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Faster, Higher Quality Dashboards for Better Customer Analysis
Technology Category
- Analytics & Modeling - Data-as-a-Service
Applicable Functions
- Business Operation
Services
- Data Science Services
The Challenge
OVH, a global provider of hyperscale cloud services, was facing challenges with its dashboarding system. The business analysts responsible for disseminating data and insights to inform the commercialization and optimization of the website were spending more than 80% of their time on data preparation for the dashboard. The existing dashboard only provided basic, high-level metrics and did not combine different data sources for a complete view. This necessitated ad-hoc analysis, for which the analysts had little time. Additionally, the ETL process for the dashboard presented concerns for the data architects around data and insights quality, as there was a lack of transparency around exactly what data was being transformed and how.
About The Customer
OVH is a global provider of hyperscale cloud, which offers businesses a benchmark for value and performance in the sector. Founded in 1999, the group manages and maintains 27 datacentres in 12 sites across four continents, deploys its own global fibre optic network, and manages the entire supply chain for web hosting. Running on its own infrastructures, OVH provides simple, powerful tools for businesses, revolutionizing the way that more than 1 million customers work across the globe. Respect for individuals’ right to privacy and equal access to new technologies are central to the company’s values.
The Solution
OVH chose Dataiku Data Science Studio (DSS) to power their dashboards. With Dataiku, business analysts at OVH were able to connect directly to any number of data sources, combine data sources for more complete customer insights, and do data preparation work efficiently thanks to an intuitive visual interface and the automation of much of the process. This freed up their time for more creative and innovative ad-hoc analysis. Dataiku also gave data architects peace of mind, thanks to clear visualization of data flows, that analysts were working with quality data. Data scientists were also able to work directly with data prepared by analysts to apply machine learning techniques to other facets of the business more efficiently.
Operational Impact
Quantitative Benefit
