Data Governance vs Data Security: 5 Key Differences You Must Know
Harsh Sahu
CTO
April 2024 | 10 mins
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There’s no question that quality data is crucial for business leaders to make confident and reliable business decisions. For business intelligence reporting and analytics to be trustworthy, the underlying data must be accurate, consistent, and complete. Data governance is a key factor in how organizations achieve the quality of data that leads to that trust.
To keep the quality of data intact, securing the most sensitive assets of an organization serves the right approach to parallel data governance and security. Yet, organizations fail to leverage the full potential of data governance and security.
According to BCG, 60% of organizations rated their data governance capabilities at various levels of underdevelopment.
In this blog, we’ll discuss what entails data governance and data security, their key components, major use cases, the key differences between data governance and data security, and what are the risks of not opting data governance and security.
Data governance is a comprehensive framework of policies, procedures, and practices established to manage, utilize, and protect an organization's data assets.
It involves the management of people, processes, and technology to ensure that data is accurate, accessible, consistent, and secure.
Data governance framework ensures that data across the organization is standardized and correctly used to support business objectives and maintain compliance with regulations and standards.
Data quality refers to the accuracy, completeness, reliability, and relevance of data throughout its lifecycle.
High-quality data is crucial for making informed decisions, optimizing operations, and ensuring regulatory compliance. Poor data quality can lead to erroneous decisions, financial loss, and damaged reputation.
In the financial sector, accurate data is essential for risk assessment and credit scoring. If data quality is compromised (e.g., incorrect customer information), it may result in improper risk assessments, leading to bad credit decisions and potential losses.
Data stewardship involves the oversight and management of an organization's data assets to ensure their proper usage and maintenance.
Data stewards ensure that data is managed according to the organization’s policies and standards and that it remains a reliable resource for the organization. They play a key role in resolving data issues and facilitating collaboration between IT and business units.
In healthcare, a data steward might oversee patient data to ensure it is accurate and only accessible to authorized personnel, thereby maintaining patient privacy and compliance with healthcare regulations like HIPAA.
Data policies and procedures are the formal guidelines and practices that govern how data is to be handled, processed, and secured within an organization.
These policies establish standards for data usage, security, and management, helping to prevent data breaches, ensure legal compliance, and support data-driven decision-making.
A retail company might implement data policies that dictate how customer data is collected, stored, and shared. This would include procedures for obtaining customer consent and guidelines for third-party data sharing to comply with consumer protection laws.
Metadata management involves the handling of data about data, which includes creating, storing, and controlling access to metadata.
Metadata helps organizations understand the context and structure of their data, which is critical for data lineage, quality management, and regulatory compliance. It also facilitates easier data discovery and retrieval.
In an insurance company, metadata management is essential for managing policyholder information, claims data, and risk assessments. For instance, each insurance claim file might include metadata such as the claim number, date of the claim, type of claim (e.g., auto, home, life), status of the claim (open, under review, closed), the policy number it relates to, and the assigned claims adjuster.
Data privacy and compliance refer to the practices and policies in place to ensure that data is used in accordance with legal and regulatory requirements protecting individual privacy rights.
This component is vital for maintaining consumer trust and avoiding legal penalties associated with data breaches or misuse. It helps organizations safeguard sensitive information and adhere to relevant data protection regulations.
An organization must adhere to various data protection laws like GDPR in Europe or CCPA in California, requiring policies for data retention, deletion, and processing practices that respect the privacy rights of individuals.
Data security refers to the protective measures, processes, and policies implemented to safeguard digital information from unauthorized access, corruption, or theft throughout its entire lifecycle.
It encompasses techniques and technologies aimed at securing data both at rest and in transit, ensuring that it remains intact, private, and accessible only to authorized users.
Additionally, data security is an ongoing process that involves continual assessment and adaptation to new threats and vulnerabilities, which includes regular updates to security practices and infrastructure. Data Security protects integrity and confidentiality of data but also ensures compliance with regulatory laws governing data protection.
Access control in cybersecurity is a security technique to control access to sensitive or restricted information by regulating who or what can users view or use resources in a computing environment.
It is a fundamental concept in security that minimizes risk to the system or the data it contains. With access control mechanism, only authorized users are allowed to access specific resources such as applications, databases, and networks.
Access controls models are critical for protecting the confidentiality, integrity, and availability of data. They help prevent unauthorized access to sensitive information and ensure that only authorized personnel have access to specific resources.
Attribute based access control is the preffered type of access control model for organization handling dynamic data at scale.
In a hospital, access controls ensure that only designated healthcare providers can access patient records, while administrative staff may have restricted access limited to non-medical data. This helps in maintaining patient confidentiality and compliance with healthcare regulations like HIPAA.
Data encryption is the process of converting data into a code to prevent unauthorized access. It uses algorithms to transform readable data into an unreadable format or rather called synthetic data, unless decrypted with a secret key or password.
Encryption protects sensitive data’s confidentiality and integrity, particularly important when transmitting data across insecure networks or storing it on potentially accessible systems.
A financial institution uses data encryption to secure client financial information during transactions. For instance, when a customer uses online banking to transfer money, encryption ensures that the transaction details are secure and cannot be intercepted or altered by cybercriminals.
Data backup involves making copies of data so that these additional copies can be used to restore the original after a data loss event. Data recovery refers to the process of restoring lost or corrupted data from backup storage.
Regular backups and efficient recovery processes are vital for maintaining data availability and continuity of operations after data loss incidents, such as system failures, human error, or cyber-attacks.
An fintech company regularly backs up its customer data and transaction histories. When their primary data center experienced a hardware failure, the company was able to quickly restore services using data from a recent backup, minimizing downtime and preventing data loss.
Network security involves policies and practices designed to protect the usability, reliability, integrity, and safety of a network and data using both hardware and software technologies.
It guards against unauthorized intrusion, cyber threats, and vulnerabilities in the network that could lead to data theft or sabotage.
A technology company uses firewalls, intrusion detection systems (IDS), and intrusion prevention systems (IPS) to monitor and protect its network from unauthorized access and threats. For example, the firewall helps prevent unauthorized Internet users from accessing private networks connected to the internet, especially intranets.
Security compliance involves adhering to a set of standards, laws, and regulations designed to protect information technology and data security.
Compliance ensures that organizations follow best practices and legal requirements, thereby protecting the organization's data and its stakeholders' interests.
A organization adheres to the General Data Protection Regulation (GDPR) for processing personal data of individuals within the EU. This involves implementing appropriate security measures to protect data, conducting regular audits, and ensuring that all data handling practices are transparent and compliant with GDPR.
The integration of data security within a data governance framework is not just complementary but essential. Security measures provide the necessary foundation on which governance can be effectively built and maintained.
Without robust data security, the goals of data governance—ensuring data quality, compliance, and effective data management—cannot be fully realized. Thus, these practices not only overlap but also reinforce each other, creating a comprehensive approach to managing and securing an organization's data assets.
Here’s how Data Security and Data Governance overlaps:
Data Security Practices: Implementing strict access controls and authentication procedures ensures that only authorized personnel can access sensitive data. Encryption protects the data's integrity and confidentiality during storage and transmission.
Support for Data Governance: By securing data against unauthorized access and ensuring its integrity, data security measures build trust in the data’s reliability and accuracy. This trust is crucial for data governance as it ensures that the data used in decision-making processes is accurate and has not been tampered with.
Data Security Practices: Data security is fundamental in complying with various data protection regulations such as GDPR, HIPAA, or PCI-DSS, which require specific security measures like data encryption, regular audits, and breach notifications.
Support for Data Governance: Data governance frameworks often include compliance as a major component. Effective data security practices ensure that data governance efforts meet all legal and regulatory requirements, thus avoiding legal penalties and reinforcing the organization's reputation.
Data Security Practices: Risk assessments, intrusion detection systems, and proactive threat monitoring are vital data security tools that identify and mitigate potential threats to data security.
Support for Data Governance: Part of data governance involves understanding and mitigating risks related to data handling and storage. Security measures feed into this by providing the necessary insights and tools to address these risks, ensuring data remains safe and governance policies remain relevant and robust.
Data Security Practices: Security protocols include defining who can access data at different stages of its lifecycle, implementing data retention policies, and securely deleting data that is no longer necessary.
Support for Data Governance: Data lifecycle management is a key aspect of data governance. Security practices ensure that data is protected from creation to deletion, aligning with governance policies that dictate how long, where, and in what form different types of data should be maintained.
Data Security Practices: Measures like data encryption and regular integrity checks prevent unauthorized data modifications, which could compromise data quality.
Support for Data Governance: High-quality data is essential for effective data governance. Security measures ensure that data is not only protected from external threats but also maintained in a state that is consistent and reliable for business operations.
Both data governance and data security play important roles in the operational integrity and strategic success of organizations. However, lapses in either of these areas can lead to significant risks and detrimental outcomes.
Without proper data governance, data quality and accuracy can be compromised, leading to flawed analytics and poor decision-making. A Gartner study estimated that poor data quality costs organizations an average of $12.9 million annually.
Data governance includes ensuring compliance with relevant data protection regulations (such as GDPR, HIPAA). Inadequate governance may lead to failures in meeting these legal standards.
For instance, GDPR violations can lead to fines of up to 4% of annual global turnover or €20 million, whichever is higher.
Poor governance often means that data is not organized or maintained effectively, making it hard to access and use. This lack of accessibility can delay critical business processes, reducing overall operational efficiency and effectiveness.
Without rigorous governance, data may be duplicated, outdated, or incorrect, leading to issues with its integrity.
Data that is not accurate can mislead stakeholders and management, leading to poor outcomes in various business operations.
Poor management of data can lead to privacy breaches of customer data. A study by Ponemon Institute found that companies that experienced a significant loss of data saw customer churn rates increase by as much as 7%.
Inadequate security measures can make data susceptible to unauthorized access and breaches. According to IBM's Cost of a Data Breach Report 2023, the average total cost of a data breach is $4.45 million.
Security incidents can result in direct financial losses from theft, as well as substantial costs associated with breach mitigation and recovery. The financial implications can also extend to penalties and fines for failing to protect data adequately, further exacerbating the financial strain.
A data security failure can damage an organization's reputation significantly. Loss of consumer confidence can lead to a decrease in business, and the tarnished brand may take years to recover, if at all.
Cyberattacks or security failures can lead to significant operational disruptions. This can include everything from the shutdown of critical business services to the expenditure of resources on crisis response instead of business development and growth.
Failures in data security can lead to violations of privacy laws and regulations. This can result in legal disputes, fines, and required changes to business practices, all of which require time and resources to address.
The dynamics of data governance and security continue to change rapidly, driven by technological innovations, increasing regulatory requirements, and shifting business focus.
Here are few of the trends in Data Governance and Security to look out for:
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into data governance tools is revolutionizing how businesses predict risks and automate complex compliance processes. These technologies facilitate more efficient anomaly detection, enabling real-time responses to potential data breaches or non-compliance issues. For instance, AI algorithms can automatically classify and tag sensitive information, ensuring that data handling adheres to policy requirements without manual oversight.
Quantum computing presents both an opportunity and a challenge in the field of data security. While it promises significant advancements in processing power, it also poses a threat to traditional encryption methods. Organizations are beginning to prepare for a post-quantum cryptography era by researching quantum-resistant algorithms to safeguard sensitive data against future quantum attacks.
As data breaches become more frequent and severe, governments worldwide are tightening data protection regulations. Businesses must keep abreast of changes to laws such as the GDPR in Europe, CCPA in California, and new frameworks emerging in countries like Brazil and India, which continually update their regulations to reflect the changing digital landscape.
Industries such as finance, healthcare, and technology face additional layers of data governance and security challenges due to sector-specific regulations. For example, the finance sector is seeing increased scrutiny over data handling practices, requiring enhanced measures for data privacy and security to comply with regulations like PSD2 in Europe and GLBA in the United States.
Privacy Enhancing Technologies are becoming essential tools in the data protection toolkit, allowing for the processing of personal data without compromising individual privacy. Technologies such as dynamic data masking and differential privacy enable data analysis in a way that the underlying data remains obscured, thus maintaining privacy while still gaining valuable insights.
Blockchain technology is increasingly being recognized for its ability to provide secure, transparent, and immutable records of transactions. In the context of data governance, blockchain can be used to create verifiable audit trails, enhance data integrity, and prevent fraud by providing a decentralized and tamper-evident data structure.
Decentralized identity systems are gaining traction as a way to manage identities without relying on a central authority. These systems enhance privacy and security by allowing individuals to control their personal data and share it selectively, reducing the risk of data breaches associated with centralized identity repositories.
The concept of a cybersecurity mesh is to create a flexible, adaptable security architecture where policies and controls are applied closer to the assets they are meant to protect. This approach allows organizations to more effectively manage security in a distributed environment, ensuring that every access point and data asset is properly secured, irrespective of its location.
Predictive governance uses data analytics to foresee potential compliance issues and governance challenges before they become problematic. By analyzing trends and patterns, organizations can proactively adjust their governance strategies to mitigate risks, ensuring continuous compliance and operational efficiency.
According to IBM, 82% of breaches in 2023 involved data stored in the cloud. This clearly indicates how the loose data governance framework of many organizations.
Without proper governance and security, critical business assets are under constant threat of being sold in the dark web. How to solve this dillema?
Simple. Opt for a data security platform that lets you govern every user in the organization from a single dashboard, without the worry of manual misconfiguration identification.
OptIQ data security platform allows organizations to get a clear visibility into how a user uses data, when it uses and for what purpose. Governing users and making them accountable has never been more easier.
You even get granular insights for fast decision-making by quantifying the level of threat exposures, both for internal and external attacks. Request a data security platform demo to understand how OptIQ can help you govern your data users and put a stop to unnecessary roadblocks to your maximum data utility.
The main goal of data governance is to ensure that data is accurate, consistent, secure, and usable, and the primary goal of data security, on the other hand, is to protect data from unauthorized access, breaches, and other forms of inappropriate or illegal use.
While an organization can implement data security measures without a formal data governance framework, doing so may lead to inconsistencies and potential vulnerabilities. Data security is most effective when integrated into a broader data governance strategy.
In real-world applications, data governance and data security often overlap significantly, particularly in scenarios involving compliance with data protection regulations such as GDPR or HIPAA. For example, an organization must ensure that it not only secures personal data against unauthorized access (a data security activity) but also manages this data in a way that is compliant with legal standards for data accuracy and minimization (a data governance activity).
