Exploring Telco Case History at Quantum Block: Data Quality, Performance Analysis, and More

Table of Contents:

1. Introduction
2. Files Provided
3. Telco Case History at Quantum Block
4. Data Quality Issues
5. Issues Types and Testing Performance
6. Accessing Data using Spark
7. Counting Issue Types
8. Performance Analysis
9. Creating a Data Frame with Team Size
10. Joining Data Frames
11. Hypothesis Testing and Output Analysis
12. Conclusion

Telco Case History at Quantum Block

In this article, we will explore the Telco case history at Quantum Block and discuss various aspects related to it. We will start by understanding the files provided and their significance in the analysis. Then, we will dive into some key questions that were asked during the interview process, along with their answers. We will also cover topics such as data quality issues, performance analysis, and hypothesis testing. So let's begin our Journey into the Telco case history at Quantum Block.

Files Provided

To analyze the Telco case history, three files were provided. These files can be accessed on the GitHub page. The first file is named "team.csv" and contains information about the teams involved. It includes details such as team ID, team size, and experience. The Second file is named "alarm.json" and contains information about network failures. It includes details such as alarm ID, event time, alarm source, and network information. The third file is named "ticket.net" and contains additional data related to the telecom server. It includes details such as ticket ID, alarm ID, start time, resolving team, and source system.

Now that we have an overview of the files provided, let's move on to discussing the Telco case history at Quantum Block in more Detail.

Telco Case History at Quantum Block

As part of the interview process at Quantum Block, I was asked various questions related to the Telco case history. One of the questions asked was about the potential drivers for variations in network maintenance performance between regions. While I didn't have a definitive answer, I Mentioned that network failures could be a major driver. Network failures can cause delays in signal transmission and result in poor voice quality.

Another question focused on data quality issues. I was asked to identify the data quality issues that I would consider when accessing the available data. In response, I mentioned that I would check for issues such as missing values, incorrect data types, inconsistencies, and adherence to business rules. I explained that I would perform checks on primary keys, mandatory fields, and Apply Relevant business logics to ensure data quality.

I was also asked to analyze issue types and test performance. To address this, I used Spark to access the data and created a view to count the occurrence of different issue types. This allowed me to identify the most common issues such as 3G failure, power failure, and speed failure. By executing the necessary tests and analyzing the results, we can gain insights into the performance of the engineering teams.

Furthermore, I was tasked with creating a data frame that combines team size information with ticket information. This involved joining the "team.csv" file and the "ticket.net" file Based on the team ID. By doing so, we could analyze how team size influences ticket performance.

During the analysis, it was observed that the team was working more effectively with longer time-to-issue resolution. To uncover the driving factors behind this, a function was required to determine the network Type based on alarm records or subparts. This involved extracting the relevant information from the "alarm.json" file, specifically the "alarm source" column. By identifying keywords like "GSM," "3G," or "4G" in the column, we could determine the network type associated with each alarm record.

In conclusion, the Telco case history at Quantum Block provides valuable insights into network maintenance performance and the factors that influence it. By analyzing the provided files and answering the interview questions, we can better understand the challenges faced in the telecom industry and propose strategies for improvement.