Ensuring Security And Privacy In Life Sciences Master Data Governance

By Ravikumar Vallepu

For life sciences organizations to succeed in an ever-evolving digital world, it is vital to ensure the security and privacy of master data. Core data domains such as product, consumer, and supplier data serve as the foundational building blocks upon which businesses are built. Unlike transactional or everyday data generated by business activities and events conducted within an organization, master data represents critical entities and serves as the reference point for transaction data.

Master data is maintained centrally and often shared across multiple business processes and transactions. Examples include customer names and addresses, product descriptions and procurement details, supplier payment terms, and employee names and positions. Because master data can contain sensitive and personally identifiable information, robust governance is essential.

Master data governance establishes protocols and helps companies cleanse and standardize their data ensuring its accuracy, consistency, and safety. It streamlines data governance processes, improves data quality, and enhances decision-making capabilities. Master data governance offers a user-friendly interface and customizable workflows to help account for and resolve transparency, maintenance, data ownership, change management, compliance, accountability, authority, auditability, data stewardship, standardization, and education.

Best Practices For Master Data Governance Success

A comprehensive master data governance plan should address the critical aspects such as data classification, quality, ownership, access, life cycle, and data culture. By tackling each, IT teams can play a critical role in ensuring high-quality and secure data that serves as the lifeblood of innovation and organizational success.

To maximize its full power, it is critical for IT teams to ensure that master data is safe, secure, and reliable. A failure to implement master data governance best practices can create significant security and privacy concerns, making it vital to classify data by sensitivity, value, and risk for ensuring efficiency and security. Consider the following best practices:

• Determine data ownership. Begin by defining roles and responsibilities among the master data governance team—administrators, stewards, owners, users, and others. Analytics8, a data and analytics consulting firm, breaks down the duties that come with specific data governance rules within a given organization. Data stewards, for example, “are responsible for executing the day-to-day governance activities,” ensuring that data is appropriately classified, documented, and protected consistent with the organization’s defined policies and standards. Stewards collaborate with data owners, data users, and other stakeholders to resolve issues, address quality concerns, and support data-related initiatives. Analytics8’s Julia Liceaga adds that data stewards “need to have a good understanding of both business and technical aspects to effectively carry out their responsibilities.” Adopting a shared responsibility model helps an organization break down information barriers and encourages collaboration facilitating a more integrated and efficient data governance process.
• Identify and classify data. Classification of data according to its sensitivity, value, and risk helps determine the appropriate level of protection, access control, and retention policies for various types of master data, including personally identifiable information (PII) such as names, addresses, phone numbers, or social security numbers that may require higher encryption. PII could necessitate stricter authentication and shorter retention periods than less sensitive or public data. Proper data classification has several benefits. Establishing access control, for example, ensures that only authorized individuals can access sensitive information, thereby reducing the risk of unauthorized data exposure. Correct encryption guarantees that stolen data remains unreadable to unauthorized parties even if a breach occurs. Sensitive data should be retained only as long as necessary to minimize potential exposure, and data should be categorized to ensure regulatory compliance and to avoid legal consequences due to noncompliance with such guidelines as the EU’s General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).
• Establish access management parameters. Implementing robust security measures, including authentication, encryption, and authorization, limits access to only authorized users and helps minimize potential risks. Regular audits, reports, and alerts enhance monitoring capabilities and allow for quick detection of unauthorized activities. To protect data subject identity and privacy, consider such techniques as anonymization in which critically identifiable data is either altered or deleted for security and identity protection. Adhering to these practices helps prevent unauthorized or inappropriate access, fortifying overall data security and compliance.
• Define data life cycle management. Data life cycle is best defined as a series of phases over the course of (data’s) useful life. Establishing and enforcing governance policies during the life cycle are a necessity for all data management and security.
• Foster a data-centric culture. A data-centric culture describes an organization in which data is the foundation of everything from planning and strategy to operations and other decisions. Such a culture is best cultivated through regular training, education, and communication on best practices, standards, and regulations for data security and privacy, trust, transparency, and responsibility. A data-centric culture also can empower and engage stakeholders by involving them in decision-making, feedback, and improvement processes for master data governance.

Implementing and adhering to data standards are critical for establishing benchmarks for data quality. It is also vital for organizations to create mechanisms to enforce these standards, maintain the relevance of master data, and mitigate potential issues.

What’s Around The Corner?

Looking ahead, issues with data quality, data security, and creating organizational awareness around data governance should always be considered permanent and paramount as should ethics—another vital component of any data strategy. According to McKinsey, individuals and companies worldwide will produce an estimated 463 exabytes of data daily by 2025. Additionally, the democratization of data will continue, enabling employees at all levels to become more comfortable applying data for decision-making and improving job performance.

For these reasons, it is essential to develop and nurture a culture where employees are comfortable asking probing questions about data governance and requesting additional training and tools to help them improve their data skills. Companies can foster a data-centric culture by communicating best practices, emphasizing the importance of regulatory compliance, and cultivating trust and transparency.             

Ultimately, master data governance is a crucial part of an organization’s overall data management strategy if it expects to leverage opportunities manifested by a well-formulated, consistent, and trustworthy data culture. Because the proliferation of AI and ongoing privacy concerns will continue to impact data security, business and IT functions will face mounting challenges surrounding master data governance, among them data quality and consistency, establishing data governance policies and procedures, and change management. Developing effective master data governance protocols will be imperative if the organization and its culture are to overcome these hurdles and prepare for the future.

About The Author:

Ravikumar Vallepu has more than 14 years of experience in master data governance, technology consulting, digital transformation, and process design. He has proven success in leading, managing, and delivering complex projects across diverse industries, including life sciences. For more information, contact Ravikumar.vallepu@outlook.com.