The Customer, a company operating in large-scale distribution, expressed the need to migrate its marketing database to the Cloud, on the Google platform, to speed up the process of defining and generating marketing campaigns, an activity that is carried out more and more often, currently almost daily.
However, the need to reduce timing clashed with that of updating data in the on-premise data warehouse whose performance, also due to the increasing amount of data to be managed, suffered continuous degradation.
In agreement with the Customer, Google Big Query was chosen for the marketing database, a serverless, scalable and pay-per-use data warehouse service, without installation costs, maintenance and service interruptions.
It was not a simple migration as the predictive models underlying the process of identifying the recipients of the marketing campaigns had been defined in a proprietary environment, with a high degree of customization and took a long time to execute. It was therefore necessary to rewrite them with standard languages (SQL and python) and optimize them: thanks to this intervention, the loading, transformation and processing times have been significantly reduced (from hours to a few minutes). All this was carried out while always keeping costs and protection of sensitive data under control.
Considering the presence of sensitive data, considerable attention was paid to their anonymization and to the precise definition of differentiated access profiles.
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