Collibra implementation in Banking: What large financial institutions get right (and what they don’t)

There’s a massive gap between buying a Collibra license and actually extracting value. At Murdio, we’ve seen projects planned for three years get delivered in ten months, and we’ve also seen multi-million dollar implementations turn into shelfware. 

The difference between the two is not just in technical implementation (though working with skilled professionals definitely helps). It starts much earlier than anyone touches any software or code – in the organization’s data culture (or lack thereof). 

Banks are in a very special position here – they usually deal with enormous amounts of sensitive data, and yet, often the first impulse to govern it properly is looming regulations or audit results. And then, the “fun” begins, which often is building that data culture from scratch. Because implementing a tool that nobody understands how and why to use, with no processes in place, is a very short-sighted strategy. (Because it’s no strategy at all.)

So, let’s talk about the challenges of Collibra implementation in banking specifically, and our solutions. 

Key takeaways:

• Implementing Collibra allows banks to establish a single source of truth for data definitions, policies, and ownership.
• The common scenario is a Colibra implementation following regulatory pressure – and usually a deadline. But there’s much more value banks can get from the platform.
• Collibra streamlines adherence to critical regulations such as BCBS 239 or GDPR by providing clear data lineage and audit trails.
• By automating data quality workflows and monitoring, financial institutions can reduce operational risks and enhance decision-making.
• But in order to do the above (and more), Collibra implementation needs to be more than an IT project – it needs to include a strategic overhaul of thinking about data governance within the institution. 

The banking data landscape: why generic implementation advice falls short

Generic data governance advice usually focuses on data democracy and broad accessibility – both indeed very important. In banking, though, the reality is far more rigid. You are doing more than just organizing data. Really, you’re building kind of a Google Map of your financial flows to prove to an auditor that your reports aren’t coming from thin air.

Generic advice fails because it ignores the specific thresholds that banks cross. As a bank grows in assets or clients, it hits new regulatory tiers (like those under FINMA or BCBS 239, depending on jurisdiction) that mandate a level of documentation impossible to manage in Excel anymore. 

And for that, you need a tool that can track lineage across hundreds of legacy applications and identify the single source of truth among seven different systems carrying the same data point. 

And then the situation gets more complex as new regulations come into play, with the AI regulatory landscape complicating it even further.

Where most large bank Collibra implementations win or lose

Ask our Collibra experts, and they’ll tell you that most implementations fail because companies lack a data culture, not the right tech. Looking at the most significant challenges, here’s how we could group them:

1. The shelfware trap

Banks often buy their Collibra licenses because they need to comply with a regulation, but they lack a Solution Architect to build an operating model that fits their structure. 

And when there’s no leader – like a Chief Data Officer (CDO) – to take responsibility for the project, Collibra adoption stalls, and the tool just sits on a shelf. We’ve seen it happen more than we’d like to admit.

Then, when there’s regulatory pressure, and most likely, a deadline, we’re brought in to resuscitate Collibra. Which we don’t mind doing, but we know we need to start much earlier than the platform itself.

2. Top consulting firms vs. specialist experts 

Large consulting firms often deliver impressive 30-slide decks but struggle with hands-on Collibra implementation. We’ve found that banks are often apprehensive of consultants who might have only opened Collibra a handful of times. 

Sure, they probably have in-depth expertise in data governance, but success comes from experts who can actually build things in the software, as well as understand data governance processes. That’s why we often partner up with the Big Four to deliver a complete service, with our detailed knowledge of Collibra.

3. Ownership

This is likely relevant to any enterprise project. And the more so the more the business grows, with more regulatory requirements to fulfill.

If IT buys Collibra but the business doesn’t own the data, you can bet that the metadata will be outdated within months. Plus, people leave organizations, and data access shouldn’t leave with them (yet it so often does.)

Use cases for banking Collibra implementations

Among the multitude of regulations in the banking and finance industry, here are some of the most popular use cases for implementing Collibra (mind you, this list is not exhaustive)

Regulatory data lineage

Showing the end-to-end flow from the system of record to the final regulatory report is key for compliant reporting, including for FINREP (Financial Reporting) and COREP (Common Reporting), both key for banks based in the European Union.

Critical data elements management

Collibra lets you identify data domains and then critical data elements within those domains – the data points that are truly material for financial stability. Those data elements are usually located across systems and relate to each other, and Collibra will make it apparent and clear at a glance.

And it’s more than just having a CDE catalog – it’s managing sensitive data elements later. So, going beyond a static CDE list, building management processes that include updating, adding, removing, and editing CDEs, plus notifying the right people, so that the catalog is always up to date, and sensitive data is managed properly and genuinely, not just on paper.

GDPR & unstructured data governance

Unstructured data can be a real nightmare when it comes to compliance – it’s also where a lot of key data, including sensitive personal data, lives. Collibra is basically indispensable to discover, classify, and catalog structured data, making it compliant with privacy regulations and easily auditable. 

For more practical details, read this case study: Discovering, classifying and cataloging unstructured data for a European bank

Data product governance

Treating data sets as products with owners and access workflows speeds up data management and enables data-driven decision-making, and that includes banks as well. 

Data products democratize data access across organizations, and Collibra provides a centralized platform to manage their entire lifecycle, from creation to consumption – all in a secure and collaborative framework.  

Model risk governance

Model risk governance is essential for banks using AI, as they need to make sure that models are built on high-quality, governed data. And that includes both staying compliant with regulations and simply having only high-quality and relevant data feed AI models.

Read more about how we helped reinforce AI governance in an international bank in this case study: Strengthening AI Governance in a global bank

DORA operational resilience mapping

Collibra lets banks align with DORA’s core requirements by mapping, managing, and reporting on ICT-related risks, especially when integrated with other enterprise data platforms such as SAP. 

Mapping Collibra use cases to banking compliance obligations

Now, let’s take a look at how Collibra fits specifically with the most important data regulations in banking.

BCBS 239 (Risk Data Aggregation and Risk Reporting)

It’s a set of 14 principles issued by the Basel Committee on Banking Supervision in 2013, designed to strengthen banks’ risk data aggregation and reporting capabilities.

It requires banks to prove that their critical data elements (CDEs) are cataloged with clear ownership and lineage. If a report shows a discrepancy, the regulator wants to see exactly which system and which owner is responsible for the bug.

Collibra helps with BCBS 239 by:

• Providing a unified, flexible data governance model, adaptable to evolving regulations and business needs
• Risk data aggregation and reporting, with automated profiling, rule creation, monitoring, and notification across systems and sources
• Supervisory review with clearly defined responsibilities and timelines for audits, and a process for remedial actions and escalation.

DORA (Digital Operational Resilience Act) 

DORA mandates that financial entities improve their ICT risk management, incident reporting, and third-party risk management.

Collibra helps map operational resilience by identifying which critical services depend on which data assets. It also helps monitor and validate data health and reduce human error with built-in data privacy and governance tools.

EBA guidelines

The European Banking Authority (EBA) issues guidelines to harmonize supervisory practices across the EU, covering internal governance, risk management, and regulatory reporting. Many updates align with DORA and other regulations.

Collibra helps comply because you can:

• Catalog and classify ICT assets, services, and data for a comprehensive overview of risk management
• Track end-to-end data lineage from source to final reports, minimizing risk and maintaining an immutable audit trail 
• Map policies to data and technology assets for easy auditability
• Assign clear data ownership for accountability for data quality
• And more.

FINMA / local regulators

FINMA is Switzerland’s independent financial market regulator to supervise banks, insurance companies, financial institutions, collective investment schemes, and their asset managers and fund management companies.

And with this one, let us show you a real-life example.

Here’s a case study showing how we helped a Swiss private bank automate the cataloging and governance of its most sensitive data across 100+ applications using Collibra ahead of the FINMA Circular 2023/01 regulation, which mandated stricter governance and cataloging of sensitive critical data elements (SCDEs):

Case study: Management and cataloging sensitive critical data elements in a Swiss bank

GDPR in banking

Beyond basic privacy regulations, GDPR compliance using Collibra involves managing unstructured data and making sure that data retention and access policies are automated, not just documented in a PDF.

Read more:

Unstructured data cataloging for AI and compliance

Structured vs unstructured data: key differences

Collibra Unstructured AI: Making unstructured data AI-ready

Collibra implementation sequencing for large banks: what to prioritize first

We asked our experts, certified Collibra Rangers with in-depth knowledge of the demands of the banking industry when it comes to data governance, regulatory compliance, and Collibra.

Instead of the proverbial “boiling the ocean” by cataloging every data point in the bank at once, here’s what they recommend:

1. Identify priorities first (Days 1-30). Start with a specific pain point from your last audit or the most urgent regulatory deadline (e.g., Risk and Finance divisions).
2. Define the operating model and establish who the Data Owners and Stewards are before you start clicking in the tool.
3. A hands-on pilot implementation (Days 30-90) – instead of slides, show the business a working lineage of one critical report.
4. Automate maintenance by building the “Request Access” and “Add New CDE” workflows early so the system stays up-to-date as the bank evolves.

Integration challenges specific to banking environments

Banks are often piles of legacy systems. In addition to the technical connectors, implementing Collibra requires navigating internal silos – and the tech is often the easier challenge to overcome between the two. 

We often see IT teams that are too busy to provide access, or shadow IT systems where critical data lives outside of official catalogs. 

The real challenge is building automated maintenance processes so that when a new SCD is added to an application, it’s automatically flagged in Collibra, for instant and continuous audit-readiness rather than waiting for a manual yearly audit.

The bottom line 

Banking is one of the industries that can benefit immensely from a properly implemented and used Collibra. But there’s one key requirement for it to succeed: rather than just an IT project, you need to treat it as a strategic initiative that transforms how the bank perceives and uses its data. 

Yes, the initial setup requires significant coordination across departments, but the long-term benefits of regulatory readiness, reduced data redundancy, and trusted analytics provide a substantial return on investment.

And if your banking institution needs help – we’re here to help (and have done it multiple times before).

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