America Data Set

Purpose: Data from the Raw Zone is cleaned, standardized, de-duplicated, and enriched. This makes it more usable for downstream analytics.
Transformations:

Pseudonymization/Anonymization: Crucial for privacy. Sensitive identifiers like raw phone numbers should be pseudonymized or hashed at this ...

Privacy by Design: Incorporating privacy protections into the design of systems and processes from the very beginning, rather than as an afterthought.
Data Minimization: Collecting only the mobile number data that is strictly necessary for a specific, legitimate purpose.
Consent & Transparency ...

n today's digital economy, mobile phone numbers are not just contact details; they are critical pieces of personal identifying information (PII) linked to financial transactions, digital identities, and communication patterns. Given the context of recent data leaks in Bangladesh and the increasing ...

Regardless of the model, the quality and relevance of features derived from mobile number data are paramount.Usage Patterns: Average daily call duration, number of unique contacts called, SMS sent/received count, data consumption (2G/3G/4G/5G), time of day/week usage peaks.
Recharge Behavior ...

Logistic Regression: A good baseline for binary classification (churn/no-churn, fraud/no-fraud). Simple, interpretable.
Decision Trees / Random Forests: Excellent for handling mixed data types and identifying important features. Random Forests are robust and perform well.
Gradient Boosting Machines ...

Estimate the total revenue a customer is expected to generate over their relationship with the company.
Data Points: ARPU (Average Revenue Per User), tenure, usage patterns (voice, data, SMS), MFS transaction volume, subscription to value-added services.
Why it matters: Helps prioritize high-value ...

Machine Learning (ML) models are transforming how businesses, particularly mobile network operators (MNOs) and mobile financial service (MFS) providers in Bangladesh, leverage vast amounts of mobile number-centric data. By analyzing historical usage patterns, demographic information, and ...

Considerations for Bangladesh: This layer directly supports the business intelligence and data science initiatives relevant to local operations (e.g., optimizing network coverage in Bogra, identifying MFS fraud patterns across districts).
Metadata Management & Data Governance:

Purpose: To manage ...

Purpose: To capture data from various sources and move it into the data lake. For "recent" mobile data, real-time streaming is crucial.
Batch Ingestion: For historical data dumps, daily logs, or less time-sensitive data.
Tools: Apache NiFi (open-source, visual data flow management, good for ...

Designing a data lake architecture for recent mobile data, particularly in a context like Bangladesh, requires a strategic approach that balances high-volume, high-velocity data ingestion with cost-effectiveness, privacy concerns, and the need for diverse analytical capabilities.

A data lake is a ...