From Field to Dashboard — Real-Time Asset Intelligence With Fulcrum & Power BI

Mobile data collection · Spatial analysis · Risk management · Power BI reporting

The Challenge

A stakeholder group needed a reliable, end-to-end system to capture, manage, and monitor critical asset and risk data across a dispersed field operation. Existing processes were fragmented, manual, and couldn't keep pace with the reporting demands of a complex, multi-party project.

What We Did

Designed and built a custom Fulcrum workflow tailored specifically to the stakeholder's operational requirements and approval processes. The solution enabled field teams to capture key asset data and risk information on the ground — accurately, consistently, and without the lag of paper-based systems.

Spatial analysis was embedded directly into the workflow, giving teams the ability to assess and interrogate location data on the fly — right where the work was happening. Automatic data collection rules ensured nothing fell through the cracks, reducing manual handling and human error at the point of capture.

On the reporting side, a live Power BI dashboard brought everything together — giving stakeholders a single, always-current view of project progress, emerging risks, and cost tracking. No more chasing updates. No more stale spreadsheets.

The Result

Stakeholders stayed informed at every stage. Field teams worked faster and with greater confidence. And the organisation had a repeatable, scalable data collection and reporting system built around the way they actually work.

Seeing the Full Picture — Custom Python Dashboarding for Retail Intelligence

Python development · Plotly · Machine learning · Sales analytics · Business intelligence

The Challenge

A retail client needed more than off-the-shelf reporting. They had sales data, stock levels, and website usage metrics sitting in silos — and no clear way to see how it all connected, or how it tracked against the targets that actually mattered to the business.

What We Did

Developed a fully custom dashboarding system built in Python, using Plotly to deliver rich, interactive visualisations tailored to the client's specific data environment and reporting needs.

Machine learning modules were integrated into the platform to surface patterns and trends that standard reporting would miss — giving the client a layer of analytical depth that goes well beyond what any templated tool could offer.

Sales data was broken down across a variety of lenses — by product, category, time period, channel, and region — allowing the business to interrogate performance from every angle. Stock levels and website usage data were woven into the same view, creating a genuinely connected picture of how the business was operating day to day.

Critically, every metric was tied back to the business's own targets and goals. Rather than reporting in isolation, the dashboard kept the client anchored to what they were actually trying to achieve — flagging where they were on track, where attention was needed, and where opportunity was being left on the table.

The Result

The client gained a single intelligent platform that replaced scattered reports and manual analysis. Decision-making became faster, better informed, and grounded in a complete view of the business — from the warehouse shelf to the website visit.

Precision From Point Clouds — LiDAR Asset Collection & Clearance Analysis

Terrestrial LiDAR · LiDAR 360 · Python · Spatial analysis · Geodatabase management

The Challenge

A client needed an accurate, structured record of physical assets and vegetation across a project area — captured with precision, enriched with meaningful detail, and stored in a way that could support ongoing spatial decision-making. On top of that, they needed to understand the potential impact of planned works on those assets before anything hit the ground.

What We Did

Deployed terrestrial LiDAR scanning to capture high-density point cloud data across the project area. Using LiDAR 360 alongside custom-built Python scripts, we processed the raw scan data to extract vegetation and physical assets directly from the point cloud — cleanly, accurately, and at scale.

Extracted features were converted into precise 2D positional data and uploaded into a custom structured geospatial database, purpose-built to hold and serve the information in a consistent, queryable format. Each asset and vegetation feature was then enriched with measurements, classifications, and other key attributes — turning raw geometry into genuinely useful, decision-ready records.

With the asset database established, DataTroop performed detailed clearance analysis across the project area. This gave the client a clear spatial understanding of how proposed works would interact with existing assets — identifying potential conflicts, flagging risk areas, and providing the evidence base needed to plan works with confidence before committing to the field.

The Result

The client moved from guesswork to precision. A complete, enriched spatial record of assets and vegetation replaced manual survey methods, while the clearance analysis gave project teams and stakeholders the foresight to plan works safely, efficiently, and with full awareness of what was at risk.