Insights

Explore Rohari Group perspectives on real public case studies in data engineering, geospatial intelligence and digital transformation. These examples are referenced to illustrate market direction, delivery patterns and practical lessons for organisations building modern data capabilities.

What Aegea Shows About Modern Data Platforms

Industry example: Aegea with Microsoft

Microsoft highlights how Aegea built a more unified Azure data foundation to support faster reporting, stronger operational visibility, and broader AI readiness across essential services. The practical takeaway is to design the platform first, then layer analytics and automation on top of trusted shared data.

Rohari view: organisations should prioritise a shared operating data layer before expanding dashboards, AI, or automation initiatives.

5 min readData StrategySource: Microsoft

Geospatial Intelligence in Infrastructure Planning

Industry example: Burgess and Niple with Esri

Esri showcases how Burgess and Niple used geospatial digital twin methods for water infrastructure planning, giving teams a more complete view of network condition, project timing, and investment choices. For firms in asset-heavy sectors, the recommendation is clear: combine mapping, engineering data, and scenario analysis in one operating view.

Rohari view: infrastructure organisations get the strongest return when GIS is connected to engineering, maintenance, and capital planning decisions rather than used as a standalone map layer.

4 min readGeospatialSource: Esri

Automation Lessons From Experian Modernization

Industry example: Experian with AWS

AWS describes how Experian used AWS Transform to accelerate portions of mainframe modernization and reduce manual effort in complex engineering work. The broader insight is that automation creates the most value when it removes repeatable technical toil while leaving architecture and governance decisions with the delivery team.

Rohari view: automation should first target repetitive engineering work, handoffs, and validation steps that slow delivery across large data estates.

6 min readAutomationSource: AWS

Governance at Scale: Covestro and Data Product Control

Industry example: Covestro with AWS

AWS documents how Covestro moved toward a governed data mesh with Amazon DataZone, improving discoverability, ownership, and quality controls across a large estate of pipelines. A strong recommendation for growing firms is to treat governance as a product experience, not just a compliance layer, so teams can find and trust data faster.

Rohari view: governance frameworks work best when ownership, metadata, and access workflows are simple enough for delivery teams to use without friction.

7 min readGovernanceSource: AWS

Public Sector Service Design With Location Intelligence

Industry example: Vilnius with Esri

Esri reports that the City of Vilnius used drones, GIS, and AI-supported analysis to improve how urban services are monitored and managed. The lesson for public institutions is to connect field capture, spatial analysis, and service planning so decisions reflect what is actually happening on the ground.

Rohari view: public sector data programmes are strongest when field evidence, spatial monitoring, and planning teams work from the same operational picture.

5 min readPublic SectorSource: Esri

Turning Customer and Asset Data Into Operational Insight

Industry example: EDF Energy with Snowflake

Snowflake features how EDF Energy built a more connected customer intelligence environment to support decision making across teams. For organisations pursuing better operational insight, the recommendation is to align analytics around measurable decisions such as retention, service performance, and forecasting rather than dashboards alone.

Rohari view: analytics programmes should be measured by the quality and speed of decisions they improve, not by reporting volume alone.

6 min readAnalyticsSource: Snowflake