With the advent of advanced technologies, organizations have access to more data than ever before. This data is used to make informed business decisions, drive growth, and provide a better customer experience. However, with the increasing amount of data comes the need for proper management and protection. Organizations that fail to protect their data face severe risks that can have detrimental effects on their operations.

One of the ultimate and perhaps most significant risks associated with data breaches is financial losses. When sensitive data is exposed, organizations may face legal fees, fines, and compensation claims. Additionally, the loss of sensitive data can result in lost revenue as a result of a decline in customer trust and confidence.

This is where reputational damage comes into the picture. When data breaches occur, the media and public may perceive the organization as careless or untrustworthy, which can have long-lasting effects on the organization’s reputation. Customers may be hesitant to do business with the organization, again, leading to a decline in sales and revenue.

Decreased customer satisfaction is another side effect of not having effective data governance in place. Customers expect their personal data to be protected, and when it’s not, they may lose faith in the organization’s ability to keep their information safe. This loss of trust can result in customers taking their business elsewhere.

Proper data governance is essential in today’s digital age to protect organizations from the risks associated with data breaches. By adopting best practices and data governance principles, organizations can proactively protect their data, maintain regulatory compliance, and safeguard their reputation and financial stability.

Understanding Data Governance

Data governance is critical for businesses to ensure that their data is accurate, reliable, secure, and compliant with legal and regulatory requirements. It’s essential to understand that data governance is not just about managing data, but also about managing the processes, people, and policies that govern data.

One of the key differences between data governance and data management is that the former is focused on the technical aspects of managing data, such as storing, processing, and analyzing it. On the other hand, data governance focuses on the policies and processes that ensure data is being managed correctly and meets regulatory requirements. You can learn more about the differences between data governance and data management, here.

Data governance comprises several components, each of which plays an essential role in managing data effectively.

• Data policies and standards establish guidelines and rules for how data should be managed and maintained throughout its lifecycle
• Data stewardship and ownership ensure accountability for data management, quality, and protection
• Data architecture and technology deal with the infrastructure required to manage data effectively
• Data security and privacy ensure that sensitive data is protected from unauthorized access and misuse
• Data lifecycle management involves managing data from creation to disposal, ensuring that it is appropriately archived or destroyed

Together, these components help organizations achieve several common benefits. Improved data quality and accuracy lead to better decision-making, while increased trust and accountability improve overall organizational efficiency. Better compliance and risk management enable organizations to meet regulatory requirements and avoid legal penalties. Ultimately, effective data governance helps organizations use their data assets to drive business growth and remain competitive in today’s digital age.

Essential Principles of Data Governance

Now we’ve covered the basics, let’s get into the principles that organizations should be considering for their data governance strategy…

1: Establishing Data Governance Policies and Procedures

Establishing data governance policies and procedures is critical for organizations to ensure that their data is managed effectively. There are several reasons why having data governance policies and procedures is important.

First, they help organizations maintain data quality and accuracy. By establishing clear guidelines for how data should be managed, monitored, and maintained, organizations can ensure that their data is always reliable and accurate.

Second, by defining clear roles and responsibilities for data management, transparency, and accountability are increased throughout the organization. This helps to avoid confusion or disputes over who is responsible for data-related issues.

Third, many industries are subject to strict regulatory requirements relating to data management, such as data privacy laws, security standards, and industry regulations. Having data governance policies and procedures in place can help organizations better comply with these regulations and avoid costly penalties for non-compliance.

Fourth, when data is managed in a consistent and standardized way, it can be more easily analyzed and used to support decision-making across the organization. Having policies in place ensures that data is captured, stored, and managed in a way that is consistent, and aligned with the organization’s goals and objectives.

Finally, establishing a strong foundation for data management can support data-driven innovation within the organization. In turn, this can help drive business growth and competitive advantage by enabling organizations to make more informed decisions based on reliable data.

To create and implement data governance policies and procedures, organizations should follow these steps:

1. Define data governance goals and objectives to help guide the development of policies that align with the organization’s overall data strategy
2. Identify key stakeholders and roles across the organization, including IT, legal, compliance, and business units
3. Develop policies and procedures based on the organization’s goals and objectives, showing how data should be managed throughout its lifecycle, from creation to disposal
4. Monitor and enforce policies and procedures to ensure that they are being followed and are effective in achieving the organization’s goals and objectives

Some examples of data governance policies and procedures that organizations may consider implementing include data classification policies, which define how data should be classified based on its sensitivity, criticality, and regulatory requirements. Data retention policies define how long data should be kept and when it should be disposed of. Data quality policies look at how data quality should be measured, monitored, and maintained. Data access policies review who has access to data and under what circumstances. Finally, data-sharing policies define how data can be shared both internally and externally.

Establishing data governance policies and procedures is critical for organizations to ensure that their data is managed effectively, complies with regulatory requirements, and drives business growth. Organizations can create a strong foundation for data management by following the steps outlined above, and ensure that their data is reliable, secure, and aligned with their overall goals and objectives.

2: Identifying and Categorizing Data Assets

Identifying and categorizing data assets is an essential aspect of data governance that enables organizations to better understand their data landscape and make informed decisions about how to manage, use, and protect their data.

The process involves conducting a data inventory to identify all data assets, including data type, format, location, and ownership. This can be done through data discovery tools, data mapping exercises, and manual audits.

Once data assets have been identified, they can be categorized based on various characteristics such as sensitivity, security requirements, and purpose. This categorization process enables organizations to prioritize their data management efforts and allocate resources accordingly. For example, highly sensitive data may require stricter security controls, while less sensitive data can be managed with fewer controls.

One example of a data categorization scheme is the Criticality, Sensitivity, and Privacy (CSP) framework, which categorizes data assets based on their criticality to the organization, sensitivity to disclosure or unauthorized access, and privacy requirements. Another example is the Data Asset Framework (DAF), which categorizes data assets based on their business value, technical quality, and risk.

Regardless of the categorization scheme used, it’s essential for organizations to ensure that data assets are accurately categorized and that appropriate controls are in place to protect sensitive data and comply with regulatory requirements. This involves implementing security measures such as access controls, encryption, and data masking, and ensuring that data is only accessed and used by authorized personnel.

3: Assigning Data Stewardship Roles and Responsibilities

Assigning data stewardship roles and responsibilities is a critical aspect of data governance that ensures accountability for the management, quality, and protection of specific data assets. It promotes data ownership and facilitates collaboration among stakeholders, ensuring that data-related decisions are made in a coordinated and efficient way.

To assign data stewardship roles and responsibilities, organizations must first identify the data assets that require stewardship and determine the roles and responsibilities needed to manage them effectively. This typically involves creating job descriptions, defining workflows, and establishing governance policies and procedures, as mentioned above.

Once these roles and responsibilities have been determined, individuals should be adequately trained to perform their roles effectively.

Some examples of data stewardship roles and responsibilities include data custodians, who are responsible for the physical storage and protection of data assets. Data managers oversee the collection, processing, and quality of data assets, and data stewards are responsible for ensuring the accuracy, consistency, and compliance of data assets. Data governance committees also play a critical role in ensuring effective data management by providing oversight and guidance on data management practices, while data privacy officers ensure that data privacy regulations are being followed.

It’s essential that data stewardship roles are clearly defined, with specific responsibilities assigned to each role. This way, organizations can ensure that each role is adequately supported and that data-related decisions are made in a coordinated and efficient manner. It’s also important to establish regular communication and collaboration among stakeholders to ensure effective data management, with data stewardship roles and responsibilities clearly communicated throughout the organization.

4: Ensuring Data Quality and Integrity

Ensuring data quality and integrity is an essential principle for managing and protecting your organization’s data.

Establishing data quality standards is the first step in ensuring data quality and integrity. These standards provide guidelines for how data should be collected, stored, processed, and maintained. Organizations need to establish these standards based on their specific business needs and objectives.

Once data quality standards have been established, organizations need to implement data quality checks and monitor data on an ongoing basis. Data profiling tools can be used to identify anomalies or inconsistencies, data validation rules can be implemented, and data cleansing activities can be performed to remove duplicates, errors, or missing data. These activities ensure that data is accurate, complete, and consistent across all systems.

Data completeness checks are one example of data quality tools. They ensure that all required data fields are present and contain valid values. Data consistency checks ensure that data is consistent across different sources or systems, while data accuracy checks ensure that data is accurate and reliable.

Some other examples of data quality and integrity checks may include data validation checks that look at whether data meets specific validation rules or criteria, or data transformation checks that review whether data is transformed accurately from one format or structure to another. Data lineage checks ensure that data can be traced back to its original source or system.

Organizations need to establish data quality and integrity as a priority and invest in data quality tools and technologies. They also need to involve stakeholders from across the organization in ensuring the accuracy, consistency, and completeness of data assets. Data quality and integrity checks should be performed regularly and systematically to ensure that data is of the highest quality and can be trusted for decision-making and compliance purposes.

Organizations need to establish data quality standards, implement data quality checks, and involve stakeholders in ensuring the accuracy, completeness, and consistency of data assets.

5: Managing Data Security and Privacy

One effective way to manage data security and privacy is to put in place security and privacy policies. These policies should outline the organization’s approach to data security and privacy and provide clear guidelines on how employees should manage and protect data assets.

Organizations should also perform regular risk assessments to identify vulnerabilities or weaknesses in their data security and privacy practices and establish controls and procedures to prevent unauthorized access or disclosure of data.

Encryption technologies, access controls, and data masking techniques are just a few examples of tools that organizations can use to protect data at rest or in transit. Data encryption is a process of encoding data to prevent unauthorized access, and access controls can be used to limit who has access to sensitive data. Data masking techniques can be used to obscure sensitive data, so it’s not visible to unauthorized individuals.

In addition to these technical measures, organizations need to establish user access controls and authentication mechanisms to ensure that only authorized individuals have access to data. Implementing regular security audits, data classification schemes to determine the sensitivity of data assets, and monitoring and logging activities are also essential to detect and respond to security incidents.

Alongside this, organizations should implement data retention and disposal policies to ensure that data is deleted or destroyed when it is no longer needed. Providing regular training and education to employees on data security and privacy best practices can also help to raise awareness of potential security threats and reduce the risk of accidental data breaches.

To effectively manage data security and privacy, organizations need to take a comprehensive approach, involving stakeholders from across the organization, staying up-to-date on the latest security threats and privacy regulations, and continually reviewing and improving their data security and privacy practices.

6: Establishing Data Retention and Disposal Policies

Developing data retention and disposal policies is a critical data governance principle. Data retention policies help organizations manage the volume of data they collect, store, and process. When organizations retain data without a proper retention policy, the storage costs can become very high, and the risk of data breaches or misuse increases in tandem.

To establish data retention and disposal policies, organizations need to first identify their legal, regulatory, and business requirements for retaining data. They may need to consult with legal and compliance teams, review industry standards and best practices, and consider the impact of data retention on operational efficiency and risk management.

Once the retention requirements have been established, organizations can then develop policies and procedures for storing and disposing of data assets. Establishing retention periods for different types of data, ensuring that data is securely stored and protected during the retention period, and implementing processes for deleting or destroying data when it is no longer needed are all parts of this process.

For example, retaining financial data for a certain number of years to comply with tax regulations, retaining customer data for a specific period of time to support business operations, and deleting personal data of customers or employees once it is no longer required for the purpose it was collected are all examples of data retention or disposal policies.

Regardless of the specific policies and procedures used, it’s important for organizations to document their data retention and disposal practices, communicate them to stakeholders, and monitor their effectiveness regularly. This can be achieved through regular audits and reviews of the data retention policies and procedures, as well as regular communication with all stakeholders to ensure that the policies are being followed and remain effective.

By developing and implementing these policies and procedures, organizations can better manage their data, reduce costs, and minimize the risk of data breaches or misuse.

7: Monitoring and Auditing Data Governance Activities

Monitoring and auditing data governance activities are crucial for organizations to ensure the effectiveness of their data governance policies and procedures, and to identify any gaps or weaknesses in their data governance program. It helps organizations demonstrate compliance with legal and regulatory requirements, mitigate risks and maintain the accuracy, completeness, and security of their data assets.

Organizations can monitor data governance activities through various methods, such as data quality checks, data security assessments, and compliance audits. For example, monitoring data access logs can help identify unauthorized access or data breaches, while conducting data quality checks can ensure data accuracy and completeness. Reviewing data retention and disposal policies can also help ensure compliance with legal and regulatory requirements.

To effectively monitor and audit data governance activities, organizations can establish KPIs and metrics to measure the effectiveness of their data governance program. This often includes monitoring data quality, data security, and compliance with data retention and disposal policies, among other factors. Periodic audits can also be conducted to assess the effectiveness of the program and identify areas for improvement.

Compliance audits can ensure adherence to relevant regulations, while risk assessments can identify potential vulnerabilities and risks. Additionally, establishing data governance scorecards or dashboards can track progress against KPIs and metrics.

It’s essential for organizations to establish clear objectives, communicate findings and recommendations to stakeholders, and continuously improve their data governance program based on feedback and results. This can help organizations ensure the effectiveness of their data governance program and reduce the risk of data breaches, non-compliance, and reputational damage.

Implementing Data Governance in Your Organization

Data governance is critical for organizations to manage their data assets effectively, reduce risk, and drive business value.

To implement data governance in your organization, you should first establish a data governance framework that aligns with your organization’s needs and goals. This framework should include identifying and categorizing data assets, assigning data stewardship roles and responsibilities, ensuring data quality and integrity, managing data security and privacy, and establishing data retention and disposal policies. Communication is key, and you should ensure that these steps are communicated clearly to all stakeholders.

However, there are common challenges that organizations face when implementing data governance. Resistance to change, lack of buy-in from key stakeholders, inadequate resources or funding, and difficulty in aligning data governance with business objectives are all potential obstacles to successful implementation. To overcome these challenges, you can engage in increased communication and education efforts, establish a clear business case for data governance, secure executive sponsorship and support, and leverage technology and automation to streamline data governance processes.

To achieve successful data governance implementation, you should align data governance with business objectives, engage stakeholders, implement a phased approach to implementation, leverage existing technologies and data management tools, and continuously monitor and improve data governance processes. You should establish clear goals and KPIs, document policies and procedures, and ensure compliance with legal and regulatory requirements.

Organizations can also benefit from leveraging industry standards and best practices provided by organizations such as the Data Management Association (DAMA), the International Organization for Standardization (ISO), and the General Data Protection Regulation (GDPR). By aligning their data governance program with these standards and best practices, organizations can improve their credibility and actively demonstrate their commitment to effective data governance.

Finally, successful data governance implementation requires ongoing commitment and investment from all stakeholders. It’s important to establish a governance structure that includes cross-functional teams, establish a governance council or steering committee, and conduct regular training and education programs to ensure that all stakeholders are aware of their roles and responsibilities and are equipped to contribute to the success of the data governance program.

Conclusion

There you have it – the essential principles of data governance, mixed in with how organizations can implement data governance and the benefits of doing so.

Organizations need to prioritize data governance for all the reasons outlined above, but it can be a daunting task, especially if you don’t have the resources, skills, or know-how to do an effective job. Something else that may sway an organization’s decision on how to approach data governance is the top-down versus bottom-up debate. This is where organizations either choose to start with the strategy and policies and work their way down (top-down approach) or start small with a single process and then build up their understanding and knowledge of data governance as they go along, while establishing their data management strategy (bottom-up approach).

Data governance can be a huge task, that’s why at Exmon, we advocate the bottom-up approach – get one aspect 100% right and working, and then expand from there. That’s why companies like us exist, to help organizations successfully adopt and practice data governance, one step at a time.

If you’re looking to bolster your data governance efforts or simply don’t know where to start, reach out to an Exmon expert to learn how we can help you protect your data.