This anonymized visualization was created for a Telco operator analyzing residential Telco lines. The project aimed to identify linkages between line and network hardware performance that may impact customer experience.
The dots (nodes) represent DSLAM (Digital Subscriber Line Access Multiplexer) on the Telco's network. DSLAM provide a vital service that can impact customer call experience; they connect customer lines to the main network. DSLAM service levels were measured by metrics, such as attenuation, bit rate, noise margin and output power, and clustered into three performance categories for each line. The purple nodes show DSLAM with excellent performance, orange: good performance and white: poor performance.
In the chart only a small number of DSLAMs experienced a high quality of service (purple). These DSLAM were co-located in the same building as the main network infrastructure, hence their proximity to the central network hub results in a premium service. The majority of customers achieve a good experience (orange), however there are a large number of DSLAM delivering a poor service (white) that were found to be located outside of the main city.
Customer experience and satisfaction suffers most when customers receive variable network quality. The Telco's primary concern is to ensure customers receive a consistent experience, even if that may be consistently poor due to their location is outside of the main city. The chart pinpoints every DSALM that delivers variable service levels; represented by the shared nodes between the good (orange) and poor (white) clusters. Armed with this data the Telco can now investigate and optimize the variable DSLAM.
This sigma visualization was created using the in-built analytics and visualizations found in the Teradata Aster platform.
Data attributes from residential lines across the city were gathered, such as attenuation, bit rate etc. These attributes were clustered to identify performance bands indicating customer network experience.
These clusters formed a basis for correlation and regression analyses to determine how the network performance varied in conjunction with factors such as: line technology and length, modem type and configuration, DSLAM, card technology, geographic location etc.
The sigma visualization shows only one part of the overall analysis, namely the linkage between DSLAM'â€™s and network performance.
Yasmeen is one of the most creative and insightful Data Scientists at Teradata. Yasmeen grew up in Scotland, where she enjoys the great outdoors, in particular hiking the Scottish Munros and sea kayaking.
Her work has seen her traverse many countries, including the UK, Ireland, Netherlands Turkey, Belgium and Denmark where she covers the finance, telecommunications, retail and utilities industries. Yasmeen specializes in working with businesses to identify their challenges and translate them into an analytical context. She has a unique ability to focus on how businesses can leverage new or untapped sources of data, alongside novel techniques, to enhance their competitive capabilities.
Yasmeen has worked with many analytical teams, providing leadership, training, guidance and hands-on support to deliver actionable insights and business outcomes. She uses various analytical approaches, including text analytics, predictive modelling, development of attribution strategies and time series analysis. She believes strongly in the power of visualizations and their ability to communicate complex findings to business users in a way that makes taking action easy.
Prior to Teradata, Yasmeen worked as a Data Scientist in the life sciences industry, building analytical pipelines for complex, multi-dimensional data types. Yasmeen also holds a PhD in Data Management, Mining and Visualization, which was carried out at the Wellcome Trust Centre for Gene Regulation & Expression. She has published several papers internationally and is a speaker at International conferences and events. In addition she has taught on MSc courses related to Data Science and Business Intelligence.
Yasmeen developed a keen passion for data analytics and visualization through her studies, having always been curious to ask questions and learn more. These skills have allowed Yasmeen to explore many opportunities in multiple disciplines, providing her with an endless world of new challenges!
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