Data Quality and Data Reliability are two closely related but distinct aspects of data integrity. Scaler helps you manage both — at the point of input and across long-term reporting cycles.
What is Data Quality?
Data Quality means your data is:
- Complete – all required fields are filled in
- Accurate – values are correct and properly formatted
- Consistent – field logic and values align across your portfolio
- Valid – data passes required validations and reporting logic
How Scaler Supports Data Quality:
- Required fields and dropdown menus ensure standardization
- Built-in validation rules flag invalid or illogical entries
- Automated alerts for:
- Errors (invalid values)
- Missing data
- Warnings (e.g., outliers, inconsistencies)
- Outlier detection highlights abnormal changes in resource use
Together, these tools ensure your data is clean and usable for analytics and reporting.
What is Data Reliability?
Data Reliability refers to how trustworthy and representative your data is for tracking performance over time. Scaler calculates a Data Reliability Score (0–100) for energy, water, and waste at the asset level.
Scaler’s Data Reliability Score is based on:
- Meter type
- Area coverage – how much of the building’s floor area is covered
- Time coverage – how many days have valid data in the reporting year
Meter Type Scoring Breakdown:
Meter Type | Score |
Smart meters | 1.0 |
Invoices / Conventional meters | 0.8 |
estimation_(sjv_cluster) | 0.6 |
estimation_(sjv_postal_code) | 0.4 |
estimation_(calculation) | 0.2 |
The overall score for each asset is a weighted average based on the meter types and how much area/time they cover.
Bonus: Explain Anomalies
Scaler also flags unusual consumption trends and allows you to add context or comments directly in the platform to explain any valid anomalies (e.g., building closures, occupancy shifts).
Why it Matters
Together, Scaler’s data quality validations and reliability scoring ensure that:
- Your input is correct from the start
- Your metrics can be trusted over time
- Your ESG reporting is defensible and accurate
Related Tags:
#Data Collection #Analytics #Validation #Data Quality #Data Reliability #Scaler Score