Jodi Daniels is a privacy consultant and Founder/CEO of Red Clover Advisors, one of the few Women’s Business Enterprises focused on privacy.
Twenty years ago, companies could work within the framework of reactive data privacy programs. Why? Because data collection was (comparatively) limited, privacy laws were less stringent (and less common) and enforcement mechanisms were weaker. Companies only needed to address privacy concerns when a breach occurred or regulators acted. But those days are long gone.
Today’s international and U.S. state consumer privacy laws are evolving rapidly. AI technology is evolving even faster. It doesn’t work anymore to “hold off on figuring out privacy until next quarter.” Here’s why privacy is paramount to your business’ success, especially as more regulations start to require data governance measures:
Where do things stand with data governance and privacy compliance?
Data governance and privacy intersect, but they’re not the same.
Data governance is an entire discipline focused on managing data throughout its lifecycle. It establishes policies for data ownership, quality, access control and usage. It’s the rulebook for how data flows through your organization—who owns it, who can access it, how accurate it is and how long you keep it.
Privacy compliance is about protecting personal information in line with legal requirements. Privacy laws, like the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), as amended by the CPRA, often rely on strong data governance practices, but governance itself goes beyond what privacy regulations require.
For example, a privacy law might say, “Minimize data collection to reduce risk.” A data governance program would go further: “Our marketing team may only collect first names and email addresses, not full birthdates, unless there’s a clear business need.” In other words, data governance is the framework that enables privacy compliance, not the other way around.
How does data governance enable privacy compliance?
At least 20 U.S. states now require some level of data governance, and the number is growing. But even when it falls outside the specific scope of law, data governance still supports privacy compliance by helping to ensure:
• Data Ownership: Governance assigns clear roles for data management, ensuring accountability for privacy practices.
• Data Quality: Accurate, consistent data makes it easier to fulfill privacy rights requests, such as access or deletion.
• Access Control: Least-privilege access ensures sensitive data is only available to those who need it.
• Retention And Deletion: Governance policies enforce automatic data purging once the data is no longer needed, reducing privacy risk.
In short, privacy compliance is more manageable when your company already has clear governance policies in place.
What are the key elements of data governance?
What was compliant yesterday might be a liability tomorrow. Yet, many companies still treat data governance as an afterthought, something to address only when regulators come knocking. A strong governance program requires intentional policies, defined roles and enforcement mechanisms that keep data under control and aligned with business goals.
Data Ownership And Accountability
Data doesn’t manage itself. Without clear ownership, business data can become scattered, inconsistent and difficult to secure. Governance programs must define who is responsible for managing data across departments and how those responsibilities are enforced.
To establish accountability:
• Assign data owners and stewards. Designate individuals responsible for data quality, security and access within their teams.
• Define governance policies. Establish written guidelines outlining data ownership, usage and maintenance requirements.
• Set up governance oversight. A cross-functional committee—including legal, IT, security and operations—should oversee policies and resolve disputes.
• Ensure executive buy-in. Governance only works when leadership supports and enforces it.
Example: A healthcare company might designate its HR team as the data owner for employee records while making IT responsible for system access controls.
Data Minimization
Data collection should be purpose-driven, not excessive. Without minimization policies, companies may store unnecessary personal data, increasing risk and regulatory exposure.
To implement data minimization:
• Set collection limits. Define what data each department can collect based on business needs and legal requirements.
• Create automated retention schedules. Establish rules that delete or archive data after a set period.
• Monitor secondary data use. Require approval for new uses beyond the original purpose.
• Audit collection practices. Regularly review workflows to ensure that excess data isn’t being collected.
Example: A marketing team might be restricted to collecting only first names and email addresses for newsletter sign-ups.
Data Quality
Governance programs should proactively manage data quality. Poor-quality data can disrupt decision making, slow operations and make compliance harder.
To keep data quality up, include these measures:
• Automate validation rules. Flag missing fields, incorrect formats and duplicate records before they enter workflows.
• Standardize data entry. Use consistent naming conventions, date formats and metadata structures across systems.
• Schedule routine audits. Regularly review datasets to detect and fix incorrect records.
• Implement real-time monitoring. Set up automated tools that detect anomalies, such as unexpected spikes in missing data.
Example: A B2B company might set up automatic alerts for sales reps when a customer record lacks ownership details, preventing leads from idling in the pipeline.
Access Control And Security
Data governance is ineffective if employees can access more data than they need. Governance policies should ensure that access is restricted based on necessity, reducing the risk of breaches and noncompliance.
To enforce access controls:
• Define role-based permissions. Restrict data access based on job responsibilities.
• Monitor access logs. Track who is viewing, editing or exporting sensitive data.
• Conduct quarterly access reviews. Remove outdated access and adjust permissions based on role changes.
• Use authentication controls. Require strong passwords, multi-factor authentication or biometric verification for systems with sensitive data.
Example: An HR team might have access to employee performance reviews, while IT can manage system credentials but not personal HR files.
In closing, a proactive data governance program doesn’t just support privacy compliance. It strengthens your entire data ecosystem, improving accuracy, security and efficiency across the board. By establishing clear policies for data ownership, access and quality, you can reduce regulatory risk, improve operational efficiency and build customer trust. And when privacy regulations inevitably evolve, you’ll already have the governance structures in place to adapt quickly.
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