Challenges
The main aim of the Sankey Dashboard is to visualise the volume of support tickets between two stages in the lifecycle of a user support request.
With the aim of improving both quantity and quality, a more detailed understanding of how support works was required. The customer wanted to go further and identify the most frequently used or congested contact channels, the volume of exchanges between the different levels of support and the most common technical problems encountered.
Knowledge and understanding of the support activity was therefore essential.
Mobilize our skills, BI as well as functional Service Desk
Drawing on the group’s expertise in Service Desk management, the Consortia team deepened its knowledge of client’s organisation and processes in order to gain a better understanding of the issues at stake. The analysis phase made it possible to identify all the players and the operating mode in the life cycle of a user support request, the key to implementing appropriate indicators.
The team then took charge of the analysis, development and deployment of the dashboards, including both the implementation of the dashboards and the migration of the data and dashboards to the new user support software.
This phase included analysing the customer’s requirements, analysing and quality-auditing the data needed for the project, mapping the data for migration, mock-up, development, deployment, functional and technical documentation, and training the project managers.
Sankey Dashboard
The first step was to create a data source, structured differently from the initial data source. The Sankey dashboard (Sigmoid curve) needs to be viewed in columns rather than rows (e.g. transposed into Excel).
Once the dashboard was created, we customised the visual to add additional indicators in the form of statistics, word clouds and histograms.
Dashboard analysis and development
- Data Assessment
- Copil/Coproj
- Service provider invoicing
- SLA
- Ticket resolution time
- Interaction between support levels (ping-pong effect)
- …
Data analysis and mapping
- Query the DataLake to identify new data and understand how it is fed,
- Creation of a document specifying the data feed rules,
- Supervision of developers and acceptance of ETLs (DataPrep),
- Development of data checks before and after ETL processing (DataQuality).
Dashboard transfer (DataViz)
Adaptation of existing Dashboards to the new software’s data and operating methods
More than 1.5
years of support
130
dashboards processed
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