Data governance matters: Trusting your data to support transformation

In an era of digital transformation, data serves as the lifeblood of decision-making and innovation.

Imagine a bank where data isn't just numbers and codes, but a powerful tool that fuels better decisions and customer experiences. That's the magic of robust data governance in banking. It's like having a trusty compass on a journey; it ensures that data is accurate, consistent, and securely managed. Good data governance isn't just about complying with rules – it's about unlocking a world of benefits.

As a data governance specialist, I know that it’s not everyone’s favorite IT topic or practice. Yet, all the consequences of poor data governance are quite real – and all too common. It can amplify operational inefficiencies, increase the risk of financial errors, hinder regulatory compliance and erode customer trust. Whilst data governance is one of the key enablers of cloud, AI, personalization, and open banking. Implementing robust data governance practices can contribute to a bank's overall resilience, competitiveness, and ability to navigate complex industry challenges.

Getting to the root of poor data governance

No one sets out to have poorly governed data. In financial services, the opposite is more likely to be true. Major initiatives include a plan for data governance. These plans call for a clear governance process, along with a data catalog application and adequate staff to manage it. Then, as initiatives progress, deadlines get closer. Other aspects of the initiative go over budget. In an effort to get the rest of the project on track, data governance loses staffing, timeline, and budget.

Why does data governance seem like it’s an aspect of projects that can slide, especially at the beginning? IT leaders often look back at past projects and wish that more proactive data governance was baked in. But when looking at current and future projects, they see orderly data contributing. Data governance plans look like overkill for this tidy data. So it seems sensible to move budget away from data governance resources. Organizations replace robust governance processes and applications with a list of recommended practices and manual tracking tools. And often, people fail to follow the processes or use the tools.

Data becomes messy with time and use. People who know where the data came from can leave. The remaining staff may not know who to ask about it. Lax data governance leads to serious consequences, especially on transformation initiatives. Consider how data governance can help or hinder the following at banks:

• Machine learning: Building machine learning models on top of questionable data leads to the development of algorithms that are not fit for purpose.
• Personalization: You need the right data to deliver the right offer to the right person at the right time. Just as crucially, data governance helps ensure that you’re using data in compliant ways.
• Cloud: Data in the cloud becomes less orderly over time just as it does on-premise, but the unlimited capacity of the cloud can also lead to a costly proliferation of unused and duplicate data sets.

Keeping the house tidy

Data governance is a bit like tidying the house – fantastic, but hard to do consistently without discipline and the right tools. Imagine looking at your perfectly clean home and canceling your weekly cleaners. It’s reasonable, and correct, to think that it’s easier to keep a house clean than it is to get it clean. You just have to do the small tasks that keep things straight. Things look good for a few weeks with just a little effort.

But time passes and entropy sets in (and no one wants to drag out the heavy old vacuum). A few months later you’re asking the cleaners to return. And paying a bit extra to get the place back into shape.

Like your house, data moves from a state of organization to increasing chaos as people use it. An analyst sets up and runs a report to help the business respond to an urgent situation. Years later the report is still running. No one knows why it’s running, if it’s still needed, or if the contributing data is still adequate for the task. Multiply similar data governance problems over the years, and you see the scope of the issue.

At a high level, preventing data chaos is simple. You need to:

• Develop a plan that includes clear roles and responsibilities along with adequate resources
• Automate as many tasks as possible
• Hold people accountable for any aspect of the plan that can’t be automated
• Follow the plan and use your data governance tools while looking for new automation opportunities
• Adjust the plan as needed as your data strategy evolves

Build data governance into your transformation

With well-governed data, banks can analyze the preferences and habits to offer tailor-made services, making their customers feel like a valued individual rather than a face in the crowd. And the cherry on top? Efficiency. Think about breezing through loan applications or transactions with minimal fuss. Good data governance streamlines processes, reducing errors and cutting down on time wasted in a sea of paperwork. So, whether you're a tech wizard or just dipping your toes into the banking world, understanding data governance isn't just insightful – it's empowering.

Are you ready to enjoy the – nearly – endless benefits of good data governance? At Amdocs, we take data governance into account as we deliver transformation projects. From cloud to GenAI and personalization to data migration, we help banks achieve and sustain desired outcomes. Talk to us about how we can help you build leading data governance practices and tools into your bank’s transformation.