Data Governance: How to structure processes and ensure security

The amount of data generated by companies has never been greater—or more strategic. With the exponential increase in available information and the acceleration of digitalization, data governance has become an essential pillar for ensuring security, efficiency, and regulatory compliance.

Structuring an effective data governance policy is crucial, for instance, to align a company's processes with the requirements set by the General Data Protection Law (LGPD). However, the benefits go beyond risk management: by investing in data quality, integrity, and usability, organizations can enhance operational efficiency, foster innovation, and turn data into strategic assets for decision-making.

Nonetheless, structuring effective governance goes beyond simple access control or leak prevention. It involves establishing policies, methodologies, and technologies that ensure the quality, integrity, and usability of information, allowing data to become a strategic asset for decision-making. Keep reading! In this article, we present best practices in data governance, combining preventive measures with the flexibility needed to keep pace with technological evolution.

What is data governance and why is it essential?

Data governance is the set of processes, policies, and technologies that ensure the quality, security, compliance, and usability of information within an organization. Today, data is among the most valuable assets for any business, requiring special attention to how it is used—not only for extracting valuable insights but also for protecting it from leaks and cyber breaches.

Given this context, data governance is directly linked to information security, as it defines protection guidelines against unauthorized access as well as response mechanisms for data loss and leaks. Without proper control, important information can be exposed to threats such as cyberattacks, fraud, and internal errors, compromising system integrity and causing financial and reputational damage.

In addition to security, governance is also tied to compliance and regulatory adherence. With legislation such as Brazil’s General Data Protection Law (LGPD), the National Cybersecurity Policy (PNCiber), and the European General Data Protection Regulation (GDPR), companies must ensure that customer and partner data is collected, stored, and processed transparently and securely. Non-compliance can result in heavy fines, legal sanctions, and loss of market credibility.

To meet these requirements, an effective governance model should establish:

• Access and control policies: clearly define which users can view, modify, or share specific data sets;
• Encryption and anonymization: strategies to protect sensitive information, reducing risk in the event of a leak;
• Continuous monitoring and audits: track data movement and conduct regular reviews to detect flaws and fix vulnerabilities;
• Data lifecycle management: establish rules for data retention, archiving, and secure disposal, according to regulatory requirements.
• Beyond protection and compliance, data governance plays a crucial role in operational efficiency and competitiveness. Organizations lacking well-defined processes often face errors, redundancies, and inconsistencies that hinder data analysis and decision-making.

What are the benefits of data governance?

With a structured governance model, it is possible to improve data quality, boost productivity, reduce costs, enhance business intelligence (BI), and cut operational expenses. All of this delivers tangible results.

Check out the main benefits of data governance!

Enhanced security and risk mitigation

Implementing strong governance policies reduces the chances of data leaks, unauthorized access, cyberattacks, and other security incidents. Mechanisms like encryption, access controls, regular audits, and continuous monitoring help companies protect sensitive data and avoid financial and reputational damage.

Regulatory compliance and penalty reduction

With regulations like LGPD and GDPR, non-compliance can lead to hefty fines, operational restrictions, and loss of trust. Data governance ensures all company practices comply with legal requirements, avoiding sanctions and ensuring transparency in data use.

Improved data quality and reliability

Duplicated, inconsistent, or outdated data can compromise analyses and lead to flawed strategic decisions. Governance improves organization, standardization, and validation of data, ensuring information is accurate, reliable, and accessible across all departments.

Operational efficiency and cost reduction

Lack of structured processes wastes time and resources due to rework, unnecessary storage, and poor information management. Governance streamlines workflows, automates data administration, and boosts team productivity, lowering operational costs.

Faster, strategic decision-making

With well-structured and accessible data, managers can conduct faster, more informed analyses, identifying market trends, customer behavior patterns, and growth opportunities. Data governance enhances BI and Data Analytics initiatives, increasing a company’s competitive edge.

Innovation and new business models

Companies that master data management can leverage technologies like AI and machine learning to generate strategic insights, develop new products and services, and improve customer experience. Governance enables efficient data integration across departments and business partners, fostering advanced digital ecosystems.

Greater transparency and trust with clients and partners

In a landscape where privacy and ethical data use are increasingly valued, governance strengthens trust with clients, investors, and stakeholders. By ensuring transparency in data handling, companies demonstrate responsibility and a commitment to data protection, reinforcing their market reputation.

What are the foundations (pillars) of data governance?

Data governance is supported by a set of essential principles that ensure quality, security, compliance, and data reliability. These pillars structure strategic data use, ensuring efficient and secure management aligned with current regulations. Discover the main foundations of data governance and their importance for companies.

1. Quality

The foundation of any data governance strategy is data quality. Inconsistent, incomplete, or inaccurate data can compromise analysis and lead to poor decisions. Governance should ensure data is:

• Accurate: free from errors and redundancies;
• Complete: containing all necessary information for analysis and operations;
• Consistent: no contradictions across different sources or systems;
• Up to date: regularly reviewed to reflect changing business contexts.

2. Security and privacy

Data protection is one of the main challenges companies face. With increasing threats like cyberattacks, leaks, and unauthorized access, it's essential to implement protection and cybersecurity mechanisms, such as:

• Access controls: define permissions so only authorized users access sensitive data;
• Encryption: use technologies to protect stored and transmitted data;
• Anonymization: hide sensitive information to reduce risk in case of leaks;
• Continuous monitoring: implement systems to detect and quickly respond to security incidents.

3. Compliance and regulation

Beyond security, data privacy must be ensured through compliance with laws like LGPD and GDPR, guaranteeing personal information is used transparently and with user consent.

Non-compliance can result in fines, lawsuits, and reputational damage. Clear policies on data collection, storage, and usage—as well as audit mechanisms—must be implemented to ensure ongoing compliance.

4. Responsibility

Data governance requires clarity in assigning responsibilities within the organization. Each team or professional must understand their role in managing data to avoid conflict and streamline processes. Key roles include:

• Chief Data Officer (CDO): responsible for data governance strategy;
• Data stewards: oversee the quality and integrity of data;
• IT and security teams: ensure data protection and monitoring;
• Employees and end-users: trained to handle data securely and ethically.

5. Ethics

In a world where data is a strategic asset, ethical use and privacy best practices are essential. Data governance must ensure companies:

• Use data transparently and respect user rights;
• Avoid discrimination and algorithmic bias in analytics or AI;
• Ensure privacy rights and allow users to control their data;
• Refrain from unethical data monetization, maintaining trust with clients and partners.

6. Trust

Trust in data depends on transparency regarding its origin, changes, and usage. Governance must ensure organizations maintain full traceability, ensuring:

• Data origins are documented;
• Data is monitored with all changes tracked;
• Records are protected against tampering, ensuring integrity and reliability.

What is a data governance policy and how to develop one?

A data governance policy is a set of guidelines, rules, and processes for managing data within an organization. It sets standards for data collection, storage, usage, sharing, and protection, ensuring secure, ethical, and regulatory-compliant data management. Here are the essential steps for developing and implementing an effective policy.

1. Define policy objectives and scope

The first step is to outline the policy’s goals and scope. Consider these key questions:

• What is the purpose of data governance? (e.g., regulatory compliance, improving data quality, reducing risks);
• What types of data are covered? (e.g., customer, vendor, employee, financial, operational, strategic data);
• Which departments are involved? (e.g., IT, Legal, Compliance, Marketing, HR, Finance).

2. Establish security and access control guidelines

Data security is a core goal of governance. The policy should define:

• Who can access data—permissions and restrictions based on roles and hierarchy;
• How data is protected—encryption, anonymization, and regular backups;
• Protocols for incidents—procedures for leaks, cyberattacks, or unauthorized access.
• Authentication tools, continuous monitoring, and audits should also be included to reinforce data protection.

3. Ensure regulatory compliance and good governance practices

To avoid penalties and enhance transparency, the policy must align with current regulations, including:

• Compliance with LGPD, GDPR, or applicable laws;
• User consent for personal data collection and usage;
• Processes for data subject requests (deletion, correction, portability);
• Compliance reports for internal and external audits.

4. Define data quality and integrity processes

The policy should include guidelines to ensure data is accurate, complete, and reliable. This includes:

• Standardizing data formats and naming conventions;
• Ongoing validation and updates to keep information current;
• Removing duplicates and inconsistencies for efficient storage and analysis;
• Establishing data lifecycle rules, including retention, archiving, and secure disposal.

5. Assign roles and responsibilities

Effective governance requires clearly defined responsibilities. Assigning specific roles helps avoid conflicts and improves policy implementation.

6. Implement monitoring and audit processes

To ensure the policy is followed, continuous monitoring and audit mechanisms must be established, such as:

• Tracking performance metrics (e.g., data access volume, update frequency, compliance levels);
• Regular audits to detect flaws and risks;
• Security testing and attack simulations to assess vulnerabilities;
• Action plans for continuous improvement.

7. Create a communication and training plan

Governance only works if employees are informed and engaged. Key actions include:

Creating an accessible policy manual detailing processes and responsibilities; Offering regular training, especially for teams handling sensitive data; Promoting a culture of information security and compliance best practices.

What are the main challenges of data governance in companies?

Data governance faces challenges ranging from managing complex data environments to regulatory and security issues:

Managing large volumes of data: information is often scattered and unstructured, requiring effective organization and analysis processes; Lack of structured security processes: leaks and unauthorized access are constant threats, demanding encryption, access control, and monitoring; Regulatory compliance: adapting to stricter data protection laws is essential; Increased operational risks: poor decisions based on inconsistent data can result in financial and strategic losses; System and data source integration: lack of platform standardization hampers data unification and reliability.

What is the relationship between data governance and metadata management?

Metadata management is a core pillar of data governance. It enables the organization, tracking, and reliability of information within a company.

Metadata is structured information that describes characteristics of a data set. It acts like a label identifying the source, type, format, and context. Examples include:

• Date and time of creation or modification;
• Author or system that generated the data;
• Content format and structure (e.g., CSV, JSON, XML);
• Storage and archiving rules.
• Data governance and metadata management work together to ensure data quality, security, and traceability. While governance defines the guidelines and processes, metadata enables their structured application.

In an increasingly digital and regulated world, data governance is essential for reducing risks, improving data quality, and enhancing decision-making.

Looking to implement efficient data governance aligned with your business needs? The act digital team is ready to help you structure processes, information security, and regulatory compliance. Get in touch with us!