Data analysis & BI
Data analysis that connects location, risk and decision-making.
GeoTelligence combines spatial analysis, risk scoring, exploratory data analysis and BI dashboard design into analytical outputs that decision-makers can act on and regulators can scrutinise.
Analytical outputs
From raw data to decision intelligence
The following panels show synthetic examples of the kind of analytical output GeoTelligence produces. All data is illustrative.
Assets indexed
2,847
+142
High-risk assets
156
+12
Data quality score
94%
+2%
Open recs
423
-18
Overdue actions
38
+4
Routes analysed
12
—
Risk matrix (5×5)
High-risk assets by route
Synthetic data · Illustrative only
Spatial analysis summary
Synthetic data · Illustrative only
Data pipeline
Source data
Access · Excel · API
Ingest + validate
Python · pandas
Transform + model
SQL · PostGIS
Spatial join
PostGIS · buffer
Risk score
Python · matrix
Dashboard + export
Next.js · BI tool
Analytical capabilities
Six areas of analytical practice
Exploratory data analysis
The first step in any engagement is understanding what data exists, what quality it is and what it can reliably tell us. Poor data quality is not a barrier to analysis — it is part of the analysis.
Data profiling
Row counts, field completeness, value distributions, format consistency
Missing data analysis
Systematic identification of gaps by field, record type and source system
Duplicate detection
Record-level and field-level duplicate identification with confidence scoring
Outlier detection
Statistical and domain-specific outlier flagging — risk scores, dates, dimensions
Trend discovery
Time series analysis of examination dates, recommendation creation and closure rates
Data quality scoring
Composite quality score per asset and dataset with prioritised remediation list
Spatial analysis
Location changes the analytical picture. Assets do not exist in isolation — they exist in networks, near water, in flood zones and along routes with operational consequences.
Asset clustering
Geographic groupings of assets by condition, risk or type for targeted programmes
Route and region analysis
Risk and condition distribution by route, region, operational area or portfolio
Proximity analysis
Assets within a given distance of watercourses, known scour sites, flood zones
Flood and watercourse
Spatial join of asset locations to flood zone and watercourse datasets
Hotspot mapping
Concentration of high-risk or overdue assets in spatial clusters
Density and coverage
Examination coverage density, inspection gaps and geographic blind spots
Risk and prioritisation analysis
Risk analysis turns a list of assets into an ordered programme. The output is not just a risk score — it is a defensible, evidence-backed prioritisation that can be presented to decision-makers and regulators.
Risk matrix design
Domain-appropriate 5×5 or custom matrices with likelihood and consequence axes
Severity and likelihood
Component-level scoring with aggregation and weighting rules
Criticality scoring
Asset criticality combining structural risk, operational consequence and exposure
Backlog prioritisation
Ordered programme with risk-adjusted priority, urgency and resource constraint
Overdue identification
Assets overdue for examination, assessment or recommendation action
Consequence modelling
Route-level and operational consequence of asset failure or closure
BI dashboards
Analytical findings need to be communicated to different audiences — engineers, operations managers, executives and regulators. Each needs a different view of the same underlying data.
Tableau
Interactive dashboards, geospatial maps, KPI panels and filtered views
Power BI
Microsoft ecosystem integration, scheduled refresh and enterprise sharing
Looker Studio
Google ecosystem, rapid prototyping, embedded dashboards
Custom web dashboards
Next.js + React dashboards embedded in the asset platform — live data
Excel / Access migration
Migrating existing BI from spreadsheets to scalable, maintainable platforms
KPI and executive design
KPI framework design, executive summary dashboards, board-level reporting
Data storytelling
Data analysis is only valuable if the conclusions are understood by the people who need to act on them. Complex asset data needs to be translated into clear, credible narratives — with the evidence visible.
Executive summaries
Concise, evidence-backed summaries of risk position, programme status and priorities
Board dashboards
One-page views for senior leadership — risk headline, programme status, key actions
Regulator-ready evidence
Structured outputs that show the evidence trail behind risk decisions and mitigations
Operational decision packs
Clear, contextual packs for field teams, programme managers and decision-makers
Trend narratives
Time-based stories of condition change, risk evolution and programme progress
Comparison analysis
Before/after, baseline-to-current and cross-route comparative analysis
AI-assisted analysis
AI is used carefully and specifically — to accelerate repetitive analytical tasks, surface patterns at scale and generate structured outputs for human review. Not to replace engineering judgement.
Summarisation
Condensing long recommendation descriptions, defect notes and assessment narratives
Defect clustering
Grouping similar defects across a large asset estate for programme planning
Anomaly detection
Flagging inconsistent risk ratings, unusual patterns and outlier records
Duplicate detection
Near-duplicate recommendation and defect identification across legacy datasets
Decision support
Suggesting risk categorisation based on description, with human review required
Report generation
Structured summary generation from asset records for regulatory submission
BI tools and platforms
Platform-agnostic analytical delivery
GeoTelligence works with whatever BI tools the organisation has — or selects the most appropriate tool for the output required. Dashboards can live in Tableau, Power BI, Looker Studio or as custom web applications embedded in the platform. The underlying data model and analytical logic are the same regardless of the presentation layer.
Tableau
Interactive dashboards and spatial views
Power BI
Enterprise BI, Microsoft ecosystem
Looker Studio
Rapid prototyping, Google ecosystem
Custom Next.js
Live platform-embedded dashboards
Python / pandas
Analytical computation layer
Excel export
Structured export for existing workflows
Get in touch
Discuss a data analysis challenge
Whether you need a data quality assessment, a risk analysis, a BI dashboard or an analytical report — start with a conversation about your data.