Your geospatial team runs the same workflow every week. Or every month. Or every quarter. The data sources change, but the steps are identical:
- Download data from 5 different sources
- Open GIS software, import layers, reproject
- Run the same 15 processing steps
- Export results, open Excel, manually format
- Copy-paste into PowerPoint or reporting tool
- QA check everything manually
Time required: 8 hours. Sometimes 16 hours. Sometimes 3-4 weeks for complex workflows.
Frequency: Weekly, monthly, quarterly—depending on the workflow.
The cost? Most organizations never calculate it.
The True Cost Formula
Let's do the math on a "simple" weekly workflow that takes 8 hours to complete manually:
Weekly Workflow Example
Annual Hours
416
(10+ weeks of work)
Annual Labor Cost
$31,200
416 hours × $75/hour
Automation Cost
~$1,200
Annual cloud compute
ROI achieved in <6 months (including automation build cost)
And this is a simple weekly workflow. Now consider organizations running dozens of these workflows.
Real Example: MunichRe Catastrophe Modeling Workflow
At a global reinsurance company, the geospatial analysis workflow for each country took 3-4 weeks of full-time analyst work end-to-end.
The Manual Process
- Download exposure data, hazard maps, building footprints, infrastructure layers
- Reproject and harmonize data from multiple sources and formats
- Run spatial joins, overlays, proximity analysis (20+ steps in GIS software)
- Export intermediate results, manually QA in Excel
- Rerun failed steps, troubleshoot data quality issues
- Generate final deliverables, compile into reports
- Manual review and validation
Total time: 3-4 weeks (120-160 hours) per country
Frequency: Quarterly for existing markets, ad-hoc for new market assessment
Capacity: The team could process 2-3 countries per year given other priorities
The Business Impact
This manual bottleneck had real business consequences:
- Treaty renewals delayed: Risk assessment couldn't keep pace with reinsurance treaty deadlines
- New market entry blocked: Couldn't evaluate 50+ potential markets for expansion
- Competitive disadvantage: Competitors with automated systems could move faster
- Analyst burnout: Skilled analysts spending 80% of time on repetitive data wrangling
The Automation Transformation
We built an automated pipeline using Python (GeoPandas, Rasterio), deployed on Databricks. The workflow now runs as:
- Scheduled data ingestion from all sources
- Automated processing pipeline (all spatial operations)
- Built-in QA checks and validation rules
- Automated deliverable generation
- Analyst review of outputs only (not data wrangling)
Automation Results
Time
30 min
from 3-4 weeks
99%+ reduction
Cost
90%+
reduction
Compute + licensing
Scale
50+
countries/year
from 2-3/year
Analyst time freed up: Now spent on higher-value analysis and model improvement instead of data wrangling
The Hidden Costs Beyond Labor Hours
The direct labor cost is obvious, but manual workflows have hidden costs that are harder to quantify:
1. Scaling Bottleneck
To process 10x more work, you need 10x more people. Automation lets you scale to 100x without proportional headcount increase.
2. Inconsistency and Errors
Different analysts produce slightly different results. Manual steps introduce errors. Automated workflows are consistent and reproducible.
3. Knowledge Silos
Critical workflows exist only in veteran employees' heads. When they leave, the knowledge leaves. Automated workflows are documented in code.
4. Slow Turnaround Blocks Business Decisions
Weeks-long workflows mean stakeholders can't get timely answers. Automated workflows enable real-time decision-making.
5. Analyst Morale and Retention
Skilled GIS analysts don't want to spend 80% of their time on repetitive data wrangling. Automation lets them focus on actual analysis and insights.
When Does Tool Migration Happen?
In the MunichRe example above, workflow automation happened to include migrating from desktop GIS tools to Python-based automation. But tool migration is not always required for automation.
✅ Automation Without Migration
You can automate workflows using your existing tools:
- ArcPy scripts: Automate ArcGIS workflows with Python
- Model Builder: Chain geoprocessing tools into automated workflows
- FME Desktop: Automate data transformation workflows
- QGIS Processing: Python scripts calling QGIS algorithms
✅ Automation With Migration
Tool migration becomes relevant when it serves automation goals:
- Cloud integration: Desktop tools can't integrate with Databricks/Snowflake/BigQuery
- Scalability: Seat-based licensing prevents horizontal scaling of automation
- Cost optimization: High licensing costs justify migration ROI
In the MunichRe case, migrating from ArcGIS Desktop → Python/Databricks made sense because:
- The company was already moving to Databricks for data infrastructure
- Desktop workflows couldn't scale to 50+ countries/year
- Licensing costs were $2M+/year
We're tool-agnostic. If your automation can be achieved with existing tools at good ROI, we'll use them. If migration provides better ROI, we'll recommend it.
How to Calculate Your Manual Workflow Cost
Use this formula to calculate the annual cost of any recurring manual workflow:
Annual Labor Cost =
Hours per Run × Runs per Year × Hourly Rate
Examples:
Weekly workflow, 8 hours:
8 hrs × 52 weeks × $75/hr = $31,200/year
Monthly workflow, 16 hours:
16 hrs × 12 months × $75/hr = $14,400/year
Quarterly workflow, 120 hours (3 weeks):
120 hrs × 4 quarters × $75/hr = $36,000/year
Multiple workflows add up fast:
If you have 5 different recurring workflows, you could easily be spending $150K+/year on manual labor for tasks that could be automated.
What Workflows are Good Automation Candidates?
Not every workflow is worth automating. The best candidates are:
✅ Ideal for Automation:
- Recurring: Run weekly, monthly, quarterly (not one-time)
- Consistent steps: Same process each time (data sources may change, steps don't)
- Time-consuming: Take hours or days to complete manually
- High volume: Process many items through the same workflow
- Well-documented: Team knows the steps and logic
❌ Poor Automation Candidates:
- One-time projects: Not worth automation investment
- Constantly changing: Requirements change every run
- Requires judgment calls: Human decision-making at every step
- Quick and simple: Already takes <30 minutes manually
The Automation ROI Timeline
Most workflow automation projects follow this timeline:
Workflow Audit
Document current process, identify automation candidates, assess feasibility
Proof-of-Concept
Automate one representative workflow to demonstrate feasibility and ROI
Production Build
Build full automated pipeline, test with real data, deploy to production
Knowledge Transfer
Train team to maintain and extend automated system, achieve full autonomy
Typical ROI Achieved: 6-18 months
Automation cost (build + infrastructure) paid back by time/cost savings within 6-18 months depending on workflow frequency and complexity
Conclusion: Calculate Your Cost, Then Decide
Before assuming you need to keep doing manual workflows, calculate the real cost:
- List your recurring geospatial workflows
- Calculate annual labor hours for each
- Multiply by hourly rate
- Add up the total
If you're spending $50K+/year on manual labor for workflows that could be automated, the ROI is likely there.
We're happy to tell you if automation doesn't make sense. We've turned down projects where the ROI didn't justify it. Better to be honest upfront than deliver a failed project.