Data Governance Best Practices: Security and Compliance

Data has become an invaluable resource for organizations. It’s driving the AI revolution and is used daily to help business leaders make critical decisions. However, there’s also a responsibility to protect the information collected from customers, vendors, and other stakeholders. 

Data governance practices are a crucial part of workplace modernization. The framework you set up to keep data secure, accessible, and accurate should be the backbone of your business’s data strategy. Neglecting data governance best practices can have far-reaching consequences on regulatory compliance and your company’s reputation. 

This article outlines ways that organizations can establish a data governance framework to overcome internal inefficiencies. You’ll also learn how to create a data environment that encourages innovation while empowering workers to use data responsibly.  

Best Practices of Data Governance

Integrating data governance best practices into your data processes helps you avoid scenarios where your company has information spread across multiple systems. That leads to instances where, for example, a sales department ends up with outdated information that bogs down efforts to generate new leads. 

A lack of data integrity can lead to lost revenue, frustrated customers, and missed business opportunities. Let’s look at some ,master data governance best practices your business should follow to avoid these roadblocks. 

Creating a Data-Driven Culture

Company leaders must champion the importance of strong data access governance best practices and set a vision for how it will impact business outcomes. That includes clearly articulating how following best practices leads to better customer service, improves risk management, and helps the company find new ways to expand. 

One way to emphasize the importance of data governance is to hire a chief data officer (CDO). This person can oversee data governance, strategy, and other data culture initiatives, making it easier to keep the organization aligned on following a unified set of data standards. 

Establishing a Data Governance Framework

Banks are highly regulated environments due to the critical nature of protecting customer data, which makes a successful data governance program even more critical. Some of the ways that financial institutions can create a data governance framework tailored to their culture include:

• Creating a data governance council: Create a team with representatives from areas like IT, compliance, and customer service to oversee governance initiatives.
• Focus on data security: Establish strict controls, anonymization techniques, and encryption to prevent unauthorized access to sensitive customer information. 
• Aligning with regulatory requirements: Create policies that comply with regulations like PCI-DSS and GDPR. Financial institutions can set up processes to report transactional data promptly to regulators.

In contrast, retail companies might need a framework that accommodates a culture of agility and customer focus. For example, using a customer relationship management (CRM) platform to centralize customer data can provide multiple teams with a single source of truth. You can also add configurations to enforce data entry standards and track data changes. 

Organizations should find ways to create a data governance framework that reflects their specific needs. What a healthcare company requires would be different from what a technology company requires as far as privacy regulations. 

Fostering Stakeholder Collaboration

Bring together stakeholders from every part of the company impacted by data, which is everywhere. These initial teams often include individuals from executive leadership, IT, and compliance and legal. From there, organizations can work to get buy-in from everyone by doing the following:

• Emphasizing the positive impact on business outcomes (better sales conversion rates, higher customer satisfaction scores, etc.)
• Showcasing how data governance can solve current pain points
• Providing success stories of how similar organizations achieved success through effective data governance

Stakeholders should remain involved throughout policy development to ensure nothing is overlooked. Try testing policies in certain areas before rolling them out to the entire organization. This allows for adjustments based on feedback. 

Implementing Data Quality Policies

Once you get stakeholders involved, you need to define clear data quality standards that align with your organization's goals and regulatory requirements. That typically includes:

• Making sure data is error-free and reflects real-world values
• Keeping data consistent across various systems
• Updating data and making it available when needed
• Ensuring data is meaningful and useful for the intended purpose
• Making sure required data fields get populated
• Minimizing or eliminating duplicate records

Enhancing Data Integrity

Start by establishing a data governance framework that defines who owns what data. From there, you should assign stewardship roles and ensure the data standards reflect your industry’s standards. For example, a healthcare company should use the Health Insurance Portability and Accountability Act (HIPAA) as a standard for implementing data quality management processes, including:

• Using validation rules during data entry
• Performing data profiling and cleansing to eliminate inconsistencies
• Investing in AI/ML tools to perform automated data quality checks

Organizations should implement other data integrity checks, including:

• Maintaining audit logs
• Implementing role-based access control (RBAC)
• Encrypting sensitive data at rest and in transit

Choosing the Right Tools for Governance

Before you make any purchases, make sure you understand what your current challenges are in data governance. What gaps are you looking to address? Is it a lack of data quality controls, issues aligning with compliance, or poor data visibility? 

Some companies want to focus on incorporating business intelligence to make better use of their data, while others may need tools to improve data quality and integrity. Set your data tool priorities based on the need for features like data cataloging, access control, and audit trails. 

Ensuring Adherence to Compliance Standards

Companies should set up training sessions that educate employees on data governance best practices. They should make these available in multiple formats, including workshops and e-learning courses. They should also try to customize the training to fit the needs of different teams, such as marketing, HR, finance, and IT. 

In addition, companies should keep employees updated by sending regular alerts on policy changes or new regulations that could impact their current governance policies. Make this information available through a central knowledge hub with an FAQ about compliance questions. 

Establishing KPIs for Data Governance Success Measurement

Use key drivers for your industry to help you select the right key performance indicators (KPIs). A healthcare provider might emphasize data security and patient data accuracy, while finance companies may focus on KPIs tied to risk management and transaction integrity. Examples of common KPIs include:

• Data accuracy rate
• Duplicate data rate
• Regulatory compliance score
• Number of compliance violations
• Unauthorized access attempts
• Policy adoption rate
• Data issue resolution time
• Data usage rate
• Data trust index

Continuous Adaptation of Data Governance Practices

Data governance is an ever-evolving practice, so businesses need tools capable of adapting to changing data governance needs. Dell Technologies provides guidance to companies looking for the best solutions for handling modern data challenges. Click here to learn more about the benefits of joining the Dell Technologies community.