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Data Management

For a reliable and qualitative data foundation

Without Data Management, there is no suitable data to work with. Within Data Management, everything revolves around the quality and reliability of the input. Where does the data come from? How do we handle and store data? Do we comply with the rules concerning data? Do we have the right procedures and systems in place throughout the bank? Data Management is about finding, understanding and processing data in such a way that we, as a bank, can make reliable, fact-based decisions.

Stories of our colleagues


  • The data steward has become an invaluable asset for companies looking to manage their data better. Data stewardship is a functional role in Data Management and governance, with responsibility for ensuring that data policies and standards turn into practice within the steward’s domain.

  • Risk and compliance go hand in hand in banking. By being operational, we are exposed to different non-financial and financial risks, such as the likelihood a loan cannot be repaid. On the other hand, we are custodians of the trustworthiness of the financial sector. In doing so, we ensure that our regulatory compliant models instill trust with our clients throughout their journey with the bank.


  • IFRS 9 is an International Financial Reporting Standard. As a bank within risk, we are looking at different angles of how we can manage risk to offer the best service to our customers whilst meeting regulatory requirements as well as being viable as a business. In doing so, you will come to different topics such as Credit Risk, Market Risk and/or IFRS 9 and Basel IV.

  • Good data management forms the foundation of accurate data analyses. Our goal is to make accurate, complete and consistent data easily accessible to the right employees. To do so, we identify what data is required, where it resides, how it flows throughout the organization, and who is responsible for this data.

  • Risk analytics is involved in the (re)development and maintenance of credit models in banking. These quantitative models, such as PD (Probability of Default), EAD (Exposure at Default), LGD (Loss Given Default), RAROC (Risk-adjusted return on Capital), Provisioning, and Securitization, are required to comply with regulatory requirements (Basel, CRR, IFRS9) as well as input for (senior) risk management within the decision making process. Their objective is to ensure development and maintenance of high quality, best practice models that comply with business needs and furthermore comply with regulatory standards and requirements.

Data & Analytics at Rabobank

Data Management is a part of Data & Analytics. Discover everything about working within this challenging area at Rabobank.
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