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GIS / Analytics Platform

Building GIS analytics as a platform capability

Allps Digital

Enterprise GIS work that combined geospatial analytics, platform capabilities and cloud/ML delivery for operational decision support.

2× Model-training throughput+20% Model accuracy

01 Context

Allps Digital. Formal title: Platform Product Lead — GIS & Analytics.

02 Problem

GIS and geospatial analytics needed to support operational decisions as platform capabilities rather than isolated modelling work.

03 Users / Stakeholders

  • Operational users of geospatial analytics
  • Engineering and ML counterparts
  • Stakeholders defining enterprise GIS requirements

04 My Scope

Building an AI-driven GIS analytics suite, including ML delivery, services and model tracking. Formal title: Platform Product Lead — GIS & Analytics.

05 Product Decisions

  • Frame GIS analytics as a platform capability for operational use, not only a modelling exercise
  • Invest in training throughput and accuracy where they improved decision support
  • Use model tracking so capability quality could be operationalised

06 Technical Constraints

Cloud/ML delivery, services and model lifecycle constraints. Internal architecture is not expanded.

07 Trade-offs

  • Model sophistication versus operational throughput
  • Accuracy gains versus training cost and complexity
  • Platform generality versus immediate operational needs

08 Measurement

Model-training throughput and model accuracy.

09 Outcome

2× model-training throughput and +20% model accuracy.

10 Reflection

The product-relevant work was treating GIS analytics as a platform capability for operational users, not only as a modelling exercise.

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