raw charger data
- location
- 57–77 Mario N Capecchi Dr
- directions
- —
- parking
- —
- coordinates
- —
- status
- Available
- access
- —
- reviews
- —
- reliability
- —
enriched charger data
- location
- 57–77 Mario N Capecchi Dr
- directions
- Lower level, behind the green employee-parking sign
- parking
- 1
- coordinates
- 40.771837, −111.84008
- status
- Available
- access
- Public parking
- reviews
- Easy to use, but often busy.
- Charger offline during visit.
- Fast charging, great location!
- reliability
- 85%
01Overview
public charging data is often incomplete, outdated, or too imprecise to help drivers locate and evaluate the reliability of chargers. i led the development of a data refinement and enrichment model that combines network data, third-party sources, and driver feedback to improve charger location data, access information, and reliability insights.
02Decision Rationale
challenge
charging data came from fragmented sources with inconsistent formats, incomplete location details, and uneven signals of charger reliability.
key decision
I prioritized a layered model that first standardized records, then enriched their context and estimated reliability using multiple independent data signals.
03Product Strategy
- collect
aggregate data from charging networks, third-party sources, and driver feedback.
- refine
normalize, enrich, and validate infrastructure records.
- score
calculate reliability using recent operational signals and historical charging outcomes.
04How the Data Pipeline Works
location refinement
05Outcomes
- records enriched
212,722
records enriched
- integrated sources
10
integrated sources
- enterprise customers
11
enterprise customers