Information Center

How Data Governance Ensures Security and Compliance.

Just as airports worldwide rely on precise coordination and rigorous protocols to ensure the safe and efficient movement of aircrafts, data governance helps organizations oversee vast data streams, guiding each “flight” of information to the correct “gate” while maintaining security and compliance. And just like air traffic control systems manage the flow of aircraft to help planes take off and land safely, data governance software provides guidance and oversight that ensures every piece of data travels from creation to destruction without interference or error.

There’s never been a greater need for sophisticated systems to manage, safeguard, and regulate your organization’s data. In this article, we explore how data governance is pivotal in maintaining data security and compliance. From establishing robust data policies to implementing advanced security protocols, we look at the importance of data governance in a landscape increasingly defined by digital innovation and data-centric strategies. We also showcase how effective data governance can make a tangible difference in enhancing operational efficiency and fostering trust in an organization's data assets.

Whether you're a CIO, business owner, or data professional, these data governance strategies and best practices can help you achieve a high level of data precision and reliability, ensuring your organization’s information assets are managed effectively.

Data Governance: A Comprehensive Overview

Data governance is the strategic framework an organization uses to manage its data assets. Its processes, policies, standards, and metrics ensure the effective and efficient use of information to achieve business goals in meaningful ways.

No longer a recommended best practice, data governance is now a necessity, as organizations increasingly depend on data for decision-making, operations, and customer engagement, all of which directly impact their success and reputation. The growing influence of big data, analytics advancements, and the increasing adoption of technologies like AI and machine learning (ML) have made data governance more critical than ever.

While these are undoubtedly exciting times for data technologies, digital transformations aren’t without their challenges and risks, including growing concerns over data privacy and security. Regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) mean organizations face ever more stringent requirements for data handling, with non-compliance resulting in hefty fines and reputational harm.

Another significant challenge? The sheer volume and complexity of data that needs to be managed. The rapid growth of cloud computing and the Internet of Things (IoT) have led to a data explosion that’s made it harder for companies to keep track of what data they have, where it resides, and how it's being used. Without effective governance, issues like data silos, inconsistent data quality, and difficulties in data integration can get in the way of creative decision-making and operational efficiency.

A common misconception is that data governance is about managing risk. However, it's also about unlocking data’s value. Crafting a robust data governance strategy ensures your organization’s data is accurate, accessible, consistent, and secure; in other words, it allows you to use your data as the strategic asset it is.

Why Most Data Governance Strategies Fail

Most organizations understand how critical good data governance is to their overall business strategy, yet many struggle to implement it effectively. Understanding why data governance strategies fail provides valuable insights into how to avoid common pitfalls.

  • Lack of clear objectives. Data governance initiatives often start without well-defined goals or a clear understanding of what they’re meant to achieve. Without specific objectives, they lack direction and purpose, making it difficult to measure success or gain organizational buy-in.
  • Inadequate stakeholder engagement. Successful data governance requires the involvement and support of stakeholders from various departments, not just IT. When initiatives are driven solely by IT, without input and buy-in from other business units, they fail to address an organization’s broader data needs and challenges.
  • Underestimating required cultural change. Data governance isn’t just about implementing new policies and technologies; it requires a fundamental shift in how an organization views and handles data. Resistance to change, lack of data literacy, and failure to foster a data-driven culture severely undermine many data governance efforts.
  • Over-reliance on technology. While technology is, of course, a vital component of data governance, it’s not a cure-all. Overemphasizing technical solutions while neglecting processes, people, and policies leads to strategies that are technologically advanced but practically ineffective.
  • Poor data quality. Even the best data governance strategies won’t make it valuable if the underlying data is of poor quality. Failing to address data quality issues at the outset leads to mistrust and decreased user engagement with governance initiatives.
  • Rigid governance structures that don’t adapt to evolving business needs. Data governance should be flexible enough to accommodate changes in business strategies, regulatory environments, and technological advancements.
  • Inadequate training and resources. Without proper training and resources, employees don’t understand their roles in data governance or how to adhere to the policies and procedures, leading to ineffective implementation.

By understanding and addressing these challenges, organizations can develop more effective data governance strategies that are sustainable, adaptable, and aligned with their business goals.

Data Governance vs. Information Governance: What’s the Difference?

While data governance and information governance are interconnected, they aren’t synonymous.

  • Data governance is a component of information governance, with a specific focus on the proper handling of data.
  • Information governance is a more inclusive framework that addresses all aspects of an organization’s information.

Recognizing and understanding the distinction between the two terms is vital if organizations are to manage their information assets effectively and strategically.

Data Governance: A Focused Approach

Data governance deals with data management, quality, and lifecycle. It is a subset of information governance but takes a more focused approach, establishing policies, procedures, and standards to ensure data accuracy, accessibility, consistency, and security. It’s central to controlling data entry, storage, and retrieval processes to maintain data integrity and reliability throughout its lifecycle.

For instance, healthcare providers need to accurately and securely manage patient records. Data governance sets rules and protocols about how patient data is collected, stored, accessed, and shared. It ensures patient information is accurate, up-to-date, and used appropriately, adhering to privacy laws and regulations like HIPAA.

Information Governance: The Broader Spectrum

Information governance encompasses a broader spectrum, referring to how an organization manages all its info, regardless of format, including digital data, paper records, and multimedia. It integrates policies, processes, and technologies to address information risks, comply with legal and regulatory requirements, and align information management with business objectives.

Using the same healthcare example, information governance extends beyond patient records to include financial data, administrative records, employee information, and more. It's about overseeing all information assets to support compliance, reduce risks, and enhance the information’s value.

Scope and focus are the primary differences between data and information governance. Data governance is more technical and specific, dealing with the nuts and bolts of data management. Information governance is broader and more strategic, addressing all types of information and its role in the organizational ecosystem.

Tackling Risks: Data Governance Challenges and Trends

Two particularly challenging aspects of implementing a data governance framework are accurately capturing and securely storing data. Compliance risks and potential legal implications of improperly archiving data make prioritizing robust data governance strategies essential.

As more organizations embrace cloud-based data storage, attracted by its cost benefits and scalability, the focus on data security and privacy has intensified. The cloud's shared environment poses risks such as unauthorized access and data breaches. And companies must navigate a growing number of global data protection laws, each setting stringent data handling and storage standards.

Another trend is big data analytics, which comes with its own set of challenges, including data quality and integrity. Because big data is so complex, it can be difficult for companies to maintain accurate and consistent data, which, in turn, increases the risk of making business decisions based on faulty information.

To overcome these difficulties, organizations must take a multi-faceted approach to data governance, including:

  • Adopting end-to-end encryption in cloud storage to significantly enhance data security.
  • Implementing robust access controls and regular security audits to further protect data.

To manage big data, companies should invest in advanced data quality tools and processes and regularly clean, validate, and update data to ensure its reliability and usefulness.

Lastly, organizations should appoint a dedicated data governance team to:

  • Keeping up with legal changes.
  • Ensure compliance with data protection regulations.
  • Monitor regulatory developments.
  • Implement necessary changes in data handling practices.

By addressing these and other data governance challenges head-on, you not only mitigate risks but also leverage your data governance framework as a strategic asset, driving innovation and business growth.

A Modern Approach to Data Governance

Traditional data governance is a centralized approach that typically relies on a central team or department to manage all aspects of an organization’s data governance. It uses manual processes for data auditing, quality checks, and compliance monitoring and focuses on fixed data formats. Its limited tools and technologies make it a reactive rather than proactive method.

Modern data governance is versatile, agile, and comprehensive to accommodate the complexity of today’s data environments. It uses a personalized, community-centered approach where everyone in the organization is responsible for data governance, not just the IT team. Most importantly, it prioritizes data value enhancement, a critical shift that encourages input and insight from all organizational levels.

By emphasizing personalization, collective involvement, and automation, today’s data governance provides a more flexible, scalable, and effective approach to managing data. Key features include:

  • Universal data accessibility. Instead of a top-down approach that often creates bottlenecks and impedes innovation, every stakeholder can access and interpret data, even if they lack specialized technical skills. By fostering a data-driven culture, organizations enhance operational efficiency and promote innovation.
  • Clear definition of roles and responsibilities. Modern data governance roles aren’t merely titles; they’re fundamental to shaping an organization’s data culture. Assigned roles ensure individual stakeholders know their part in preserving and enhancing data integrity, quality, security, and privacy.
  • Automation. Companies can leverage advanced tools and AI to streamline data governance tasks like data quality checks and compliance monitoring, reducing manual labor and increasing accuracy.
  • Non-siloed workflows. Encouraging cross-departmental collaboration and data sharing breaks down traditional data silos, enhancing decision-making and fostering a more integrated and holistic approach to data management.

Implementing an advanced data governance model is daunting, but it’s also immensely rewarding. Effective governance encourages principled data democratizing, drives open access to high-quality data, and promotes responsible data usage.

How to Create an Effective Data Governance Strategy

A data governance strategy is a comprehensive plan that defines and outlines an organization’s data governance goals and direction, laying out how a company manages, utilizes, and protects its data assets.

An effective data governance strategy emphasizes data quality, ensuring all data is accurate, consistent, and reliable. Getting there involves setting stringent data quality policies and employing robust processes to maintain data integrity throughout its lifecycle. By aligning data governance with business processes, organizations ensure their data management efforts are not isolated but integrate seamlessly into everyday operations.

Another critical task, risk management, involves identifying, assessing, and mitigating data risks, including security breaches and non-compliance. A well-crafted strategy helps organizations comply with both external regulations and internal policies, reducing the risk of penalties and reputational damage.

Current data governance strategy trends include:

  • Enablement, where organizations empower employees across the organization to use data responsibly and effectively rather than strictly controlling or limiting their access to it. This approach fosters an environment where it's easier for people to do the right thing with data.
  • Common data platforms that provide unified, accessible repositories of data, break down silos, and enable more coherent and comprehensive data utilization across various business units.
  • AI and ML. AI and ML algorithms automate data quality and cleansing processes. AI can predict potential data issues and governance needs before they become problematic and continuously monitor data to detect unusual patterns. ML algorithms classify and categorize data automatically, making it easier for users to discover and access the data they need to perform their tasks. And AI assists in analyzing existing data’s effectiveness, suggesting improvements or adjustments.

These and other modern strategies are not just about controlling data but enabling it to be a catalyst for an organization’s digital transformation.

Data Governance Best Practices: The Path to Success

Data governance serves as a critical component in leveraging data as a strategic asset. In an era where data-driven decision-making is paramount, implementing robust data governance practices helps your organization harness its data’s full potential.

The path to success in data governance relies on a precise blend of strategy, technology, and people management. These best practices ensure data is secure, compliant, and accessible, becoming a powerful tool for insightful analytics and strategic development.

Establish Clear Governance Policies and Standards

Establish clear policies and standards that define how data is collected, stored, accessed, and maintained. They should be comprehensive yet flexible enough to accommodate future changes in technology and business needs. Regularly review and update these policies to remain compliant with evolving data protection regulations.

Ensure Data Quality

High data quality is essential for reliable analytics and decision-making. Implement processes to routinely check, clean, and validate data to ensure its accuracy and usability. Set up mechanisms to identify and correct errors, inconsistencies, and duplicates in data sets and invest in automated data quality tools to provide more accuracy and efficiency. Lastly, foster a culture of quality among data handlers and users to ensure the maintenance of high data standards and minimization of errors at the source.

Promote Data Literacy Across the Organization

Data governance is most effective when there’s an elevated degree of data literacy throughout the organization. To foster a data-driven culture, train and educate employees on the importance of data and how to use it responsibly. They should also understand how it impacts their roles. Make sure team members understand your data governance policies, their roles in maintaining data quality, and the implications of non-compliance. Regular workshops, e-learning modules, and informational sessions are effective ways to enhance data literacy. 

Implement Role-Based Access Control

Controlling who has access to what data is a fundamental aspect of data governance. Role-based access control (RBAC) ensures users can access only the data necessary for their job functions, minimizing the risk of data breaches and misuse. Dynamically manage and regularly review RBAC to adapt to changes in roles, responsibilities, and organizational structures. RBAC is also essential for compliance with data privacy regulations, which often require strict controls over data access.

Utilize Technology for Automation and Efficiency

Automated tools and systems streamline various aspects of data governance, including data quality control and compliance monitoring. The technologies help manage the increasing volume and complexity of data more effectively than manual processes. Choose the right data governance tools that align with your organization's specific needs and integrate them into your existing IT infrastructure to enhance efficiency and gain better insights and reporting capabilities.

Encourage Interdepartmental Collaboration

Effective data governance requires collaboration across different departments. It breaks down data silos and encourages information sharing, ensuring a unified approach to data management. Regular meetings, collaborative platforms, and cross-functional teams help align data governance with business objectives and ensure the organization’s data policies are practical and relevant across different departments.

Monitor and Audit Regularly

Continuous monitoring and regular auditing ensure data governance policies are being followed and are effective. This includes tracking compliance with internal policies and external regulations, as well as monitoring the overall quality and security of data:

  • Risk assessment evaluates data storage, transmission, and processing practices. It also identifies points of entry for unauthorized access.
  • Threat identification identifies cyberattacks, data breaches, and human error.
  • Access controls, including multifactor authentication, ensure that only authorized individuals can access sensitive data.
  • Encryption for at-rest and in-transit data safeguards it from unauthorized access, even if intercepted.

Routine audits identify areas for improvement and help you make necessary adjustments to policies and practices. Use metrics and key performance indicators (KPIs) to assess the effectiveness of your data governance efforts and gain insight into potential improvements.

By implementing these best practices, organizations pave the way for a successful data governance strategy that’s precise, transparent, and adaptable. You secure and optimize your data assets and are better equipped to make more informed decisions, remain compliant, and maintain a competitive edge in today's data-driven world.

Does Your Organization Need a New Data Governance Strategy?

Ultimately, your organization’s data security and compliance are only as strong as the elements and people that support them. By investing in proven solutions, you ensure your data remains secure from start to finish, and you’re able to keep pace with an ever-changing data governance landscape.

At Veritas, we understand data governance is complicated. That’s why we offer an integrated portfolio of compliance and governance solutions that consolidate intelligence across data sources to surface relevant information, deliver actionable insights, and reduce the risk of costly regulatory fines. We’re proud to be named a Leader in the Gartner Magic Quadrant for Enterprise Information Archiving, as it recognizes our commitment to delivering market-leading, cloud-centric solutions that address data and regulatory complexity for our customers.

Interested in learning more about how data governance ensures security and compliance? Contact us today to discover how Veritas can help you implement a framework tailored to your organization’s unique needs and objectives.


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