In this session, Alexandre showcased the journey Fenix has undertaken on the path of utilization of data. On one hand, Fenix’s experience in novel areas the solar sector has informed the evolution of the way it handles data. For instance, the company realized over time that it is not only a solar product provider but also a financial service provider, as PAYGO was slowly embraced by their customers. On the other hand, real-time data the company collects reversely informs the expansion of its operations.

2. OUR VISION
dirty
dangerous
dim
10-20 cents per day
cleaner
safer
brighter
10-20 cents per day

4. DATA SCIENCE AT FENIX
Data collection
Prediction (credit scoring, repayment & sales models)
Classification (automatically diagnose technical issues)
Experimentation & pilots

5. DATA SCIENCE PROBLEMS
Financial
Customer vetting
Credit scoring
Targeting interventions
Upgrade eligibility (ex. fuel-efficient stoves)
Operations
Call center staffing (predicting language needs)
Last mile distribution optimization
System Performance
Product analytics
System malfunction prediction and classification
Fraud/Tamper detection

6. GSM FOR REMOTE DATA COLLECTION
Before:
Gather data through experiments, technical repair diagnostics

7. GSM FOR REMOTE DATA COLLECTION
Now:
Remotely send/receive near real-time + historical data over GSM network.
Use cloud services (Amazon S3 + Spark data pipeline) to store and aggregate
millions of data points daily.
Devices send data on measurements (e.g. power, energy, temperature), user
interactions (e.g. button pressed), system events (e.g. battery full)
Data feeds databases, APIs, dashboards, automated diagnostic systems
Insights consumed by analytics team, operations, customer support, managerial &
business teams

9. GSM TO MONITOR SYSTEM USAGE (GENERATION,
USAGE)
Ideal

10. TIME SERIES ANALYSIS TO CLASSIFY PANEL ISSUES
Time
Power
1 day

11. Hour of the Day
SOLAR PANEL PERFORMANCE VARIES WITHIN A DEVICE
23 2 5 8 11 14 17 20 23
PowerGeneratedbyPanel(mW)

12. GSM DATA CHALLENGES
Scalability from thousands of data points, to millions, to billions…..in a single year as
customer base grows potentially in parallel
Pressure to get the design right the first time, to avoid tedious backfills
Interaction with legacy systems already in place e.g. choice of database technology
Overloaded analysts: - other departments (data consumers) growing much faster than
data team
Establishing partnerships and/or budget for relevant sources of supporting data

13. DATA SCIENCE PROBLEMS
Financial
Customer vetting
Credit scoring
Customer repayment
Financial Operations System Performance Field Work

14. OTHER DATA SCIENCE PROBLEMS
Financial
Customer vetting
Credit scoring
Customer repayment
Targeting interventions
Financial Operations System Performance Field Work

15. OTHER DATA SCIENCE PROBLEMS
Operations
Track product from supply to end-of-life
Last mile distribution optimization
Financial Operations System Performance Field Work

17. Other cool things the DS team dabbles in
- Aggregate information for other teams
- Product diagnostic suite
- Inform product design (GSM data led to design of new lights)
- Track call centre productivity
- Identify new markets via geo-spatial data