From Data to Decisions: Using the Sewer Data You Already Have
In Issue #1, we explored a familiar problem: utilities collect a lot of sewer data, but very little of it shapes day-to-day decisions. CCTV is completed. GIS is updated. Work orders are closed. Then the next emergency takes over.
The issue is not lack of data for predictive analysis but most sewer data are not decision-ready for CIP.
The Value Hiding in Plain Sight
Every collection system already has what it needs to make better calls:
CCTV inspection reports
Work orders and emergency responses
GIS attributes (age, material, diameter, slope)
Flow or level monitoring data
Complaint and blockage histories
Individually, these datasets describe the system.
Combined, they explain where attention actually belongs.
Why Are Data Still Unused
Across utilities, the same patterns show up:
Data lives in multiple systems that don’t talk
Inspection reports describe condition, not priority
Cleaning and CCTV plans are reactive or calendar-based
Capital decisions lean on “known bad areas”
The Shift That Changes Everything
Better decisions don’t come from more dashboards.
They come from better questions.
Instead of:
What condition is this pipe in?
Ask:
Where is failure most likely in the next 12–24 months?
Which assets combine poor condition and high consequence?
What work can be deferred safely?
Where will cleaning, CCTV, or point repair matter most?
These are operational questions and your existing data can answer them.
What This Looks Like in the Real World
In one active collection system, blockages kept recurring in the same corridors year after year. Crews responded. Lines were cleaned. Service was restored. Then it happened again.
Rather than expanding cleaning everywhere, the team overlaid:
Repeat work orders
CCTV defect patterns (roots, structural issues)
GIS pipe attributes (material, diameter, age)
A small group of pipes was driving a large share of the problems.
Those locations moved to the top of the CCTV and rehab list—not because they had the worst scores, but because they combined failure history, defect type, and consequence.
The result:
Targeted cleaning instead of blanket programs
CCTV focused where it mattered
Capital projects backed by defensible logic
Smarter work not more work.
A Simple Framework Utilities Can Use Now
You don’t need new software to start.
Combine condition, performance, and consequence
Rank assets by risk, not just inspection score
Match actions to risk:
O&M for manageable risk
Investigation where uncertainty is high
Capital where failure isn’t acceptable
This works in GIS, spreadsheets, or basic dashboards.
What Comes Next
When data starts driving decisions:
Planning improves
Justification gets easier
Field and office teams align
In the next issue, we’ll tackle a common misconception:
Risk is not just about score.
It’s likelihood, consequence, and timing. Misunderstanding it is one of the biggest reasons good data still leads to poor decisions.