Data Governance in the Pharmaceutical Industry

In the pharmaceutical industry, where data drives innovation and ensures regulatory compliance, sound data governance practices are critical. This is because data is at the heart of the most recent innovative breakthroughs, like Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS). Without a high-quality, well understood dataset, the use cases, machine learnings and data science-variables cannot be expected to generate optimal outputs. Hence, organizations are making significant investments in data clean-up, management and governance across the lifecycle of each data asset. This in turn sets the stage for the democratization of data, with proper security and controls in place. Furthermore, for pharmaceutical companies, a proper governance framework ensures that data remains a reliable asset for drug development, patient safety, and regulatory adherence.

Implementing a data governance framework involves integrating tools and practices like master data governance to streamline data sources and ensure consistency. Leading organizations are now leveraging data governance tools to automate processes, reducing the manual effort and error rates, and enabling a faster time-to-value for data-driven initiatives. This is especially important in a heavily regulated industry, where non-compliance risks can be mitigated through proactive data tracking, classification, and management strategies. Thus, with the right approach to data governance, pharmaceutical companies who are early adopters of the concept, can turn well curated and managed data into a strategic advantage.

Joseph Humm

Commercial Director and Program Manager,

Consulting & Nets

In our latest blog, we spoke to our Commercial Director & Program Manager, Consulting & Nets, Joseph Humm. Read on for the full interview.

What is data governance?

Data governance is a comprehensive approach to managing an organization's data assets. It involves establishing processes, policies, roles, metrics, and standards to ensure data is collected, processed, stored, and used securely and efficiently throughout its lifecycle.

Data Governance Vs Data Management

How is data governance different from data management?

Data governance establishes policies, procedures, and standards for data usage with an objective of ensuring data is managed as a critical asset, maintaining its quality, security, and compliance. Whereas data management implements the policies and procedures set by data governance with an objective of managing the entire data lifecycle, from creation to deletion, ensuring data is available and usable.

Benefits of Data Governance

What are the benefits of data governance in the pharmaceutical industry?

By establishing a robust set of policies, procedures and data usage standards, data governance ensures quality data can be scaled across an organization for use in data science (identifying and predicting the efficacy of new drugs), artificial intelligence (optimizing customer experience, supply chain planning, sales planning/forecasting), operational efficiencies (using data and trusted KPIs to properly manage all business operations).

Challenges of Data Governance

What are some of the challenges in data governance?

In building a Data Governance discipline, organizations can present a number of challenges, which include:

• Developing a strategy based on business partitions/hubs. For example, organizations may want to create data hubs based on “like” data sets (i.e., Sales-Marketing-Finance, Manufacturing-Supply Chain-Quality, and Clinical- R&D-Regulatory). The challenge is then how should an organization establish a data governance policy to account for the nuanced difference of each data asset? Is it appropriate to establish a capability per hub, or a centralized function that can scale and adapt to each business function? This is particularly pertinent for the Life Sciences industry, where Clinical-R&D-Regulatory data is heavily scrutinized by regulators and Sales-Marketing-Finance, Manufacturing-Supply Chain-Quality data has no regulatory restrictions.
• Identifying the type of governing body an organization should establish to ensure data and next generation technology, which is consuming the data, is properly managed. Is Data Governance a developing space within the business, whereby it is appropriate to apply a focus on the creation of body that oversees Data Stewards and Data Owners distributed across the business? Or is the organization well-established in its Data Governance practices and thus the business is ready for an Artificial Intelligence Governing body, which includes representation from Ethics, Privacy, Legal, IT, Business and Data Governance?
• Discovering the data that is to be to curated, cleansed, aligned (lineage) and managed. In other words, it’s important for the Data Governance team to establish strong relationships with its business partners to identify and prioritize the data assets that are to be catalogued and curated via the established Data Governance policies and procedures.
• Scaling policies and procedures that will be readily adopted by the organization can also present a challenge. For example, businesses tend to focus on data quality and data management when a project arises but fail to develop a culture of Data Governance/Quality. And when a Governance mindset is embedded within the actions of all employees, data is continually curated and developed into a strategic asset that can quickly translate into successful data science projects, artificial intelligence solutions and efficient operations.

Data Governance Best Practices

Please provide some data governance best practices

Data Governance Best Practices are denoted below:

• Pinpoint the business problem/use case you are looking to optimize with data governance first
• Identify and prioritize the data assets that are of the most value to the business problem/use case you are seeking to enable
• Establish the Data Governance governing body, policies, procedures, data stewards and data owners, ensuring they are properly deployed in addressing the use case or defined business problem

Utilizing Market Leading Technical Solutions

As denoted above, Navitas’ experience suggests that organizations are creating natural data partitions, aligning datasets based on capability and function. For example, on the commercial side of the business, organizations may want to align their sales, marketing and financial data. Conversely, in the drug development and patient management area of the business, Navitas sees organizations aligning the R&D, Regulatory Affairs, CMC, Pre-Clinical, Quality, Supply Chain, Clinical, PV and Medical Affairs datasets. The data governance elements, such as defining data policies, cataloging data, defining the lineage, classifying and cleansing the data is often managed by marketing leading solutions, such as Informatica, Collibra or Atlan. Furthermore, this capability will often underpin a Data Lake/Warehouse and will feed into a Business Intelligence capability or Data Science function. All of which moves the data from a cleansing state to a business value creation/insight development state.

Navitas Life Sciences’ Services

What makes Navitas Life Sciences a strategic partner for Data Governance?

Navitas Life Sciences is the ideal partner to help organizations with the development of their Data Governance strategy and implementation for the following reasons:

• Navitas has extensive experience in Data Governance/Privacy, strategy development and the Life Sciences industry. Thus, we can help develop strategies and solutions that account for the nuances of the governing data in a regulated environment.
• Navitas has industry relationships, products and services that can ensure the development of any Data Governance strategy is successfully deployed into the operating environment.
• Navitas has expert functional team members with industry, data governance, technology and other experiences that can be utilized to address complex questions or problems that may be unique to your business. (i.e., our expertise can ensure your Data Governance solutions uses industry best practices while also being tailored to your business).

Data Governance Solutions- The Navitas Life Sciences Edge

Creating a Data governance construct can be a complex endeavor. As with most business problems, organizations can utilize the implementation of a software solution as the catalyst for building the governance foundation. However, in our experience, simply deploying an electronic solution without first organizing the business around a strategy could very well deliver mixed results. Hence, Navitas recommends the following steps:

• Maturity Model Assessment: Assess the organization’s data governance maturity, determining where each component sits on the curve defined by Navitas
• Strategy Development: Develop a strategy moving the organizations’ maturity model from the current state to the desired state
• Data Governance Framework & Operating Model: Define the Data Governance Framework and Operating Model that absorb the solution developed
• Technology Evaluation: Evaluate the utilization of market leading technologies or a home-grown solution approach for managing your organization’s governance of data
• Data Quality: Discover, assess, cleanse and catalog the data that will be managed by the solution of choice

The volume of data generated is only going to increase over time and the utilization of innovative technologies, fueled by data, will no longer be differentiators, but rather table stakes. Hence, the pharma industry’s recognition of a significant Data Governance need and investment opportunity, for various aspects of the business. By investing in the right data governance framework, pharmaceutical companies not only future-proof their operations but also position themselves as leaders in a data-driven world. The journey to effective data governance starts with a clear strategy and the right tools, setting the stage for safeguarding data integrity, driving operational excellence and ensuring regulatory compliance.

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