What is Data Compliance? Tools, Benefits & Key Metrics

Organizations deal with ever-increasing volumes of data. More data translates into more risk, as hackers have a larger target area. This is where data compliance comes in. It helps mitigate these threats and protect consumer data by setting compliance standards that companies and individuals must adhere to while working with data. 

How does data compliance work, and what can teams do to establish more efficient compliance practices? Keep reading to find out.

What is Data Compliance?

Data compliance is all about managing personal and sensitive data in line with regulatory obligations, industry standards, and organizational rules governing data security and privacy.

Data compliance standards might vary by sector, area, and country, but they typically share common objectives like: 

• Ensuring data accuracy
• Providing individuals with awareness of their data rights
• Protecting sensitive information from illegal access or data breaches
• Tracking data storage, including the types of data an organization keeps, the amount of data stored, and how it is handled throughout its lifecycle

The General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA) are three of the most commonly used data compliance regulations.

Noncompliance with these standards can increase cybersecurity risks and result in large fines, punitive penalties, and reputational damage. Thus, data compliance is an essential component of an organization’s entire data governance and risk management plan.

Data Compliance vs Data Security Compliance

Data compliance and data security compliance sound very similar, but the latter refers to a smaller subset of data compliance.

While data compliance encompasses the broader set of rules and regulations organizations must follow when dealing with data, data security compliance focuses on the security aspects of data management: protecting data from unauthorized access, breaches, and other security threats via data security solutions such as encryption, access controls, firewalls, security audits, and so on.

Data Compliance Metrics and KPIs

1. Data Access & Security KPIs

Data access and security Key Performance Indicators (KPIs) are metrics teams use to assess the efficiency of an organization’s efforts in safeguarding sensitive data from unauthorized access and breaches. Such KPIs help companies determine their security posture, identify vulnerabilities, and ensure compliance with data protection legislation.

2. Data Retention & Lifecycle Metrics

Data retention and lifecycle metrics are critical for maintaining data in accordance with regulatory and business requirements. They ensure that data is stored, accessed, and disposed of properly throughout its lifecycle. Data retention durations, archiving frequency, and deletion latency are all important variables for optimizing storage, reducing costs, and ensuring compliance.

3. Data Classification & Tagging

Data classification and tagging are critical to maintaining data compliance. They include categorizing and labeling data based on sensitivity, kind, and regulatory requirements to simplify correct management, access control, and protection. This method is critical for meeting legal requirements, controlling risks, and optimizing data utilization.

4. Reproducibility & Auditability KPIs

Reproducibility and auditability are critical for data compliance, and numerous KPIs can be used to assess their efficacy. These key performance indicators help companies demonstrate regulatory compliance, build confidence, and improve data management processes.

Data Compliance Requirements and Standards

Global Data Protection Laws

• GDPR – The General Data Protection Regulation (GDPR) is a comprehensive data privacy framework implemented by the European Union to protect its citizens’ personal information. It focuses on personally identifiable information (PII) and imposes strict compliance requirements on data suppliers. It requires companies in and outside of Europe to be honest about their data gathering processes, giving individuals more control over their PII.
• CCPA – The California Consumer Privacy Act (CCPA) is a historic data privacy law in the United States, similar to the GDPR. Like the GDPR, it requires corporations to be transparent about their data practices and gives individuals more control over their personal information. According to the CCPA, California residents can request information about the data collected on them by corporations, opt out of data sales, and request data deletion.
• HIPAA – The Health Insurance Portability and Accountability Act specifies recommendations for how healthcare organizations and businesses should handle patients’ personal health information (PHI) to ensure its confidentiality and security. Every entity that falls within the “covered entities” category as defined by HIPAA must adhere to HIPAA data security and compliance criteria, including not only healthcare providers and insurance plans but also businesses that have access to PHI, such as data transmission service providers, medical transcription service providers, software companies, insurance corporations, and others.
• PCI-DSS – The Payment Card Industry Data Security Standard (PCI-DSS) is a collection of regulatory recommendations for protecting credit card data. Unlike government-imposed laws, PCI-DSS is a set of contractual agreements enforced by an independent regulating body: the Payment Card Industry Security Standards Council. PCI-DSS applies to any company that accepts, stores, or transmits cardholder data. Even if a third-party service handles credit card transactions, the corporation is still responsible for PCI-DSS compliance and must take the appropriate steps to protect and keep cardholder data secure.
• SOX – The Sarbanes-Oxley Act (SOX) is a piece of law passed in reaction to corporate crises, with the goal of increasing corporate transparency and accountability. Under SOX, every publicly traded firm in the United States is required to follow strict financial reporting and governance criteria.

Regional Rules

• CDPA – The Virginia Consumer Data Protection Act (CDPA) gives Virginia consumers specific rights over personal data and requires companies covered by the law to follow standards for the data they gather, how it is managed and protected, and with whom it is shared.
• CPA – In June 2020, Colorado became the third state in the United States to approve a privacy law. The Colorado Privacy Act gives Colorado residents control over personal data and imposes requirements on data controllers and processors. It shares similarities with California’s CPRA, Virginia’s CDPA, and the EU’s GDPR. 
• UCPA – The Utah Consumer Privacy Act (UCPA) draws on the CDPA, CPA, and CPRA. The rule is applicable to both data controllers and processors that earn over $25 million in yearly revenue and either control or handle personal data for over 100,000 customers annually or generate over 50% of total revenue from personal data sales and serve 25,000 or more users.

Industry Certifications

• ISO 27001 – ISO/IEC 27001 is an information security standard that outlines the requirements for developing, implementing, maintaining, and continuously improving an information security management system (ISMS). 
• NIST – NIST is the National Institute of Standards and Technology of the United States Department of Commerce. The NIST Cybersecurity Framework allows businesses of all sizes to better identify, manage, and decrease cybersecurity risk while also protecting their networks and data.

Data Compliance Use Cases by Industry

Finance

Common use cases in the financial services industry include:

• Complying with data protection regulations and gaining consent for data use
• Putting in place procedures to detect and mitigate fraudulent financial transactions
• Creating methods for managing financial risks and adhering to industry laws
• Verifying consumer IDs and identifying questionable transactions
• Credit risk assessment and loan portfolio management are the processes of evaluating creditworthiness and efficiently managing loan portfolios
• Tracking data origins while guaranteeing correctness and integrity
• Using data to do financial analysis, forecasting, and reporting

Healthcare

Healthcare organizations usually face the following compliance use cases:

• Electronic Health Record (EHR) administration and interoperability: Ensuring a smooth flow of patient health information across healthcare providers
• Patient consent management and data privacy: Developing systems for obtaining and managing patient consent for data sharing
• Medical coding and billing accuracy: Ensures that healthcare services are coded and billed correctly
• Clinical trial data governance entails maintaining data quality and security in clinical research trials
• Healthcare Analytics for Population Health Management: Analyzing data to detect health trends and enhance public health outcomes.

E-Commerce

E-commerce and retail companies may encounter the following compliance use cases:

• Consumer data analytics for personalized marketing entails analyzing consumer information in order to provide tailored marketing and experiences
• Inventory management and optimization involve using data to optimize inventory levels and reduce stockouts
• Improving supply chain visibility and monitoring vendor performance using data
• Price optimization and dynamic pricing techniques involve using data to optimize pricing strategies and maximize income
• Creating customer loyalty programs using data-driven insights.
• Product data governance and catalog management involve ensuring the accuracy and integrity of catalogs’ product data
• Taking steps to detect and prevent fraud in e-commerce transactions

Government

Here are the most common data compliance use cases for government organizations:

• Keeping citizen data secure and preserving privacy in government systems
• Making government data more accessible and shareable for the benefit of the public
• Data-driven policy making and decision support, using data to influence evidence-based policy decisions and enhance governance
• Managing and analyzing geospatial data for urban planning and development initiatives
• Providing transparency and accountability in government procurement operations
• Managing voter registration data and ensuring its accuracy during electoral processes

Technology and Cloud Services

Key use cases for data compliance in technology and cloud services include: 

• Setting strong access restrictions 
• Encrypting data at rest and in transit
• Conducting audits and assessments to assure continuing compliance
• Implementing controls and processes to comply with certain regulatory requirements, such as data residency, data subject rights, and breach notification protocols
• Implementing robust IAM techniques, including role-based access control (RBAC), multi-factor authentication (MFA), and least privilege access, to reduce the risk of illegal access

Steps to Achieve Data Compliance

Identifying Sensitive Data

A comprehensive approach is key to achieving data compliance, especially when dealing with sensitive data. It includes data identification, classification, and security. The idea is to better understand the data landscape, implement proper security measures, and develop strong data governance policies.

Building Compliance Processes

Building efficient data compliance processes requires a multidimensional strategy that includes risk assessment, policy formulation, security implementation, and continuous monitoring. It requires a solid foundation of data governance, personnel training, and a dedication to continual development.

To set up data governance frameworks, organizations must understand the data protection rules and regulations that relate to their industry, region, and data types (for example, GDPR, HIPAA, and CCPA). This is when they can continue to create clear and explicit policies outlining how data will be gathered, used, kept, and safeguarded throughout its lifecycle.

At this point, teams may also implement access controls to restrict data access to authorized workers, such as role-based access control (RBAC), and create data classification systems to categorize data based on sensitivity and criticality, ensuring relevant security measures are implemented.

Applying Security Measures

It’s essential that organizations encrypt sensitive data during transit and at rest to prevent unauthorized access. Secure data storage is another important issue. Teams should store data securely in compliant facilities with proper access restrictions and monitoring.

Auditing and Monitoring

Continuously monitor data processing methods to verify compliance with policies and requirements. And make sure to carry out regular audits and reviews to evaluate compliance measures and identify opportunities for improvement. These entail routinely evaluating systems, papers, and activities to guarantee compliance with the law and ethical principles.

Keep up with changes in data protection laws and regulations, and update policies and processes accordingly. Promote a culture of data ethics to ensure responsible and ethical data use, beyond regulatory compliance.

Benefits of Data Compliance

Data compliance comes with many benefits, as it:

• Reduces Risk of Regulatory Penalties – Regulations such as GDPR and CCPA have significant fines for non-compliance. Data compliance reduces the likelihood of experiencing such financial obligations and legal challenges.
• Builds Trust with Users and Partners – Data breaches and mismanagement of sensitive information can harm a company’s reputation. Organizations that follow data compliance requirements can demonstrate their commitment to preserving customer data while also generating trust and loyalty.
• Enhances Organizational Resilience to Data Breaches – Data compliance involves installing strong security measures such as encryption, access controls, and retention policies. These safeguards protect sensitive data from illegal access, theft, or loss, thereby lowering the risk of data breaches.
• Facilitates Scalable, Policy-Driven Data Practices – Data compliance may require established data governance and workflows. Standardization can lead to more efficient data management, analysis, and decision-making processes.

Common Data Compliance Challenges

Challenge	Description
Scaling Compliance with Data Growth	Scaling compliance with data expansion calls for a proactive approach. To efficiently handle the rising amount, velocity, and variety of data, teams should use cloud-based solutions and platforms with autonomous scaling capabilities to accommodate increasing data volumes and user needs without requiring manual intervention. Implementing automation for processes like data discovery, access control, and audit logging works well for streamlining compliance efforts and reducing manual errors.
Managing Multi-Cloud and Hybrid Environments	Managing data compliance in multi-cloud and hybrid settings means teams need a comprehensive strategy to maintain consistent security and regulatory compliance across all platforms. This includes establishing strong governance, implementing strong security measures, and utilizing automation for constant monitoring and compliance checks.
Addressing Constantly Evolving Data Protection Laws	Maintaining data compliance in the face of ever-changing legislation is challenging. Organizations need to do their best to stay up to date on new rules, implement strong data governance processes, and constantly change compliance tactics to reflect changes in the legal landscape.
Enforcing Governance Across Teams	Without cultural buy-in, even the most comprehensive data governance approach will be ineffective. When team members perceive governance as restrictive or irrelevant, they may avoid policies, postpone implementation, or revert to legacy processes that undermine data integrity and compliance. Governance should be framed as an enabler rather than a limitation to drive adoption. It’s in your interest to show how it enhances analytics, increases access to reliable data, and safeguards sensitive information.

Data Compliance Best Practices

Using Data Versioning

Data versioning allows teams to systematically control and record changes made to datasets. It’s critical for compliance, particularly in highly regulated industries, because it allows companies to demonstrate data lineage, ensure reproducibility, and meet regulatory data storage and access requirements.

Key features of data versioning in terms of data compliance are:

Tracking data changes – Data versioning keeps track of every alteration, addition, or deletion made to a dataset, as well as who made the change and when. Maintaining a history of data versions allows data scientists to replicate earlier experiments and models, which is crucial for audits and compliance.

Audit trail – Data versioning provides an audit trail to identify the origin and evolution of data, which is crucial for showing compliance and detecting fraud.

Data lineage – It demonstrates the transformation and use of data throughout time, which is crucial for understanding the impact of changes.

Easy collaboration – Versioning allows several users to collaborate on the same dataset without disputes, guaranteeing they are using the most recent version.

Automating Policy Enforcement

Automation can significantly enhance data compliance while requiring minimal administrative effort. The process involves automating operations like data discovery, access control, and policy enforcement, which will reduce errors, manual effort, and the risk of noncompliance.

Automated policy enforcement entails automating data access, governance, and privacy regulations based on preset policies. It eliminates the need for human checks and approvals, resulting in uniform and accurate enforcement across all systems.

Implementing Access Reviews

Carrying out access reviews is another best practice for achieving and maintaining data compliance. It involves methodically analyzing and validating user access rights to ensure they’re consistent with organizational policies and regulations. This method helps to prevent unauthorized access to sensitive data, reduces the risk of data breaches, and proves adherence to compliance standards such as SOX, HIPAA, and GDPR.

Many teams use tools to automate the collection, analysis, and reporting of access data, thereby speeding the review process and enhancing accuracy. Depending on the data’s sensitivity and the organization’s risk profile, it’s beneficial to review and update access permissions on a regular basis.

Maintaining Data Lineage and Provenance

Maintaining a strong data lineage plays a key role in smooth data compliance. The practice provides data correctness, integrity, and traceability, all of which are necessary for regulatory compliance and data governance. Data lineage monitors how data moves and transforms through systems, whereas provenance examines the data’s origin and history, including any changes made.

Many regulations, such as GDPR, HIPAA, and SOX, require businesses to understand how data is used and handled. Data lineage creates a visual record of this, allowing organizations to demonstrate compliance by tracing data back to its original source and detecting any changes.

Data lineage helps in auditing by providing a full history of data flow, allowing for simpler verification of accuracy and identification of any errors or unauthorized changes.

Minimizing Manual Intervention in Compliance Checks

Manual processes are prone to errors, which can result in non-compliance and penalties. Assessments done by hand can be time-consuming and take resources from more vital activities. Scalability issues inevitably arise as companies develop and rules change, making manual operations more challenging to maintain.

Teams should use automation technology to reduce the amount of manual effort required during compliance assessments. Compliance automation software automates tasks such as data collecting, monitoring, and reporting, minimizing the need for human procedures. 

How lakeFS Supports Data Compliance

Organizations today face significant challenges in maintaining data compliance due to data volatility, manual enforcement limitations, lack of lineage and auditability, and siloed tooling. Traditional approaches like rule-based scanners, data catalogs with tagging policies, and scheduled audits are reactive rather than proactive, lacking native version control of data states and offering limited reproducibility. These manual processes don’t scale effectively, are prone to human error, and struggle to prevent policy infractions before they occur, often resulting in compliance gaps and inconsistent enforcement across teams.

lakeFS as a Modern Compliance Solution

lakeFS addresses these challenges by providing Git-like data version control capabilities that bring software engineering best practices to data management. Its key compliance features include immutable data history for precise audits and rollbacks, built-in lineage and reproducibility through commit IDs, isolated environments for safe testing, and automated compliance policy enforcement through hooks that enable Write-audit-publish workflows. With robust governance features like RBAC, audit logs, centralized identity management, metadata search for sensitive data detection, and branch protection rules, lakeFS transforms compliance from a reactive burden into a proactive, automated foundation that improves reliability, accountability, and trust in data operations.

Here’s a more detailed overview of how lakeFS helps teams achieve data compliance.

Complete data lineage and auditability

lakeFS provides a full data lineage by keeping immutable versions of datasets, allowing businesses to monitor and audit data changes at any moment. They can demonstrate compliance with regulatory demands and audits and understand data usage trends to ensure compliance in data processing.

lakeFS allows enterprises to immediately reproduce the previous states of their data, which is critical for regulatory reporting and investigations.

Fine-Grained Access Control and Identity Management

Protecting personally identifiable information (PII) is a fundamental component of many data regulations. lakeFS has comprehensive access control measures, such as:

• Fine-grained permissions that allow you to control access at the granular level
• Repository templates with predefined policies for enforcing security best practices
• Integration with identity management technologies (such as SSO and IAM) allows for centralized user administration

These capabilities enable enterprises to regulate who has access to sensitive data, ensuring that only authorized workers can read or alter PII.

Secure Audit Logging and Short-Lived Credentials

To comply with data governance standards, enterprises must often be able to monitor and document data processing actions. 

lakeFS provides:

• Comprehensive audit logs are used to track who has accessed or modified data
• Short-lived credentials are used to prevent unwanted long-term access
• Centralized authentication with Single Sign-On (SSO)

These features improve security by making it easier to identify illegal access and keep extensive compliance logs.

Zero-Copy Branches: Reducing Data Duplication

One major potential compliance issue is uncontrolled duplication of sensitive data, particularly in AI/ML research. Traditional workflows frequently require moving datasets to local disks or object stores, which raises the risk of data leakage or mismanagement.

lakeFS’s zero-copy branching minimizes the need for data duplication by allowing teams to:

• Make immediate, isolated copies of data without physically duplicating it
• Ensure data remains inside compliance boundaries, thereby decreasing exposure
• Create and test AI/ML models and pipelines securely, without making repeated, uncontrolled copies of critical data

This approach not only improves security but also strengthens the environment against data breaches and compliance infractions.

Right to be Forgotten and Controlled Data Deletion

According to the General Data Protection Regulation, users have the right to request the deletion of their personal data. However, ensuring appropriate deletion can be difficult in complicated data systems. 

lakeFS allows organizations to:

• Use branches and snapshots to safely erase data without disrupting production systems
• Implement organized data retention procedures to meet legal obligations
• Verify data deletions with full audit trails to ensure responsibility

The solution simplifies GDPR-compliant data management by imposing rigorous controls on data alterations and deletions.

Would you like to see a specific example? Read more about how lakeFS helps ensure data compliance.

Conclusion

The future of data compliance is defined by more complexity, stronger rules, and a growing emphasis on consumer rights. AI and automation are critical in helping teams navigate these problems, and there is an increasing need for global standardization and proactive, flexible responses. This is why the value of data versioning will only grow as data sets expand and companies develop more sophisticated data use cases.