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April 18, 2024

Automating data visualization for a global streaming giant

By implementing an automated data visualization system we’re able to convert complex data logs into actionable insights. This automation not only saved dozens of hours per week but also enhanced the company’s capacity for strategic analysis.


In the fast-paced world of streaming, speed, efficiency, and an incredible user experience are paramount. Our collaboration with one of the world’s leading streaming companies highlighted a significant bottleneck in their performance analysis workflow—a critical element in delivering the stellar user experiences they are known for.

This bottleneck emerged as the company strived to provide seamless, enjoyable user interactions, necessitating extensive performance benchmarks across various devices and configurations. These benchmarks produced vast amounts of data, essential for tracking progress, communicating results to stakeholders, and informing future strategic decisions.

However, analyzing this data was a hurdle for a couple of simple reasons. Firstly, the raw logs were difficult to interpret, and secondly, the process of manually translating them into presentable graphs within Excel was incredibly time-consuming. This manual process hindered the team’s ability to engage in deeper analytical work.

Using automation as the solution

Recognizing the repetitive nature of this task, our team took the initiative to streamline it. We identified a need for a straightforward solution that emphasized process optimization and efficiency – simple yet powerful enough to save numerous hours each week. Our intervention automated the conversion of raw logs into clear, actionable visualizations.

Tools and techniques used

  • Python and Pandas. We employed Python and its Pandas library to streamline data handling. Pandas allowed us to efficiently load, clean, standardize, and structure the massive benchmark logs.
  • Matplotlib for visualization. Once the data was processed, we turned to Matplotlib, a flexible Python library, for creating tailored graphs and visualizations. This gave us significant control over visual presentation compared to Excel’s limited options.
  • Automation script. The key to maximizing time savings was a custom script. This script integrated the data processing and visualization steps, automating the entire workflow from log input to graph output.

The result

Addressing such “low-hanging fruit” within typical organizational processes can yield significant improvements. By automating this aspect of their workflow, the team realized benefits in three critical areas:

  1. Significant time savings. Our automation efforts saved the client dozens of hours each week, freeing them from the burden of manual tasks.
  2. Improved insights. With the grunt work automated, the client’s team could dedicate more time to interpreting the data, identifying trends, and developing new strategic initiatives.
  3. Proactive approach. This project demonstrated our team’s proactive approach to client challenges. We actively sought out ways to streamline workflows, driving greater value for the client.

Conclusion: The power of process automation in data-intensive environments

This case study underscores the impact of process automation within data-intensive environments – particularly when there are cases of monotonous, repetitive tasks. In turn, by simplifying data visualization, we enabled the streaming giant to extract more value from their data, leading to enhanced operational efficiency, quicker insights, and, most importantly, a superior user experience for their viewers.

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By Santiago Ballester

Senior Data Scientist

Santiago Ballester is a Senior Data Scientist with Qubika. With expertise in Natural Language Processing (NLP) and an academic background in mathematics, Santiago helps our team, clients, and users understand, generate, and act on increasingly large amounts of data with ever greater speed.

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