Data governance continues to be a significant challenge for many life sciences and biopharma companies. With regulatory bodies enforcing stricter regulations and guidelines, companies must ensure the reliability, accuracy, and consistency of data generated throughout the lifecycle of pharmaceutical products. 

Strong software practices ensure compliance with regulations like HIPAA and FDA guidelines. Discover key principles for GSEP in FDA submissions.

As more data sources and types are utilized across various teams and functions, data governance has become a vital aspect of effective leadership that supports the establishment of a tightly controlled, data-first culture.

Risks of incomplete or inadequately implemented data governance include non-compliance with regulations such as GDPR, inaccurate results, delays in product development and launches, loss of revenue, regulatory penalties, reputational damage, and compromised patient safety.

In this article, we'll cover the elements for establishing strong data governance within your organization, provide advice on developing a robust data governance strategy, and share tools and frameworks for some specific use cases that are common in life sciences and biopharma.

Importance of Data Science Leadership

Data governance is the framework and strategies for storing, controlling, handling, and using data within your organization. It clearly defines the data principles, infrastructure, practices, and tools that are implemented to establish transparent processes whenever data is present or being used. By setting clear roles and responsibilities - from dataset owners to data users - it ensures trusted, reliable, quality data as a resource for project teams and decision-makers and aligns with organizational objectives and regulatory requirements.

Data management is the practical day-to-day implementation of the organizational processes and practices that are defined in the data governance framework. 

Data governance answers institutional questions around data, including:

• How does the business define data?
• Where has the data come from?
• Who is responsible for the data?
• Who has access to the data?
• How can that data be used?
• How is the data stored?
• How is the data quality ensured?
• How is security and privacy implemented?

In life sciences and biopharma, robust data governance delivers documented and auditable roles, responsibilities, and processes that support organizational and regulatory compliance by providing transparency, accuracy, and standardized processes across the organization.

The benefits of strong data governance include high-quality data available for R&D, accelerated product development timelines, reduced risk of data breaches, improved decision-making, enhanced regulatory compliance, demonstrated transformational leadership, and a reputation for trustworthiness and credibility.

The significant risks of inadequate data governance include regulatory penalties, compromised patient safety, errors in clinical trials, delayed product launches, costly rework, and reputational damage.

Creating a Data Governance Strategy

When creating your data governance strategy and framework, you will need to include:

Policies and standards that ensure consistent definitions and expectations for data usage, quality, privacy, and security throughout the data's lifecycle, from creation and storage to archiving and deletion.

Compliance frameworks that establish and outline processes and protocols to meet all legal and regulatory requirements.

Roles and responsibilities defined, with clear accountability and expectations for data quality, security, and lifecycle management.

Data quality processes and standards to ensure a consistent approach and implementation of data cleansing, validation, and monitoring.

Data access and security to ensure that sensitive data is accessed via permissions and only authorized users can modify data.

Data lineage tools that clearly show where data has originated from and any steps it has gone through demonstrate data transparency, usability, and traceability.

Mapping of data infrastructure that clearly communicates the structure of data systems and the flow of data between different technologies and datasets.

Monitoring metrics and processes to evaluate the effectiveness of data governance and identify opportunities for improvement.

The following steps will guide you toward a successful data governance framework and strategy:

1. Conduct an assessment of your current state of data governance - define what is working well and what isn't.
2. Identify data owners, users, and key stakeholders to be involved in the data governance development.
3. Define your goals and objectives.
4. Identify critical data assets and evaluate high-risk data security controls.
5. Develop the data policies and standards.
6. Research and decide on the tools and technologies that will be used.
7. Assign roles and responsibilities within the governance structure, including a Chief Data Officer (CDO), a data governance committee that reports to the CDO, data stewards to encourage and enforce data governance, and assign data owners to datasets.
8. Implement tools for access, security, lineage, data catalog, metadata, etc.
9. Establish KPIs to evaluate the performance of the strategy.
10. Promote data governance to enable a data-driven culture where each person is aware of the framework and their own role.
11. Create a data committee that fosters continuous improvement and engagement.

Best Practices for Implementation 

Some best practices to consider when developing and implementing data governance:

Involve stakeholders and listen to their issues and advice.

Calculate how much resourcing will be required for development, implementation, and ongoing support. It is an easy trap to fall into, to think this is an administrative and technical project. Data governance contains a considerable change management/cultural component to ensure that it is embedded into a company's daily data processes.

Include ongoing evaluation in your planning so you can learn what has and hasn't worked to refine and improve data governance processes and applications.

Evolving technologies, regulations, and internal processes require that data governance documents and processes be prepared for updates that allow them to adapt to new circumstances.

There’s nothing more constant than change, so plan for evolving technologies and policies. Your data store today won’t look the same in five years, so build with modularity and adaptability in mind, accounting for advancements like cloud computing and emerging tools.

Tools and Frameworks for Data Governance

There are many paid and open-source solutions aimed at solving different data governance challenges. There are also all-in-one consolidated platforms that integrate all the typical data governance tools under one banner, such as Collibra, Informatica Axon, and Microsoft Purview.

An overview of the different tools:

Data catalog: Organize and classify datasets to make data searchable. Paid tools include Alation, Alex Augmented Data Catalog, and Informatica Data Catalog. Open-source tools include Amundsen, Metacat, and Magda.

Metadata management tools: Track information about specific data points (e.g., origin, structure, usage) to ensure transparency, support data lineage, and enhance data usability by providing context and traceability. Paid tools include Dataedo, Adaptive Metadata Manager, and Oracle Enterprise Metadata Management. Open-source tools include OpenMetadata, Apache Atlas, and DataHub.

Data quality tools: Assess, clean, and improve data quality through data validation, deduplication, and standardization. Paid tools include Talend, Ataccama ONE, and SAP Data Services. Open-source tools include Pandera, Deequ, and Great Expectations.

Data lineage tools: See data dependencies and track where it has come from and how it has been transformed. These tools add data auditability for regulatory compliance. Paid tools include MANTA and Octopai. Open-source tools include OpenLineage, Pachyderm, and TrueDat.

Data privacy and compliance tools: For managing data privacy and regulatory requirements. They track data classification, consent, and risk assessment. Paid tools include OneTrust, BigID, and TrustArc. Open-source tools include Fides and Apache Ranger.

Data governance and AI: When dealing with ML and building and operating AI at scale, paid tools that include data governance and management include Domino Data Lab and Databricks.

Popular industry data governance frameworks:

• DAMA-DMBOK (DAMA Data Management Body of Knowledge) is recognized for establishing data governance standards, ensuring consistency, quality, and regulatory compliance while supporting a data-driven culture.
• ISO/IEC 38505 focuses on the goal of helping organizations develop policies, frameworks, and responsibilities for data governance.

Common Challenges for Data Science Teams

With vast amounts and varied types of data, regulatory challenges, and entire organizations of people to onboard and upskill with new processes and tools, data governance in life sciences and biopharma is a challenge that needs commitment, strong leadership, and an organization-wide strategic vision.

Challenge 1: Integrating New Data Governance Tools with Legacy Systems

For specialized, older platforms that need to integrate with modern tools, budget time and resources for extensive customization or middleware solutions, and document them in your governance workflows.

Challenge 2: Talent Acquisition and Retention

Provide clear career paths, professional development, and a sense of purpose to retain highly sought-after data governance professionals.

Challenge 3: Regulatory Compliance and Privacy

Regulations like HIPAA, GDPR, and FDA guidelines vary by region and require tools that track data classification, consent, and risk assessment. 

Challenge 4: Managing Compled Data at Scale

Implement advanced data storage and processing capabilities to handle the volume, velocity, and variety of data, especially unstructured data like imaging or genomic sequences.

Challenge 5: Cultivating a Data-Driven Culture

Employees who view data governance as a compliance burden rather than a valuable asset can be encouraged through continuous engagement, training, and strong leadership advocacy.

Challenge 6: Resistance to Change and Tool Adoption

Ongoing support through hands-on explanations, use cases, and encouragement will help prevent employees viewing new tools as additional overhead or as interfering with established workflows.

Challenge 7: Cross-Functional Collaboration and Accountability

Clear roles, incentives, and regular communication to align goals and reinforce accountability when employees are faced with shared responsibility for data governance and competing priorities.

Challenge 8: Aligning Governance Policies with Real-World Practices

Strike a balance between rigid policies and operational workflows by collaborating with end-users to develop practical policies and workflows, and create feedback loops to adjust policies as needed.

Challenge 9: Security and Access Control

Balancing security with access needs for research, compliance, and operations is solved by software that tracks access and manages permissions.

Challenge 10: Data Quality Monitoring and Validation 

Implement a scalable, automated data quality framework at data collection that monitors and validates all incoming data.

Challenge 11: Time-consuming Process of Updating Outdated Data Formats

Remaining compliant with regulatory and data governance policies, implement a plan for the conversion of legacy datasets into read-optimized formats. 

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Summary

While some elements may be new to you, there’s no need to feel too overwhelmed. It is a one-step-at-a-time process, and you’ll be an expert before you know it. While it can seem complex, good data governance actually streamlines and reduces complexities and boils down to some easily accessible core principles and actions.

For enterprises with diverse data landscapes, there are key technologies that will assist with the most common challenges and pain points related to the integration of new tools with legacy systems and processes. These tools will not only facilitate better data governance and management, but will also assist with accelerating research while speeding up model deployment and enhancing collaboration among data science teams.  

Data governance challenges like integration of legacy systems, talent acquisition, tool integration, and change management require careful planning and patience, and an investment of time and budget. 

The benefits, however, make data governance a valuable investment and include accelerated timelines, regulatory alignment, industry leadership, enhanced credibility, data accuracy, security, and compliance while supporting critical functions like R&D, product development, and the safety of sensitive information. 

Ready to strengthen your data governance approach? Learn more about our work on platform and SCE (Statistical Computing Environments).