Developing KPI dashboards saved time for charity to monitor social media performance.
The Situation
The ‘yesHEis’ app is operated by global charity Christian Vision (CV) to help Christians share their faith online. With 3 million users worldwide, its operation by CV teams and partners is supported by a substantial technology infrastructure and sophisticated use of social media and Customer Relationship Management platforms.
The Task
Design and build of 4 dashboards required to display key performance indicators (KPI) of the yesHeis app. They needed to be interactive to allow filtering by each one of 16 international franchise teams as well as enabling a view across all teams. The dashboards would cover KPIs relating to management, marketing, content and community metrics.
The data source was a CV data warehouse consisting of several different schema and tables. Some of the required metrics required complex derivation in Tableau Desktop.
The Action / Approach
Datawoj worked closely with CV to implement a solution which included:
– Collaboratively researching ways to join mismatched entities from multiple sources
– Blending of multiple data sources and cross database filtering
– Effective data visualisation techniques including ‘sparklines’ and ‘slope charts’ to show change in app performance over time
– Derivation of complex metrics using Table Calculations and Level of Detail expressions in Tableau Desktop
– Design of high performing dashboards including testing and iteration.
The Result
The dashboards were designed, built and quality assured to the required specifications. They are currently being used in the field by the franchise teams to effectively manage their daily performance of the YesHeis app. This has saved the client’s research and analysis manager considerable time in terms of building out the dashboards as he only had limited capacity.
The data itself was challenging enough to work with internally, but to expose it to an external collaborator raised this to a new level! It re-emphasised the essential role of data preparation and cleansing in any data operation, and even more so if complex big data is to be rendered usable by analysts.
