Dataiku Case Studies Data Transformation at Rabobank: A Case Study in Execution & Innovation

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	Data Transformation at Rabobank: A Case Study in Execution & Innovation Dataiku

Data Transformation at Rabobank: A Case Study in Execution & Innovation

Dataiku

Technology Category	Analytics & Modeling - Machine Learning
Applicable Industries	Education
Applicable Functions	Product Research & Development
Use Cases	Time Sensitive Networking
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Applicable Industries

Education

Applicable Functions

Product Research & Development

Use Cases

Time Sensitive Networking

Services

Data Science Services

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.

About 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.

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.

Impact #1	Rabobank's approach to data transformation has resulted in significant operational improvements. The bank has been able to tackle a wide range of use cases, create an innovation funnel for these use cases, and upskill their staff. This has not only improved their ability to execute on their data projects but has also fostered a culture of innovation within the organization. Furthermore, by keeping the business at the forefront when selecting data projects, they have been able to deliver more value to their customers. Finally, by understanding that data transformation is a constantly evolving journey, they have been able to adapt their processes and approaches to fit their growing needs.

Impact #1

Rabobank's approach to data transformation has resulted in significant operational improvements. The bank has been able to tackle a wide range of use cases, create an innovation funnel for these use cases, and upskill their staff. This has not only improved their ability to execute on their data projects but has also fostered a culture of innovation within the organization. Furthermore, by keeping the business at the forefront when selecting data projects, they have been able to deliver more value to their customers. Finally, by understanding that data transformation is a constantly evolving journey, they have been able to adapt their processes and approaches to fit their growing needs.

Benefit #1	Completed over 100 AI projects in the past year and a half
Benefit #2	Reduced the time to onboard data scientists from months to weeks
Benefit #3	81% of banking CEOs are concerned about the speed of technological change, Rabobank is actively addressing this concern

Benefit #1

Completed over 100 AI projects in the past year and a half

Benefit #2

Reduced the time to onboard data scientists from months to weeks

Benefit #3

81% of banking CEOs are concerned about the speed of technological change, Rabobank is actively addressing this concern

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Overview

Data Transformation at Rabobank: A Case Study in Execution & Innovation

Operational Impact

Quantitative Benefit