Data Analytics For A
Telecom Company

Data Analytics for a Telecom Company
Overview Data Analytics for a Telecom Company


Businesses worldwide are generating massive data volumes every day, and they need to use all this data to get valuable insights from it. Using these insights, they can make impactful business decisions. This is where data analytics comes into the picture. Data analytics is the process of evaluating big datasets to discover correlations, hidden patterns, upcoming trends and receive great insights to make forecasts. Moreover, it also enhances your businesses' productivity and speed. Using advanced technological tools, we created an Analytics Solution for our telecom client to gather information from various sources and receive valuable insights into customer behavior.

Client Requirement

Our Houston-based client owns a telecom business that helps in the National Support Program and thus, renders pre-paid mobile phones and service packages to low-income people. They wanted to gather information from various sources and receive profound insights about their user behavior. Moreover, they wanted an analytics solution to evaluate past data and make predictions using which they could make effective business decisions. Therefore, our telecom client approached HData Systems to build them a robust and secure analytical system.

Client requirement for Data Analytics for a Telecom Company


  • We developed an analytics solution that collected raw data like tariff plans, users' impressions, CTRs, etc., from more than ten sources. Therefore, our data analytics team recommended using MQTT Protocol to gather this data and transfer it to Apache Kafka.

  • To decrease the costs of AWS computing resources, our team recommended using Amazon Spot Instances. And we used AWS Application Load Balancers to guarantee the scalability of the analytical system.

  • Our team chose Amazon Redshift for data storage and warehousing. Using Amazon Redshift, we were able to retrieve the data from the Enterprise Resource Planning (ERP) and Home Location Register (LHR) and data from Android-based cell phones.

  • The customers and their tenants were allowed Access to the solution for useful insights. For this, our analytics team used 2 approaches - Dedicated Access and Shared Access.


Our team had to create and incorporate data management & analytics solutions to enable our client to gather the data from different sources and leverage it to understand customer behavior.

Moreover, our client also wanted a system to evaluate past data and allow prediction based on it.

Another challenge was access rights, as our client wanted to offer Access to their tenants to the tenant-related analytics.


Using the data analytics platform, our clients could gauge user engagement and recognize their preferences.

They could identify trends in the users' behavior.

Our client could make forecasts about the users' behavior based on historical information.

Moreover, it should be noted that using the Amazon Spot Instances helped the client prevent the AWS computing resources cost by 80%.



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