Digital Transformation

How to Develop an Effective Data Governance Strategy in the Pharma and Biotech Industry – Part 1

Digital Transformation

May 18, 2023

Share this...
Share on facebook Share on twitter Share on linkedin Youtube

Why Data Governance is Important

More so than with other industries, developing an effective data governance strategy is critical for Pharma and biotech companies. It’s imperative that data is accurate, complete, reusable, and secure.

Without proper governance,

  • Compliance could be at risk with agency regulations.
  • Data quality, accuracy, user access, reuse, and reliability may be jeopardized, and this could impact the organization’s data-driven decisions across drug development, clinical operations, and commercial. Organizations need to ensure that high data quality and non-duplicative data exists throughout the complete R&D lifecycle of the data, and data controls are implemented that support business objectives.

Several Key Steps to a Successful Data Governance Strategy

A successful Data Governance strategy involves change and requires understanding how R&D users define data, access, analyze, and share data along with policies and procedures covering ownership, accountability, and security. With data governance in place, data ceases to be a byproduct of your applications and instead becomes a crucial corporate asset.

Given Astrix’s experience in assisting clients with their data governance challenges, we highlight in this two-part series several of the key elements for a successful data governance strategy and some important considerations as well as key technologies.

There is no one-size-fits-all approach to data governance. Each organization will have their own unique requirements based on their size, organizational structure, products, and other factors. Ensuring a successful program will require experienced internal and external resources. Although each organization is different, there are some key elements that are critical to success, and they are:

  • Define Data Governance goals and objectives: The first step in developing a data governance strategy is to define the goals and objectives of the program. This involves identifying the key stakeholders, defining the scope of the program, and establishing the desired outcomes.

Questions to ask

      • What does the organization hope to achieve with data governance?
      • What are the main drivers for implementing data governance in your organization?
      • Are the goals aligned with the overall business strategy and the needs of the stakeholders.
  • Establish Data Governance policies and procedures: Governance policies enable everyone in the organization to follow the same standards and procedures for access, use, security, standards, and quality. These policies should be aligned with regulatory requirements and industry best practices.

The policies typically cover:

    • Policy purpose: The statement of purpose describes the reason the policy exists and how it supports the company’s objectives.
    • Policy scope: The scope explains who is affected by the data governance policy, organized in a measurable, timely, compliant, and repeatable manner to realize full value of enterprise data assets.
    • Policy rules: This outlines the rules guiding data usage and access, as well as privacy, security, compliance/integrity and integrations. It enables R&D data governance as an integrated part of an enterprise-level Framework.
    • Data governance structure: This entails the roles and responsibilities of individuals and groups, which ranges from the data governance body-council enterprise teams, data owners, data stewards and data users (subject matter experts). It reflects the strong Business and IT involvement, with the ultimate accountability for Governance lying with the business owners. Governance structure should be repeatable and adaptable to other domains and attributes on the basis of sustainability after implementation.
    • Definitions: A glossary of common terminology referenced in the policy i.e., roles, functional areas, data sources, metrics, standards (FAIR), key Data Governance terms, i.e., Master Data.
    • Review process: Describes how the data governance policy is established, reviewed, and updated. As data volumes grow, new data streams emerge, and new access points emerge, you’ll need a policy for periodic reviews of your data governance structure – essentially ‘governance of the data governance process’.
    • Resources: Any related documents, other policies (access, security etc.), training, or regulations (GxP) that are referenced.

Key Factors to Consider in Ensuring a Successful Data Governance Strategy

  •  Define data ownership and accountability: Pharma and biotech companies generate and manage large volumes of data, and it is essential to establish clear ownership and accountability for the data. This involves defining the roles and responsibilities of data stewards, data owners, and data custodians.
  • Establish data quality standards: Data quality is critical for the effectiveness of data governance, and it is essential to establish data quality standards that define the criteria for data accuracy, completeness, and consistency. These standards should be regularly monitored and enforced.
  • Develop a data classification framework: Pharma and biotech companies handle sensitive and confidential data, and it is essential to develop a data classification framework that categorizes data based on its sensitivity and criticality. This framework can help to ensure that the appropriate security and access controls are applied to different types of data.
  • Implement data security and privacy controls: Data security and privacy are critical for Pharma and biotech companies, and it is essential to implement robust security and privacy controls to protect data from unauthorized access, theft, or loss. This includes implementing access controls, encryption, and data masking techniques.
  • Develop a data governance roadmap: It is essential to develop a data governance roadmap that outlines the steps to be taken to implement the data governance strategy. This roadmap should identify the key milestones, timelines, and resource requirements for the program.
  • Establishing cross-functional data governance teams: Data governance is not just the responsibility of IT departments; it requires cross-functional collaboration across various departments within a Pharma company. Establishing cross-functional data governance teams can help ensure that all stakeholders are involved in the data management process.
  • Adopting a risk-based approach to data governance: Pharma companies can adopt a risk-based approach to data governance, where they focus their efforts on managing the data that poses the greatest risk to the organization. This can help them prioritize their resources and ensure that they are focusing their efforts where they are most needed.

Key Technologies that Assist in Building a Successful Data Governance Strategy

  • Implementing advanced data analytics and machine learning: Pharma companies can utilize advanced analytics and machine learning to analyze large volumes of data more effectively. This can help them identify patterns and insights that may be difficult to detect using traditional methods.
  • Leveraging cloud computing: Cloud computing can help Pharma companies store and access data more securely and efficiently. It also allows them to scale their data storage and processing capabilities more easily as their needs evolve.
  • Using blockchain technology: Blockchain technology can help Pharma companies improve the security and transparency of their data management processes. It can provide an immutable record of all data transactions, making it more difficult for unauthorized users to modify or corrupt the data.


It is critically important when building out a data governance strategy and plan to have the right experienced internal staff and external organizations involved to assist in the process. Formulating the appropriate approach along with the proper processes and technology is imperative.

Astrix’s team of professionals in our Strategic Consulting Service Practice have worked with many of the top life science organizations and leaders to assist them with respect to their business needs in these areas. As a technology-agnostic partner, without a preconceived preference for a specific supplier or product, we work closely with your team to ensure solutions are reviewed and incorporated into your business so that you succeed in realizing your vision and achieving your organizational goals.

In this blog we discussed.

  • Several key elements for a successful data governance strategy
  • Other important considerations in building a successful Data Governance Strategy
  • Technology considerations that can assist in building a Data Governance Strategy

Other Related Material

Case Study: Data Governance – Disorder to Alignment on a Common Framework

Essential Components of Data Governance – Part 1

Essential Components of Data Governance – Part 2

DAMA DMBOK and Data Governance

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations.



Contact us today and let’s begin working on a solution for your most complex strategy, technology and staffing challenges.

Web developer Ibiut