Data governance and data security: what’s the difference?

Data governance and data security are two crucial aspects of data management. Accelerated digitization in the post-COVID era has led to an explosion of data for most organizations. More and more data, in ever-increasing volume and complexity, flows from smart sensors, user-facing applications, social media, internal applications, software logs, monitoring tools and more. Large enterprises can have hundreds or even thousands of data sources. Processing the data for analysis is essential, but more importantly, organizations must manage their data to prevent misuse and meet all regulatory and compliance requirements. Data governance and security both play a key role in this space.

This article explores the difference between data governance and security, including the components and why both are essential.

Understanding data governance and data securityUnderstanding data governance and data security

Data governance is the systematic management of an organization’s overall data strategy. It defines and enforces policies and procedures for different aspects of data management, from collection to cleaning, classification, integration, storage and use. It establishes data quality standards that everyone in the organization is expected to follow. The goal is to ensure all data management technologies and processes always comply with industry standards, laws and regulations.

In contrast, data security focuses only on data protection. It includes the tools and processes that prevent unauthorized access. It implements verification (like password checks) so that only verified users can access data. Different access levels are defined so that some users can only read data while others can read and update the dataset.

Data governance tools ensure that you can:

• Define clear roles and responsibilities for different aspects of data management.
• Identify and correct incomplete, missing, or poor-quality data early in the data management pipeline.
• Create and manage metadata (data about data) for efficient data organization.
• Perform regular audits to ensure compliance with up-to-date data regulations and standards.

In case of data security, it’s different:

• Maintain data confidentiality to protect the privacy of the real-world subjects to which the data relates.
• Maintain data integrity so malicious or corrupt elements do not damage the entire database system.
• Maintain data availability so authorized users can readily access the data whenever and wherever needed.

What is the difference between data governance and data security?What is the difference between data governance and data security?

Data security is one aspect of data governance. It prevents data breaches and protects data from theft and loss. Data governance refers to a much broader scope beyond security.

You can think of your local government as an example. Your local police is one branch of the government, with the main job being protecting citizens from criminals. But the government also has other departments—for example, road maintenance, water supply, parks, housing and so on.

Similarly, data governance is responsible for all aspects of the data processing pipeline, much beyond securing access. It looks after everything from data quality to data availability and legal compliance.

Below, we give a summary table of the differences between data governance and security.

Cookie Name	Data governance	Data security
Purpose	Ensure the responsible and efficient use of data within an organization.	Protect data from corruption, loss, or theft.
Scope	It has a much broader scope beyond data security. It is essential for overall data management, including usability, availability, integrity and so on.	Has a smaller scope — data protection from unscrupulous third parties and unforeseen disasters.
Why it is important	So organizations can manage, utilize and improve data to enhance business intelligence, streamline operations and ensure compliance.	So organizations can protect their data in transit and at rest. (Over a network and in storage).
Implementation aspects	Stewardship, policies and procedures, privacy and compliance, data quality, security and metadata management.	Access controls, backup & recovery, network security and data encryption.

Defining data governance strategyDefining data governance strategy

Data governance is the collection of various policies, practices and metrics for the efficient use of data in an organization. It is an ongoing and iterative process that requires coordination between various organization departments. Since data is used in all operations and services, modern data governance strategy impacts every tactical and strategic level in the enterprise.

Data governance includes the following primary components.

Data policiesData policies

They are the high-level rules that create a data-driven culture within the organization. Typically, a data governance committee develops the policies and procedures. It may contain a legal team, business analysts, data scientists and engineers. The policies align with business goals to support the organization’s long-term strategies. The goal of this team — and of the data governance manager leading it — is to support data-driven decision-making in all operations.

Data qualityData quality

You cannot make data-driven decisions if the data is not trustworthy. High-quality data under a data governance framework has six characteristics. it is:

1. Accurate and gives true information about the subject it relates to.
2. Complete and gives all the details necessary to make it usable.
3. Reliable, which means you know it has come from a trusted source.
4. Relevant to the use case or application it is being considered for.
5. Timely, or the latest information.
6. Consistent, so data about the same subject in different datasets matches.

Data governance manager or someone in their team defines data quality standards, metrics and thresholds relevant to business needs. Data quality is the primary driver behind the data governance policies and procedures.

Data stewardshipData stewardship

Data stewardship enforces data governance policies across the organization. It is about creating a big-picture view so that organizations can turn their data into a competitive advantage. The term steward means supervisor. So, data stewardship is about supervising the entire data governance process. It is about coordinating data management between different departments to reduce silos and wasted efforts.

For example, let’s say two different departments are collecting customer data. Data stewardship means ensuring both departments follow the same rules around data collection (e.g., both departments enforce that the customer’s first and last name are collected or that the phone number is not left blank) and store the data in the same database.

Metadata managementMetadata management

Metadata is data about data. It describes when and where a dataset was collected, what it contains and how it is being used. You get a clear trail of data origin and any transformations over time. You can also find “tags”—relevant keywords that describe and categorize the data set.

Data governance strategy implies that metadata management facilitates:

• Better search and discovery of data within large datasets
• Systematic tracking and handling of data relationships and structures.
• Auditing for compliance reports

Data privacy and complianceData privacy and compliance

Data privacy relates to any personal data, such as contact details, finance, health, behavior and so on, of individuals. There are many legal and ethical considerations around how such sensitive data is managed and shared. For example, consider an application that displays customer profiles. The organization has to take steps to ensure that even the internal developer team cannot access the confidential information.

Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) regulate every aspect of private data collection, storage and sharing. Organizations that fail to comply with standards face serious legal and financial repercussions.

Use cases for data governanceUse cases for data governance

Examples of data governance efforts include establishing data quality standards, implementing policies for data access and creating procedures for data retention and deletion. Defining roles and responsibilities for data management and conducting regular audits to check for compliance with internal policies and external regulations are also data governance examples.

Defining data securityDefining data security

Data security prevents unauthorized access, disclosure, modification, or destruction of data across its lifecycle—from creation and storage to transmission and beyond. It has three main goals:

1. Confidentiality—Ensuring that sensitive information is accessible only to those authorized to view it.
2. Integrity—Protecting data from being altered or tampered with by unauthorized individuals.
3. Availability—Ensuring that data is accessible and usable upon demand by authorized users.

Data security covers the following aspects to meet the modern security challenges.

Access controlsAccess controls

Access controls determine who can access data and what permissions they have. For example, a manager may have read and delete permissions, while a third party may only have read permissions. Defining access levels for thousands of employees and contractors becomes challenging. Luckily, security technologies allow administrators to define access control for large groups in three ways.

1. Role-based access control (RBAC) allows you to pre-define permission groups based on a user’s role within an organization. You can define roles like manager, developer, analyst and grant permissions accordingly.
2. Attribute-based access control (ABAC) defines access permissions based on policies that combine multiple attributes of users, resources and the environment. This model provides fine-grained access control and can adapt to complex scenarios involving many different factors. For example, you could define access based on attributes such as the employee’s department, the sensitivity of the data, or the time of day.
3. Policy-based access control (PBAC) uses dynamic policies to govern access rights. For example, in a healthcare system, access to patient records might be granted to the treating physician but not to all doctors.

Administrators can also monitor and audit access logs to identify any unusual patterns or unauthorized access attempts and take immediate action if discrepancies arise.

Data encryptionData encryption

Data security governance depends heavily on encryption for protecting sensitive data. Cryptographic techniques are used to scramble the data. Only users with the appropriate “key” or password can unlock and decrypt the data. Scrambling techniques use complex mathematical functions, so even a supercomputer will take years if used to hack the system! Encryption protects data in storage and when data packets travel over the network. Multiple layers of encryption may be used depending on your network and database technology.

Data backup and recoveryData backup and recovery

Despite best efforts, situations can arise that corrupt your data. For example, ransomware may corrupt your files, a server may crash, or an employee may accidently delete something. That’s where data backups come in.

A data backup is just an up-to-date copy of your data stored at a different location from the main data. The main challenge is syncing up both copies, which can get quite expensive depending on the strategy used. Some do incremental backups and only copy changes made over time. Others do full backups and copy the entire database periodically. The strategy you choose depends on your database size and criticality.

The recovery process involves restoring data from the backup to the main system. Speed is essential to avoid downtime. Some systems can switch between main and backup so quickly that users have no idea something has changed.

Network securityNetwork security

Network security includes technologies like firewalls, intrusion detection systems (IDS) and virtual private networks (VPNs) that protect your network itself. It is not just about a single database or server but your entire IT infrastructure. All incoming data packets are screened for suspicious activity and all outgoing traffic is encrypted. Automated blocking, isolation and alerts are used to take prompt action in case of an attack. Of course, network security has to be balanced with performance. Firewalls and IDS systems can create network bottlenecks that slow down your apps and websites.

Use cases for data securityUse cases for data security

Data security is now more critical than ever before. Modern IT infrastructure, especially the cloud, has created many new attack vectors (entry points) in the organization’s network. Traditionally, developers had to approach the IT team if they wanted to start a new project. The IT team would deploy servers and storage in on-prem data centers that were more secure. Now, developers can self-provision infrastructure in the cloud. Misconfigurations often result in exposed APIs and unsecured data. The IT team lacks end-to-end visibility across the multi-cloud environment that most organizations prefer. Besides, cybercriminals have become more sophisticated and strategic. This requires a dynamically adapting security system.

The relationship between data governance and data securityThe relationship between data governance and data security

Data governance and security are strongly interconnected. Security without governance results in poor-quality data, duplicate efforts and a lack of structure. Even with the best security infrastructure, you might miss out on an unrecorded data set that results in a data breach. Similarly, governance without security is not feasible for long-term success.

Given the number of internet threats, most organizations typically take a security-first approach. However, implementing security controls is just the start. Things can quickly spiral out of control if at least some data governance policies and procedures are not defined and enforced alongside security.

Organizations should take a governance-first approach. Data governance framework lays the foundation for best practices in data management across the organization. When done right, it automatically includes security and gives a structured framework for getting security right in one go.

Any organization considering serious digital transformation efforts must implement both data governance and security for success.

ConclusionConclusion

Effective data governance ensures the responsible and efficient use of data within an organization. Data security protects data from corruption, loss, or theft. In general, data governance has a much broader scope beyond data security. Alongside data security controls like encryption and authentication, governance includes data policies, stewardship and metadata management for improved data quality. Your organization can meet data privacy requirements and comply with regulations in a systematic and failsafe manner. Both data governance and security are a must for digital success.