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Data Engineering for INWI

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ABOUT THE CLIENT

Inwi

PROJECT OVERVIEW

The project is part of data engineering, in particular the constitution of relevant data tables containing information crucial to making informed decisions, improving operational performance and optimizing services such as:

Network traffic statistics
Call quality
Network latency
Data traffic analysis
Most Used Applications
Problem Reporting, Resolution Time,
Churn, Customer Satisfaction, Billing and Revenues, Revenues per Subscriber, Fraud Analysis, Competitive Analysis, Market Shares, Competitive Offers and Pricing Analysis
Campaign Effectiveness: Measures the effectiveness of marketing campaigns in acquiring new customers.
Equipment Analysis: Phone usage, Device compatibility
Trend Analysis: Technology evolution

TECHNICAL DIFFICULTIES

Massive data volume: As a major data operator, our customer generates a substantial volume of data in real time. Processing, storing and analyzing this data is complex, and requires appropriate infrastructures.

Data variety: Data can be structured and unstructured, from a variety of sources, and stored in a variety of formats. Integrating and managing this variety of data is a challenge.

Data security: Telephone operators’ data is often sensitive and subject to strict confidentiality regulations. Ensuring data security and compliance with regulations, such as RGPD, is a priority.

Latency: In a real-time environment, latency is critical. Data must be analyzed quickly to make timely decisions, which requires a high-performance infrastructure.

Heterogeneity of data sources: Data can come from a variety of sources, including networks, billing systems and so on. Integrating these heterogeneous data sources is not without complexity.

THE SOLUTION

Scalable, high-performance infrastructure: The customer has invested in an infrastructure that can scale to handle the massive volume of data.
Data Analysis Platforms: We used robust data analysis platforms such as Apache Hadoop and Spark to process and analyze large-scale data.
Data Integration Tools: We have adopted data integration tools such as Talend and Informatica to facilitate the extraction, transformation and loading (ETL) of data from various sources.
Security and Compliance: We have implemented strict security policies, used data encryption and ensured we comply with data privacy regulations, such as the RGPD.

KEY BENEFITS

This data analysis led to the creation of dashboards that enabled us to make strategic decisions: Data analysis provides information that can be used to make strategic decisions. This includes identifying new market opportunities, defining pricing strategies, and planning technology investments.

Customer management: Data analysis enables us to better understand customer behavior, anticipate needs and improve customer satisfaction. Churn reduction is often a positive outcome.

TECHNOLOGY