Blog Podcast 4 Min Read

Driving Data Governance and Data Products at ING Bank France

Blog Podcast 4 Min Read
By: Sara Petrie
Posted on: August 12, 2021
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Data+AI Battlescars Takeaways: Driving Data Governance and Data Products at ING Bank France

In this episode of Data+AI Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Samir Boualla, CDO at ING Bank France, one of the largest banks in the world. They cover his battlescars in Driving Data Governance Across Business Teams and Building Data Products.

Samir BouallaAt ING Bank France, Samir is the Chief Data Officer. He’s responsible for several teams that govern, develop, and manage data infrastructure and data assets to deliver value to the business. With over 20+ years of experience on various data topics, Samir shares interesting battle-tested techniques in this podcast, including using a process catalog, having a “data minimum standard,” and having a change management mindset. Here are the key takeaways from their chat.

Data Governance

Preparing for Data Governance

  • The first step to preparing for data governance is defining data governance frameworks, guidelines, and principles as part of your organization’s data strategy and discussing them with various stakeholders.
  • Next, you need to get approval and validation from your directors. It is important to have support and commitment from senior leaders since it will be impacting the whole organization.
  • After that, you can identify the appropriate people that should be assigned to roles such as data steward, data owner, or process lead.
  • It is important to implement data governance in parallel with building a data architecture. You can identify and close gaps quickly so you don’t develop something which later on is not compliant with a regulation or with other frameworks.

Having a Data and a Process Catalog

  • In order to monitor compliance proactively, you must know your data. This is where a data catalog plays an important role.
  • A data catalog allows you to have uniform definitions in place, have a data owner for each of the specific data categories, and have people who have more in-depth knowledge and the capability to manage data acting as data stewards.
  • Having a process catalog in addition to a data catalog allows you to seamlessly track the data life cycle.
  • In a process catalog, it’s documented, for each process, what sources are used, who is doing what, and which data is being used in those processes.
  • The process catalog is linked with your data catalog and used to manage your life cycle and apply additional data capabilities, like retention and deletion, on the appropriate systems or appropriate process steps.

Data Minimum Standards

  • Data minimum standards are key frameworks that can give you insight and input on the controls and checks that should be part of your minimum standards for governance.
  • Having these data minimum standards, such as GDPR and BCBS 239, allows you to proactively monitor for compliance and apply governance.
  • These data minimum standards are part of your compliance framework, so you are able to apply them to all kinds of different processes or departments.
  • ING Bank is constantly assessing if the controls work, testing them for effectiveness, and auditing them every once in a while.

Data Products

Building External-Facing Data Products

  • In preparation for building external-facing data products, you need to have a set of standardized APIs as a product, which you can deliver to third parties for external consumers.
  • At the same time, you should also be using another product as your data catalog to make sure that the data that is being defined and flowing through those APIs are made unique.
  • The biggest challenges ING faces when building external-facing data products is making sure they are acting more or less on the edge of technology and architecture, while also ensuring that they are working towards their goal of becoming a data-driven bank.
  • They encounter several challenges in making sure that their platforms are compatible and can exchange data in the right formats and in the right structure, but also in a way that the infrastructure remains scalable.

Challenges of Building Data Products

  • Sometimes when a product is based on data quality, while Samir hopes to identify upfront any data quality issues, somewhere down the line the consumer may identify issues or have questions regarding the quality of the data. This is where another data product, called remediation, can come into play.
  • Remediation is when a consumer can address data quality issues directly to the appropriate data stewards in the organization. Using other complementary products, a consumer can look into certain data or to a certain report to identify which data point came from where and who’s the data steward or data owner of that specific data. They can then address it automatically in a workflow environment, and request remediation.
  • When building a data product, you may run into manual, legacy processes that have not yet been redesigned.

Change Management

  • Having a change management mindset means that you are willing to implement something new or change something from the legacy based on new data products.
  • A standard data model and data catalog are essential when it comes to change management.
  • By having a single data model and a data catalog, you have a decoupling layer which helps you and supports you in the exercise of identifying what that data point reflecting the truth exactly is.
  • ING has a broad framework, which allows them to work in a similar, agile way across the organization, and across the globe, whenever something needs to be adjusted.
  • Their data products also allow them to minimize the amount of effort that needs to be put into a change to make it available.

While we’ve highlighted the key talking points here, Sandeep and Samir talked about so much more in the full podcast. Be sure to check out the full episode and the rest of the Data+AI Battlescars podcast series!