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Data Transformation at Rabobank: Execution & Innovation

In the past year and a half, Rabobank has completed more than 100 AI projects and has reduced the time to onboard data team members — in particular data scientists — from months to weeks.

According to a 2020 report from PwC, 81% of banking CEOs are concerned about the speed of technological change, more than any other industry sector. Instead of backing away due to this fear, Rabobank has been able to dive in and transform their organization to move with the pace of innovation.

Rabobank has been on their data journey since 2011, and true to their mission and identity, it has been a team effort from the beginning with support from the top-down at the executive level as well as from the bottom-up from the people putting in place the technology and processes to execute. This kind of collaboration and support certainly helps, but it’s not the only driver behind Rabobank’s transformation around data.

In the past year and a half, Rabobank has completed more than 100 AI projects and has reduced the time to onboard data team members — in particular data scientists — from months to weeks. Their ability to do so comes out of their approach to:

  • Organizational structure
  • Tackle a wide range of use cases
  • Create an innovation funnel for use cases
  • The education and upskilling of staff
  • Technology

We spoke to Rabobank’s Roel Dirks, Product Manager Big Data Lab, and Martin Leijen, Business Architect Data WR, to understand their keys to success in each of these areas. Get the full ebook to see how Rabobank is able to execute on and innovate with AI initiatives by leveraging these five initiatives, serving as a model to non-digital native organizations worldwide for successfully increasing digital and AI maturity.

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Rabobank's Data Journey, Revealed

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Lessons Learned

When it comes to raising data maturity across an entire organization, there are three main takeaways from Rabobank’s journey:

  1. It’s not just about technology — the bigger challenge is organizing everything else around it. While the right technology can certainly make the journey smoother and be a catalyst for change, it’s not a magic bullet that will raise an organization’s maturity around data, machine learning, or AI in and of itself.
  2. It’s all about the business — from selecting the right data projects to delivering value, the business is front and center. Running data initiatives from an IT team or even a siloed data team alone can never bring the depth and value brought by domain experts. The secret is getting the two to collaborate deeply together.
  3. It’s a constantly evolving journey — the processes and approaches Rabobank created in 2014 are no longer applicable, and the team has adjusted them accordingly. In five years as they become even more mature, systems will continue to shift to fit their growing needs. For companies just getting started, Rabobank wouldn’t recommend beginning with their current approach — it’s something you need to grow toward, customizing for particularities of your organization.

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