Data Quality

Data Cleansing

To enhance data accuracy and reliability, Mobito performs:

Deduplication: Removal of duplicate records to prevent redundancy.

Filtering of Noisy/Damaged Records: Exclusion of records that compromise data quality, such as:

Null locations or IDs

Incorrect timestamps

Low-points trips

Data Anonymisation

Mobito implements strict anonymization techniques to remove personal identifiers and ensure compliance with data protection regulations (e.g., GDPR).

For more details, refer to Mobito Anonymisation.

Data Standardisation

All processed data is formatted to align with the Mobito universal data schema, ensuring consistency across all deliveries.

Key benefits include:

Seamless integration with various analytics platforms

Consistent structure across different datasets

Faster and easier data processing for end users

For more details, refer to:

• Probe Data Schema
• Driving Events Data Schema
• Standstill Events Data Schema
• Origin-Destination Data Schema