A Guide to Managing Data Risks Through Data Governance Practices

Data risk management is the practice an organization uses with governance, process, procedures, and compliance for acquiring, storing, processing, transforming, and using data to manage and eliminate data risks. Risk analysis involves looking at organizational assets, possible threats, and vulnerabilities to determine risk, and then putting in countermeasures relative to managing risk.

What Causes Data Risk

• Poor data governance: Organizations have to ensure that their data is of high quality to support organizational decisions. Good data governance balances the need to democratize data against the need to know and need to use data.
• Data mismanagement: The entire lifecycle of data has to be protected as data moves from one stage to the next, including data at rest and in transit. All practices for acquiring, storing, transforming, loading, and processing of data have to be managed appropriately.
• Inadequate data security: Organizations have to keep up with protecting their environment from cyberattacks and unintentional internal data compromises, with patches, education, a zero-trust model, etc.
• Bad patch management: System patches have to keep up to date and be timely; a window of opportunity for cyberattacks can happen if patch management is not done effectively and efficiently. As much as possible, patch management should be bulletin-based and automated.

In accordance with US-CERT, organizations should implement an automated, risk-based IT security program, covering all infrastructure, applications, and data both on-premises and across their cloud deployments. Rarely is either the hardware or the application environment permanently damaged or has downstream, unpredictable ramifications; the same cannot be said for data risks. A Prevent-Detect-Respond risk analysis must focus on data risks as the core for a CDM cybersecurity program.

Some Benefits of Managing Data Risks

• Reduce cost to an organization in many ways, including regulatory fines, time wasted, customer retention, and more.
• Reduce risk by being proactive instead of reactive with a strategy for managing all risks.
• Increase the agility of the organization. Data risk assessment and management is a proactive practice to support the organization’s business availability based on reacting swiftly to challenges.
• Maintain organizational longevity by having the ability to deliver and support services and products. Without managing data risk appropriately, organizations are in danger of failure.
• Increase customer satisfaction. Customer happiness is based on an organization’s ability to perform securely by protecting the shared data to conduct business. Good customer satisfaction surveys and net promoter scores have as a foundation the organization’s ability to manage data risk.

The benefit of data risk management is worth the associated costs. With this as a practice and discipline within an organization, the success of the organization increases. The following data risk management guide will give some tips and guidance on how to proceed with data risk management practice within your organization.

How to Implement Data Risk Management Via Governance

Data risk management cannot be an afterthought within any organization. There needs to be strategic intent, executive sponsorship, and cultural change for data risk management to be successful. Start by creating a team with accountability and responsibilities for data risks. Produce an overall RACI matrix relative to data risk management Policy. Hire a Data Protection Officer (DPO) to be accountable for creating a data risk management framework. Include in the framework goals, objectives, and measurements.

Data Governance Roles for Reducing Risk

Effective data governance and risk management requires organization-wide adoption, clearly defined roles, and accountability structures that reduce operational, regulatory, and reputational risk. By assigning ownership and oversight across the data lifecycle, organizations can proactively manage data quality, security, privacy, and compliance obligations.

Chief Data Officer (CDO)

The CDO sets the enterprise-wide data strategy and governance framework. This role ensures alignment between business objectives, regulatory requirements, and data management practices, reducing strategic and compliance risk.

Data Owners

Data owners are accountable for specific data domains (such as customer, financial, or operational data). They define quality standards, access policies, and usage guidelines, ensuring data is fit for purpose and protected from misuse.

Data Stewards

Data stewards manage day-to-day data quality, metadata, and policy enforcement. They monitor data accuracy, consistency, and completeness, escalating issues before they become systemic risks.

Data Custodians (IT / Engineering Teams)

Custodians are responsible for the technical environment—data storage, infrastructure, backups, and security controls. They implement safeguards such as encryption, access controls, and monitoring systems to protect against breaches and data loss.

Compliance and Risk Officers

These stakeholders ensure adherence to regulations and internal policies. They assess regulatory exposure, conduct audits, and guide remediation efforts to minimize legal and financial penalties.

Information Security Teams

Security professionals manage threat detection, vulnerability assessments, and incident response. Their oversight helps prevent unauthorized access, data exfiltration, and cyber risks.

When these roles are clearly defined and supported by documented policies and governance workflows, organizations create a structured system of checks and balances. This layered accountability significantly reduces data-related risks while strengthening trust in enterprise data assets.

General Data Risk Management Tips

• Identify risk, threat, vulnerability. Perform a data center risk assessment.
• Assess probability and impact, perform a business impact analysis. It may be helpful to use third-party support. Address financial implications and impact over time to determine priorities and actions to address data risks.
• Define governance, policies, regulations, and compliance needs, including identifying and adopting best practices for data risk management
• Assess current controls in place and continuously do this activity relative to the changing risk landscape. Implement controls in practices, processes, and work instructions across the organization.
• Test the plan to make sure it works; if not, adjust the plan and redefine any other aspect of data risk management.
• Monitor risk and provide feedback, use automated tools as much as possible, and get feedback from people around the organization.
• Continually improve the plan as needed.

Some Potential Risks to Identify and Manage

The following are a few common issues to look out for and adopt policies to manage.

Data Corruption

Data corruption can happen to data anytime, during reading, writing, transmission, loading, processing, etc. Make sure to identify the data replication, duplication, backup, and recovery, and manage risk related to each stage in the data management lifecycle.

Device Failure

Device failure on-premise and cloud, where the data resides, is possible. Organizations should understand the complete IT device stack and associated data and prepare data risk plans and actions.

Data Compliance Issues

Be sure to be compliant with both customer and partner-facing as well as industry- and region/nation-specific regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Criminal Justice Information Services (CJIS), Family Educational Rights and Privacy Act (FERPA), International Traffic in Arms Regulations (ITAR), and California Consumer Privacy Act (CCPA). Data privacy and trust have to be managed, and risks accounted for—each one of these regulations emphasizes the necessity for Data risk management.

Data Remanence

Data remanence through data retirement practices, if not done correctly, can result in a risk to the organization. Be sure to verify that this is happening appropriately.

Failure to Audit Security

Identification of security flaws and weaknesses can be done with audits. Data audits are always a good thing to do within organizations for managing risks.

Poor Employee Education

The people within an organization should be aware of possible breaches to security and threats – malware, phishing, spyware, etc.

Cloud Storage Risks

Cloud SaaS, PaaS, and IaaS risks for each service should be identified and managed. Many organizations do not use all the cloud services, but still should be aware of how the other services can affect them, related to managing the risk of data.

Lack of Incident Response Preparation

A major incident response is critical to have in place when a data management issue occurs. This activity should also be practiced or simulated to ensure it is effective.

Physical Security Threats

Physical security must always be identified and understood relative to human breaches or environmental breaches, including on-premise and cloud physical security. Organizations should understand how both their own company and a vendor address physical security, particularly in office environments that are oscillating between work-from-home and return-to-office policies.

Third-Party Threats

Third-party software and infrastructure risk should be identified. Any third-party vendor is subject to a data breach that can affect its customers. Data breaches that result in software being hacked and then installed in their customers’ environments can have lasting effects on the organization. All organizations should understand how their vendors secure their data, especially the data about their customers.

Additional Data Management Areas to be Aware Of

• Backups’ importance cannot be underestimated for the organization and for us personally. They provide data recovery in case of power failure, hacking, environmental disasters, human error, etc. This is essential for managing data risk.
• Redundancy improves the availability of your organization to do business. Redundancy of data helps manage the risk of data loss from unexpected and expected outages that may occur.
• People Risks related to data management should be identified and managed. An essential tactic is to educate people and make them know how to manage data risks within the organization. Make sure people understand and can act when needed for data risks. The practices that they learn at work can also help with personal data leakage and personal data risks.

Today, with organizations adopting Big Data practices and technologies. The risks of big data implementations in an organization need to have a data risk management strategy. An organization should review its Big Data architectures and identify data risks in its on-premise, cloud, and hybrid cloud environments. Cloud Data migration risks and Cloud storage security risks of data should be reviewed carefully. Cloud architecture adjustments may be necessary to address cloud data risks and costs. Big Data risk management should be a component of an overall data risk management strategy.

Data Governance Is Necessary to Reduce Data Risk

Data risk management is the responsibility of all functions across the lines of business, marketing, sales, human resources, operations, applications, legal, etc. Taking a proactive approach by identifying risk, adding controls, and preparing for action can make a world of difference when needed. Do not make data risk management an afterthought. It is a part of the cost of doing business and should be understood as such. Be careful of shortcuts and be strategic and comprehensive with the approach.

How the Actian Data Intelligence Platform Supports Data Governance and Risk Management

Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.

Through its centralized interface, the platform supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.