In banking, data governance is about meeting both regulatory and internal requirements. Learn what’s required of your data governance plans by reading this guide.
With most businesses today facing massive data overload, the importance of effective data governance frameworks continues to grow. Banks in particular need to derive value from data both for the innovation and modernization of their operations as well as for the ongoing compliance and ethical management of the data they work with. In this guide, we detail how and why banks can benefit from a strong data governance framework.
How does data governance work in banking?
Today’s economic landscape demands that most, if not all, industries improve their data-driven capabilities in the market in order to maintain a competitive edge. The banking sector is no exception. The introduction of data governance models in banking gives banks the resources they need to upgrade their current procedures and policies to improve their data protection mechanisms.
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Data governance specifications also improve banks’ data analysis capabilities for better decision-making. Data governance in banking means providing tools to the banking industry not only to optimize its efficiency and innovation, but also to support risk management and regulatory reporting.
What does a data governance program include?
A data governance program often involves:
- Installation of data format standards.
- Identify data that needs protection.
- Tagging data types and assigning roles and responsibilities.
- Implementation of indicators to quantify the effectiveness of the governance program.
- Infuse automation.
- Use measurement-based programs to assess, identify, monitor and improve the governance program.
How Data Governance Brings Value in Banking
This is a requirement for banks to keep all data they have safe, based on a variety of federal and state compliance regulations. Regulatory requirements continue to pressure the banking industry to master data governance as the consequences of data breaches become 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, ensuring they can apply the right controls, even during complex projects such as cloud migrations.
Knowing where the data resides, the regulations it is subject to, and the appropriate approach to protection is key to successful cloud migration and other digital transformation projects. Data governance provides relevant tagging to ensure banks meet regulatory requirements with appropriate access and security controls.
Manual data management is tedious, inefficient and expensive. The responsibility for manual data management often falls to IT teams, which means that financial institutions frequently bear the costs of maintaining active IT teams.
Data governance eases the manual burden of discovering, granting access, and enforcing data security through centralization technologies, ending the need for multiple costly third-party systems and sprawling IT teams. The self-service capabilities of many data governance tools ensure that organizations maintain secure access to data without incurring unnecessary costs.
The financial sector is now characterized by fierce competition between institutions and a saturation of new entrants. As a result, market knowledge has become a necessity for competitive advantage. Through data analytics initiatives, banks can approach their data with confidence and derive actionable insights.
Data governance supports enterprise-wide analytics and processes, ensuring easy access to data and keeping it well organized. This facilitates innovation and the use of data throughout the organization instead of leaving the responsibility to the management teams alone.
Data-driven models are increasingly transforming the way organizations manage business goals. A data-driven culture proves highly beneficial to organizations as it intuitively improves approaches to cost reduction, innovation, and customer insights. Data governance supports and encourages a data-driven culture so that banks can more effectively manage their operations and make decisions focused on the customer experience.
Data Governance Use Cases in Banking
Collaboration and risk management
Banking institutions work with hundreds of data sources and need a way to record the data they have. They must also use the data to manage and acquire new customers, detect fraud and reduce risk. With the support of data governance processes and procedures, banks create data catalogs to facilitate both data discovery and quality assessment. The result is better collaboration and better decision-making and improved productivity.
Mission Lane’s work with Alation is a great example of how data governance can support improved collaboration and risk management.
Improved compliance and customer service
As customer experience and secure data controls become more important to consumers, institutions such as Fifth Third Bank are evolving their data governance approaches to become more efficient and less invasive, improving both compliance and the customer experience.
Financial entities are also looking to deliver more personalized customer experiences to their customers, but are struggling as they encounter patchy and segmented data. NCBA is a financial services institution that addresses this challenge by adopting various customer experience platforms to enable the organization to follow the customer journey from start to finish. Through this approach, they are able to gain insights into customer patterns and improve their customer experiences.
Data Governance Best Practices in the Banking Industry
Understand and apply regulatory compliance best practices
Data privacy scrutiny is increasing, especially for personally identifiable information, due to the severity and frequency of data breaches across all industries. Current and future legislation aims to protect consumers and give them control over the privacy of their data.
A key part of successful data governance in banking is ensuring that your organization is compliant with all the regulations it is subject to. Regular self-audits of data platforms and operations help customers and regulators feel comfortable with your data management practices.
Focus on implementation
The quality of data governance implementation determines the success of data governance procedures in the long run. To begin with, financial institutions need to have a good understanding of the regulations and compliance issues they face in order to help them identify the limitations of their current data governance processes.
Throughout the implementation process, data should be treated as an asset; an asset mentality ensures that data is maintained and protected. In addition, every member of a banking institution’s management team should have a working knowledge of data governance plans and how they relate to the day-to-day operations of their teams.
Regularly monitor key indicators
Once implemented properly, the success of data governance procedures should be evaluated periodically by monitoring various performance metrics. Important metrics to monitor include data quality scores and the frequency of security risks and incidents. These metrics should also alert you to any issues and can give you insight into cost reduction and profitability.
Evolution of data governance over time
Data governance is an ongoing process, especially in organizations such as financial institutions, where data sets are complex and highly regulated. To keep your governance initiatives effective and relevant, make sure they evolve with your organization. Regularly evaluating your data governance program ensures that the program addresses your pain points, even when internal goals and external risk factors change.