Data Transformation at Rabobank: A Case Study in Execution & Innovation
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Education
Applicable Functions
- Product Research & Development
Use Cases
- Time Sensitive Networking
Services
- Data Science Services
About The Customer
Rabobank is a Dutch multinational banking and financial services company, part of the Rabobank Group, one of the largest financial services providers globally. The bank has been on a data transformation journey since 2011, with the aim of keeping up with the rapid pace of technological change in the banking sector. They have a strong commitment to innovation and have completed more than 100 AI projects in the past year and a half. They have also significantly reduced the time it takes to onboard new data team members, particularly data scientists.
The Challenge
Rabobank, a leading Dutch bank, was faced with the challenge of keeping up with the rapid pace of technological change in the banking sector. According to a 2020 PwC report, 81% of banking CEOs expressed concern about the speed of technological change, more than any other industry sector. Rabobank, however, chose to embrace this change and transform their organization to move with the pace of innovation. The bank had been on their data journey since 2011, and while they had the support from both the executive level and the people implementing the technology and processes, they needed to further streamline their approach to data transformation.
The Solution
Rabobank's solution to this challenge was a multi-faceted approach that involved changes to their organizational structure, tackling a wide range of use cases, creating an innovation funnel for use cases, upskilling their staff, and leveraging technology. They completed more than 100 AI projects in a year and a half and reduced the time to onboard data team members, particularly data scientists, from months to weeks. They also learned valuable lessons along the way, such as the importance of organizing everything around technology, keeping the business at the forefront when selecting data projects, and understanding that data transformation is a constantly evolving journey. The processes and approaches they created in 2014 had to be adjusted to fit their growing needs.
Operational Impact
Quantitative Benefit
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