In the banking sector, data governance is about meeting both regulatory and internal requirements. Learn the requirements of your data governance plan by reading this guide.
As most companies today face significant data overload, the importance of effective data governance frameworks continues to grow. Banks in particular need to derive value from data both to innovate and modernize their operations and to continue to comply with and ethically manage the data in which they work. In this guide, we detail how and why banks can benefit from a robust data governance framework.
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How does data governance work in a bank?
Today’s economic landscape requires most, if not all industries to strengthen their data-driven capabilities in the marketplace to maintain a competitive edge. The banking sector is no exception. The advent of data governance models in banking gives banks the resources they need to upgrade existing policies and procedures to improve their data protection mechanisms.
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Data governance specifications also improve banks’ ability to analyze data to make better decisions. Data governance in banking means providing the tools for the banking sector to not only optimize its efficiency and innovation, but also support risk management and regulatory reporting.
What does a data governance program include?
A data governance program typically includes:
- Set data format standards.
- Identify the data to be protected.
- Tag data types and assign roles and responsibilities.
- Establish metrics to quantify the effectiveness of the governance program.
- Transmission automation.
- Use data-driven programs to evaluate, define, monitor, and improve the governance program.
How data governance delivers value in banking
Regulatory compliance
The requirement for banks is to keep all the data they have, based on a variety of federal and state compliance regulations. Regulatory requirements continue to put pressure on the banking industry to control data management as the consequences of data breaches become more and more costly.
With the right data governance plan in place, banks always know exactly what data they have access to. They also always know where the data is located, which ensures they can enforce the right controls – even in complex projects like cloud migrations.
Awareness of where data is located, the regulations it is subject to, and the correct approach to protection are key to successful cloud migrations and other digital transformation projects. Data governance provides proper tagging to ensure banks meet regulatory requirements with correct access rights and security controls.
Cut down the costs
Manual data management is tedious, inefficient and expensive. The responsibility for manual data management is often placed before IT teams, which means financial institutions often have to pay the cost of keeping IT teams up and running.
Data governance eases the manual burden of discovering, granting access, and implementing security for data through centralized technologies, effectively ending the need for multiple costly third-party systems. poor and crowded IT team. The self-service capabilities of many data governance tools ensure that organizations maintain secure data access without incurring unnecessary costs.
Market details
The current financial sector is characterized by constant competition between institutions and saturation for new entrants. As a result, market insights have become a necessity to gain a competitive advantage. Through data analytics initiatives, banks can confidently access their data and gain useful insights.
Data governance supports the analysis of the whole company and its processes, ensuring that data is easily accessible and well-organized. This makes it easier to innovate and use data across the organization instead of just delegating responsibility to leadership teams.
Data-driven culture
Data-driven models are increasingly transforming the way organizations handle business goals and objectives. A data-driven culture is proving to be of great benefit to organizations, as it visually improves approaches to cost cutting, innovation, and customer insights. Data governance supports and encourages a data-driven culture so that banks can more efficiently run their operations and make decisions focused on the customer experience.
Use cases for data governance in banking
Cooperation and risk management
Banking institutions work with hundreds of data sources and require a way to record the data they have. They also need to use data to manage and acquire new customers, identify fraud, and reduce risk. With support from data governance processes and procedures, banks create data catalogs to easily discover and evaluate data quality. The result is better collaboration and decision making and improved productivity.
Mission Lane works with Alation is a great example of how data governance can help improve collaboration and manage risk.
Improve compliance and customer service
As customer experience and secure data controls become more important to consumers, organizations like Third Thursday Bank are evolving their data governance approaches to be more efficient and less invasive, improving both compliance and customer experience.
Financial institutions are also looking for ways to provide a more personalized customer experience for their customers, but struggle when they encounter fragmented and patchy data. NCBA is a financial services organization that tackles this challenge by adopting different customer experience platforms to enable the organization to track the customer journey from start to finish. Through this approach, they can gain insights into customer patterns and improve the experience for their customers.
Data governance best practices in the banking sector
Understand and apply regulatory compliance best practices
Data privacy scrutiny is on the rise, especially for personally identifiable information, due to the severity and frequency of data breaches across industries. Current and future legislation is geared towards protecting consumers and giving them control over their data privacy.
A key component of successful data governance in the banking sector is ensuring that your organization complies with all the regulations it is subject to. Regularly self-test data platforms and operations help both customers and regulators feel comfortable with your data management methods.
Focus on execution
The quality of the data governance implementation determines the success of the data governance procedures in the long run. To begin with, financial institutions should thoroughly understand the regulations and compliance issues they face to assist them in identifying limitations in their current data governance processes.
During implementation, data must be treated as an asset; Property thinking ensures that data is cared for and protected. Additionally, every member of the leadership team in a banking organization must have a working knowledge of data governance plans and how they relate to the day-to-day operations of the team.
Regularly monitor key indicators
Once properly implemented, the success of data governance procedures must be periodically evaluated by monitoring various performance metrics. Some important metrics to monitor include data quality score and frequency of risk and security incidents. These metrics will also notify you of any issues and can provide insights into cost and profit cuts.
Evolving data governance over time
Data governance is an ongoing process, especially in organizations such as financial institutions, where data sets are complex and highly managed. To keep your governance initiatives effective and relevant, make sure they evolve with your organization. Evaluating your data governance program on a regular basis ensures that it addresses your pain points, even as internal objectives and external risk factors change.
