How OVH Uses Automated Dashboards in Customer Analysis

Take a closer look at how OVH scaled up its data visualization efforts to slash data preparation time and efficiently disseminate data to teams across the enterprise.

Dashboards are a common deliverable for teams across an enterprise, especially in customer-facing roles where having a complete understanding of consumer behavior is critical to operations. However, they can be challenging to produce scalably or keep updated regularly, and often they prove to be more of a burden than an agile tool used reliably for insights.

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 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.

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The Challenge

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. Therefore, analyzing users’ interactions and deriving insights from their behavior to inform the product and operations teams’ decisions is paramount. But the business analysts responsible for disseminating data and insights to inform on the commercialization and optimization of the website were facing issues; namely, they had built a dashboard with basic, high-level metrics (like user behaviors and site traffic), but its utility was limited.

The main problem was that the dashboard didn’t combine different data sources for a complete view, so it necessitated ad-hoc analysis, for which the analysts had little time. Additionally, ETL (extract, transform, load) 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.

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Dataiku helped OVH power their dashboards, leveraging its features to slash data preparation time and ensure visibility into the data life cycle.

Thanks to Dataiku, OVH business analysts were able to:

  • Quickly connect directly to any number of data sources (weblogs, CRM, etc.).
  • Combine data sources for more complete customer insights.
  • Do data preparation work efficiently thanks to an intuitive visual interface and the automation of much of the process.
  • Have a culture of experimentation and rapid prototyping.
  • Accomplish more with a small team of data scientists.

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In addition, Dataiku: 

  • Gives data architects peace of mind that analysts are working with quality data thanks to the clear visualization of data flows.
  • Allows data architects to clearly see and correct any issues in data quality directly.
  • Gives data scientists the opportunity to work directly with data prepared by analysts to apply ML techniques to other facets of the business more efficiently.

Bringing Dataiku in as part of our advanced analytics and data science strategy has been a tremendous success. We have teams across the company leveraging the tool for all kinds of projects and are adding more users and projects every day, but most notably we’ve been able to use Dataiku for a revolutionary SEO project powered by machine learning.

Magdali Barreau-Combeau,OVH

OVH increased the efficiency of its data scientists by 40%, and as a consequence, they are now better able to contribute to projects and work together with the rest of the team. They attribute the significant time optimization to Dataiku’s more efficient solutions for data preparation and data workflow monitoring. All of these gains amount to a faster time-to-market overall, accelerating OVH’s ability to go from data warehouse to meaningful business insights.

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