Gaming companies, especially due to the high volume of ingame event logs, generate terabytes of data on a daily basis, which not only need to be distributed in fast and reliable access but also need to be aggregated and visualized in a timely and useful way to help the production engineers make data-driven improvements and bring business decisions. As such a high volume of data is created every day with an astonishing speed, it becomes highly challenging to process, aggregate and showcases the data in a supporting manner for business decision making.
For that reason, business intelligence approaches like visualizations, in order to explore and analyse game-related data, need to find an optimal way of capturing the most out of such diverse and voluminous dataset both from a data perspective as well as from the point of view of the end-user. This, in reality, is a rather complex and many times a seemingly irrelevant job for companies that need to keep up anyhow with the daily income of the terabytes. As a result, companies not only end up having Key Performance Indicators visualized in the simplest forms of tables but also face the burden to store more data than needed in expensive data warehouses.
In this thesis, we are going to propose a guideline for a business information visualization pipeline based on a practical use-case of a real-world gaming company. We present a pipeline that is not only built on top of the available data but also takes into consideration the cognitive processes involved in finding answers in visualizations and follows a task-driven structure. Furthermore, we are going to test which combination of view management and coordination helps better to deliver more accurate responses and a more acceptable system for decision making.
The purpose is not only to create effective visualization and provide a guideline for a pipeline but to find bottlenecks and points of failure that have an indirect effect on the efficiency of the outcome. It is critical to search for latent points of failures to improve upon pipelines that are near to a highly efficient and effective visualization that serves business intelligence with high turnover
