Our customer wanted a solution that would collect and store large amounts of data that could be efficiently accessible to their Data Science teams. The business strategy was to use this data and predictive analytics to better understand service and maintenance call data from their installed based equipment at their customer sites.
Security was a primary concern for our customer. Globally there was over 6,000 users managed in a complex ragged-hierarchy with peer reviews to facilitate their sales aggregates. Security had to be flexible while limiting exposure to unauthorized disclosure.
Tail Wind, architected, designed and developed an Azure Data Lake; including Azure Data Factory to pull all data sources and all fields, in their raw format, into the Data Lake; and built a curated reporting platform consisting of Azure Analysis Services and Microsoft Power BI.
Once the data was all located in one spot, the different teams could access the necessary data and use it as needed. Being on the BI/reporting team, we did additional processing of the data in Data Lake Analytics which was ultimately used in Power BI reports. Security was implemented to support both a hierarchical, and manager peer-review model.
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