Banks today collect, store, and process massive volumes of data related to their customers. All this information, including customer personal details and transactional records, has to be protected from unauthorized access and potential compromise. It must be stored in an easy-to-manage format and thoroughly analyzed to allow banks to improve their workflow and maximize return on investment (ROI). But how do banks and financial institutions achieve this?
Effective data governance is the key to making this possible.
However, if not done right, poor data governance can cause errors, inconsistencies, and inaccuracies in data, leading to financial fraud and identity theft.
So, how can banks and financial institutions maintain good data governance while overcoming the implementation hurdles?
Let’s see some best practices financial organizations can follow to ensure effective data governance.
Why is data governance necessary for banking and finance?
Given the financial industry's vast amounts of data created at high velocity, maintaining consistency can be difficult, especially when data comes from multiple sources and departments.
For instance, determining average transaction durations across multiple branches can be difficult if each branch employs different methodologies.
Without a data governance strategy, financial organizations and banks cannot rely on their data to make informed decisions.
Data governance is necessary for banks and financial institutions for the following reasons:
- Ensure data accuracy and integrity: Data governance helps banking and finance institutions ensure the data they use and store is accurate and reliable. This allows them to minimize errors and inconsistencies through standardized data entry, storage, and management processes.
- Improve regulatory compliance: The banking and finance sector is heavily regulated, with strict data privacy, security, and reporting requirements. Data governance ensures companies handle data according to legal standards and comply with regulations.
- Enhance decision-making: When data is complete, accurate, consistent, up-to-date, understandable, and relevant, it becomes the raw material for business intelligence (BI) operations. Financial organizations can utilize this information to plan, analyze trends, forecast growth, and make sound business decisions.
- Improve data quality and consistency: A sound data governance program sets the organization's data quality standards. It defines what 'good' data quality looks like, which metrics to use, and how to measure it. By establishing clear rules for data management, it ensures data consistency and high quality throughout the organization.
- Support business continuity: Effective data governance is critical to business continuity planning. Financial institutions can recover quickly from disruptions such as cyberattacks, natural disasters, and system failures by ensuring that data is accurate, secure, and backed up.
Data governance implementation challenges
While data governance offers several benefits, implementing a successful program can be complex due to various factors. These may include organizational culture, insufficient support from top leadership, and resource constraints.
Below are some of the challenges companies deal with when establishing data governance strategies:
- Data silos: Data silos can affect the flow of information within an organization. They often arise from departmental separation, where different units store data independently. This makes it hard to analyze data effectively and derive insights.
- Compliance with regulatory standards: Data privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) complicate data governance. Organizations should ensure their data practices comply with these regulations, which require additional resources and expertise.
- Insufficient high-level support: Data governance requires commitment from the top. Without executive-level support, data governance initiatives often lack funding and strategic direction. Strong leadership support is essential to ensure everyone understands the importance of data governance and is willing to participate.
- Data quality issues: Inaccurate or incomplete data undermines the entire data governance effort. Data quality issues can lead to poor decision-making and a lack of trust in data-driven insights. Companies should promote reliable processes to maintain data accuracy and reliability.
- Complex data environments: Modern data analysis involves wrangling data from disparate sources and formats. Integrating and managing this data in one format for data governance requires different strategies and tools. This adds to another factor of complexity for governance.
- Resource constraints: Budgetary restraints can restrict the scope and depth of data governance. Limited budget, lack of technology, or human expertise can impact data governance efforts.
- Technology limitations: Technology is evolving, and current capabilities may not be sufficient to address all the existing and potential challenges. For instance, some problems may require specialized hardware that is not yet available, or they may need specific algorithms for tasks that have not yet been developed.
- Lack of skilled personnel: There is often a shortage of qualified personnel to implement innovations in data governance. This lack of manpower can affect the implementation of new technologies and slow down innovation.
Data governance best practices
Although many companies have data governance programs, their effectiveness is not always guaranteed. Poor data governance can lead to inconsistency in data, and data quality issues can be costly for organizations. To ensure a successful data governance strategy, it is essential to follow best practices.
Here are actionable set of data governance best practices that can help you improve data management and maximize the value of your data.
1. Create a data governance framework
Develop a structured approach to managing data within your organization. This framework should outline your data governance goals, principles, and components. For example, you can include governance and lineage policies for data access, usage, and security in your framework. This helps ensure everyone in the organization understands how to handle data appropriately.
2. Establish clear data governance policies and procedures
Set specific rules and guidelines for data management. This includes how data should be collected, stored, processed, and shared. For instance, you might have a policy that customer data must be encrypted and stored in a secure database, ensuring only authorized personnel can access it.
3. Define roles and responsibilities
Assign specific roles and responsibilities related to data governance. These may include data stewards, custodians, and owners. Clear roles help ensure accountability and proper data management.
For example, a data steward may be responsible for defining data quality standards and monitoring compliance, while a data custodian manages data storage and access permissions.
4. Develop a data quality management strategy
A data quality management strategy outlines processes and procedures to maintain high data quality standards. This strategy includes activities such as:
- Data cleansing: Regularly clean and correct data to remove duplicates, inconsistencies, and inaccuracies.
- Data validation: Implement validation checks during data entry to ensure completeness, accuracy, and conformity to predefined rules.
- Data monitoring: Continuously monitor data quality metrics and trends to identify and resolve issues. For example, setting up alerts for unusual data patterns or variations that may indicate data quality issues.
5. Regularly track data governance metrics and KPIs
Monitor key performance indicators (KPIs) to evaluate the effectiveness of your data governance efforts. Regular tracking helps identify areas for improvement.
Metrics might include:
- Data accuracy rates: Measure the percentage of accurate data entries compared to total data records. High accuracy rates indicate strong data validation and cleansing processes.
- Number of data breaches: Track the frequency and severity of data breaches within your organization. Lower numbers indicate effective data security measures and incident response protocols.
- Compliance levels: Monitor compliance with data-related regulations and internal policies. Assessing compliance levels helps mitigate risks associated with regulatory penalties and reputational damage.
6. Optimize resource usage
Ensure that your data governance efforts are efficient and cost-effective. Allocate resources strategically by prioritizing critical data assets and processes. For example, allocating more resources to secure customer data while optimizing costs for less critical data sets ensures balanced resource management and enhances overall data governance efficiency.
7. Ensure compliance with regulations
Stay up-to-date with data-related laws and regulations, such as the GDPR. Implement policies and practices that meet these legal requirements to avoid penalties and maintain customer trust. For instance, procedures for data subject access requests must be built to comply with privacy laws.
8. Hire data governance professionals
Employ experts in data governance to lead and manage your initiatives. These professionals have specialized knowledge and skills to ensure the success of your data governance program. They can help design policies, oversee implementation, and provide ongoing support.
9. Promote training and awareness
Educate employees about data governance policies and practices. Conduct regular training sessions to keep everyone informed about their responsibilities and the importance of data governance. For example, provide workshops on data security best practices. These workshops should cover data security protocols, privacy regulations, and data handling procedures.
10. Invest in suitable data governance technology
Use data governance tools and software to support data governance activities. These technologies may include data cataloging tools for metadata management, data quality tools for monitoring and improving data integrity, or compliance automation tools for managing regulatory requirements.
Build data integrity and trust to ensure data governance with Secoda
Effective data governance helps maintain data quality, ensure compliance, and support business continuity. However, many organizations face challenges due to the complexity of managing large data sets and evolving regulatory environments.
So, how can you achieve effective data governance? Automation is the answer!
Investing in tools like Secoda can enhance your data governance efforts. Secoda is an AI-powered data governance platform that enables secure access to data within modern data stacks. It automates data governance workflows, manages permissions, and keeps resources up-to-date.
Here’s what Secoda offers:
- Automatically identifies and tags Personally Identifiable Information (PII), ensuring documentation is always current.
- Controls access to specific data assets internally and externally with granular role-based access control (RBAC).
- Discovers, categorizes, tags, and regulates sensitive data across your resources.
- SOC 2 compliant, supporting self-hosted environments, SAML, SSO, and MFA, with complete data encryption in transit and at rest.
- Integrates with your entire data stack using out-of-the-box connections and flexible APIs.
Don’t let data governance challenges hold your organization back. Book a demo today to see how Secoda can transform your data governance!
