Solution overview
INDUSTRY
Chemicals: Mining and Agriculture
SOLUTION
Azure Databricks
LOCATION
South Africa
CLIENT
Omnia
DELIVERY DURATION
2-3 years (Phased Rollout)
Requirement
Omnia needed to modernise a legacy, siloed data estate that was limiting visibility and slowing decision-making. The objective was to establish a scalable, enterprise-wide analytics platform that enables governed access to trusted data, supports advanced insight generation, and creates a foundation for consistent, organisation-wide intelligence
Solution
• Modern, Scalable Data Architecture
• Automated and Reliable Data Pipelines
• AI Enablement
• Data Quality and Governance
• Operational Excellence
Impact
• Scalable analytics foundation established
• Trusted data products delivered
• Operational efficiency and performance improved
• Formal data quality governance implemented
The Client
• Context: Need to modernise a legacy, siloed data estate to enable scalable, enterprise-wide analytics.
The Challenges
Legacy and fragmented systems limiting scalability and enterprise-wide insight
Siloed data with minimal integration across business units
Manual, time-consuming processes slowing reporting and decision-making
Frequent errors and inconsistencies undermining trust in data
Limited analytics capability restricting advanced insights and predictive decision-making
The Solution
Modern, Scalable Data Architecture
A Lakehouse with Medallion Architecture was implemented on Databricks, enabling cross-domain data use, unified access, and integration across previously siloed systems.
Automated and Reliable Data Pipelines
Key processes were refactored to reduce manual effort, pipelines were optimised for speed and reliability, and errors in data processing were eliminated.
AI Enablement
AI capabilities were introduced using Genie to generate insights over curated, trusted data, with the further goal of enabling a self-service BI model for broader enterprise use.
Data Quality and Governance
A data quality framework with automated checks was established for master data management, sensitive data was classified for governance, and certified data products were delivered.
Operational Excellence
DevOps pipelines were implemented to standardise builds and deployments, and financial operations processes were implemented to improve control and visibility of spend.
The Outcome
Scalable analytics foundation established – enabling cross-domain insights and future advanced analytics.
Trusted data products delivered – providing reliable, certified information across core business areas.
Operational efficiency and performance improved – automated pipelines reduced manual effort and errors.
Formal data quality governance implemented – ensuring consistent, accurate data to support confident decision-making and scale analytics adoption.
Customer Testimonial
The reality is that moving away from legacy, fragmented systems is never straightforward. This solution has provided a solid foundation, transforming how we work with data and positioning us as an insight-driven organisation. There is still more to do, but we are confident we have the capabilities to succeed today and scale into the future.
Gert Van der Walt, Head of Data & Analytics
Technology
