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Automated Dashboards in Customer Analysis: A Case Study of OVH
Technology Category
- Application Infrastructure & Middleware - Data Visualization
- Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries
- Electrical Grids
- Equipment & Machinery
Applicable Functions
- Quality Assurance
Use Cases
- Rapid Prototyping
- Time Sensitive Networking
Services
- Testing & Certification
The Challenge
OVH, a global provider of hyperscale cloud, faced a significant challenge in analyzing user interactions on their website to inform product and operations decisions. The primary point of contact between OVH and its users was through their website, where customers could place orders and receive technical advice or support. The business analysts responsible for disseminating data and insights to inform on the commercialization and optimization of the website had built a dashboard with basic, high-level metrics like user behaviors and site traffic. However, the dashboard's utility was limited as it did not combine different data sources for a complete view, necessitating ad-hoc analysis. The analysts had little time for this, and the ETL (extract, transform, load) for the dashboard presented concerns for the data architects around data and insights quality. There was a lack of transparency around exactly what data was being transformed and how.
The Customer
OVH
About The Customer
OVH is a global provider of hyperscale cloud, offering businesses a benchmark for value and performance in the sector. Founded in 1999, the group manages and maintains 27 data centers 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. The primary point of contact between OVH and its users is through their website, where customers can place an order and receive technical advice or support.
The Solution
OVH turned to Dataiku to power their dashboards, leveraging its features to slash data preparation time and ensure visibility into the data life cycle. Dataiku allowed OVH business analysts to connect directly to any number of data sources, such as weblogs and CRM, and combine these data sources for more complete customer insights. The data preparation work was made efficient thanks to an intuitive visual interface and the automation of much of the process. This fostered a culture of experimentation and rapid prototyping, enabling a small team of data scientists to accomplish more. Dataiku also gave data architects peace of mind that analysts were working with quality data thanks to the clear visualization of data flows. It allowed data architects to clearly see and correct any issues in data quality directly, and gave data scientists the opportunity to work directly with data prepared by analysts to apply ML techniques to other facets of the business more efficiently.
Operational Impact
Quantitative Benefit
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