“Privacy is a data problem with legal implications, not a legal problem with data implications”.

Debbie Reynolds “The Data Diva”

Many organizations have traditionally considered privacy a regulatory, compliance, and legal risk. However, the convergence of factors such as rising data breaches, a lack of transparency in data handling, and the growing adoption of emerging technologies has resulted in an erosion of customer trust, an increase in data breaches, and dependence on increasingly complex data systems. These forces together are creating a new Data Privacy Vector of Business Risk.

This Data Privacy Vector of Business Risk is created when data problems snowball and create more risks as data is collected, multiplied, duplicated, and used in many areas of an organization. As the data travels and goes deeper into organizations and data systems, the controls can be lost, the memory of the data line can be lost, and the data can be used in ways that are not only bad for individual privacy but also for business.

To minimize these kinds of risks, organizations must shift left, spending more time and attention on data issues before they can become legal issues. Organizations can adopt three strategies to better address the new Data Privacy Vector of Business Risk, which are, data risk prevention, data curation, and data lifecycle sunsetting..

Data Privacy Vector Risk Strategy - Data Risk Prevention

Data risk prevention is the first defense against the Data Privacy Vector of Business Risk. It involves taking proactive steps to identify potential data risks before they manifest into more significant issues. Organizations must implement robust data governance frameworks that include policies, processes, procedures, and technologies designed to protect data by enabling privacy by design thinking to minimize future Data Privacy risks.

Key elements of data risk prevention include:

Purpose Tracking - Ensuring that the purpose for which data was collected travels with the data throughout its lifecycle. This helps clarify why the data was collected and prevents its misuse.

High-Risk Use Case Monitoring - Develop a plan to be alert for high Data Privacy risk use cases. This includes identifying scenarios where data usage could pose significant privacy risks and implementing measures to monitor and mitigate those risks.

Regular Audits and Assessments - Implement audits and assessments regularly to identify high-risk future data uses. These assessments should include technical and non-technical aspects of data handling practices to ensure comprehensive risk identification and mitigation.

Data Privacy Vector Risk Strategy - Data Curation

Data curation involves the ongoing management and oversight of data to ensure its quality, integrity, and compliance with relevant regulations. Effective data curation practices can help organizations mitigate the Data Privacy Vector of Business Risk by controlling their data assets and ensuring that data is used appropriately and responsibly.

Key elements of data curation include:

Understanding the Context of Proper Data Uses - Understanding the context in which data should be used is crucial for effective data curation. Organizations must ensure that data is used in ways that align with its intended purpose and comply with relevant regulations and standards.

Minimizing Data Redundancy - Avoiding unnecessary data duplication across systems helps maintain data integrity and reduces Data Privacy risks. Implementing practices that ensure data is captured and used appropriately across the organization is essential.

Data Stewardship - Assigning data stewards responsible for overseeing the management and use of specific data assets can help ensure accountability and reduce Data Privacy risks. Data stewards should deeply understand the data they manage and ensure that it is used according to organizational policies and standards. Also, it will be vital for data stewards to understand how data flows and is used in other parts of the organization to have a more holistic view of data risks. 

Data Privacy Vector Risk Strategy - Data Lifecycle Sunsetting

Data lifecycle sunsetting involves the systematic and deliberate process of retiring data that is no longer needed or relevant. Data within organizations with low business value often presents some of the highest Data Privacy risks for organizations. By implementing robust data lifecycle sunsetting practices, organizations can reduce the amount of data they hold, minimizing the potential risks associated with data storage and use.

Key elements of data lifecycle sunsetting include:

Data Retention Policies - Establishing clear data retention policies that specify how long different data types should be retained can help organizations manage their data more effectively. These policies should be based on regulatory requirements, business needs, and best practices for data management.

Regular Data Deletion - Implementing regular processes for deleting no longer needed data can help ensure that data is removed promptly and consistently. This reduces the risk of retaining data beyond its useful life and minimizes the potential for data breaches, unauthorized access, or data misuse.

Data Anonymization - Anonymizing data can further protect individual privacy by ensuring that it cannot be traced back to specific individuals. This process involves removing or obfuscating personally identifiable information (PII) or personal data from datasets.

Navigating the emerging Data Privacy Vector of Business Risk requires organizations to adopt proactive and comprehensive strategies to mitigate potential data-related issues before they escalate into significant legal risks. By prioritizing data risk prevention, organizations can ensure robust data governance and prevent misuse of collected data. They can maintain data integrity and compliance through diligent data curation while minimizing redundancy and ensuring responsible data use. Implementing effective data lifecycle sunsetting practices allows organizations to systematically retire unnecessary data, reducing potential privacy risks. Embracing these strategies not only safeguards individual privacy but also fortifies business resilience against the multifaceted challenges posed by evolving data landscapes and helps organizations make Data Privacy a Business Advantage.