From Barrier to Enabler: Data Governance Paves the Way for Advancements in Life Sciences

Learn how data governance is enabling breakthrough discoveries using generative AI

We are now living in a world where what was once considered impossible is possible. A world where new targets and compounds can be rapidly identified through sophisticated algorithms, drastically accelerating the drug development pipeline. Where new indications for existing drugs are identified along with a prediction of their safety, toxicity, and efficiency. A world where one can mine vast stores of scientific research and multi-model data to identify new biomarkers of disease. A world where costly and time-consuming animal models are replaced by organ-on-a-chip technology (or system-on-chip technology!) paired with predictive modeling. These breakthrough discoveries, organ-on-a-chip technology, machine-generated data, and decentralized clinical trials are now possible when enabled by cohesive and scalable research and development (R&D) data strategies and generative AI (GenAI) solutions. However, to unlock these promising outcomes, a solid data governance foundation is required.

The emerging role of data governance

How do we ensure the data-building intelligence solutions to improve patient outcomes are accurate, available, compliant, and interoperable? Modern data governance provides this foundation. The impact of data governance is that it transforms data in a product or asset — enabling reuse. More and more life sciences organizations understand this and are doubling down on the development of enterprise data offices and data value offices to provide a roadmap, tools, and controls for their matrixed organizations to utilize data as a strategic asset. Hence, a major focus of these data offices is the creation of a comprehensive data strategy and federated data governance frameworks.

How did data governance go from a niche activity buried deep in siloed data offices, project management functions, and IT units without much lift-off to a hotly desired enterprise capability worthy of significant investment? This can be quickly understood by landscaping the current business needs of life sciences organizations and the pivotal role that data plays in getting to market first. It is through this lens that data governance gets a major PR boost — going from no-to-low visibility at best to a bureaucratic nightmare at worst to a key driver of efficiency and innovation.

Data governance solutions to support life sciences’ biggest business challenges

When 30% of the world’s data is generated from the healthcare and life sciences sector (and a projected 36% by 2025), and of that data, a startling 97% goes unutilized, it is no wonder that the management of data assets has risen to a key strategic focus for life sciences organizations. Every organization needs to manage its data. Data governance ensures it is managed well.

Underutilized data is a material business problem; data governance is the antidote.

Aside from these startling statistics, what are the business trends influencing this golden era of governance?

GenAI

Influenced by technological advances and pandemic times, there have been sweeping changes to the life sciences value chain — most acutely seen in the R&D space. How do we identify new off-label use of drugs? Can we be more effective at targeting recruitment of eligible patients? How do we identify the right drug for the right patient? Can we anticipate supply chain interruptions so we can mitigate impact? These questions can be addressed through the effective use of quality data being fed to GenAI models, which enable us to answer these questions faster than ever before. GenAI can bring sweeping changes to the value chain by giving businesses and scientists the ability to expand the art of possible and ask questions that seemingly felt out of reach to pursue. GenAI relies on the availability of vast amounts of findable, high-quality data in a plethora of forms and sources: preclinical labs, machine-generated data, clinical trials, electronic medical records (EMR), multiomics, wearables, real-world experience (RWE) — the list goes on. Data governance bridges the gap between real-world use cases and machines. Without this bridge, you cannot create repeatable, scalable processes, wasting precious time and resources creating one-off processes while very rarely leveraging the right amount of available data assets.

Privacy

The ever-changing global privacy law landscape is another arena where data governance solves real-world problems for life sciences organizations. How does one ensure data is captured, maintained, used, shared, and destroyed in accordance with regional and country-level privacy laws? These conversations should be happening at the strategic level in enterprise data governance councils with privacy officer leadership in strong partnership with CDOs, CDAOs, CISOs, EA, and other governance leadership. The tactical layer of data governance organizations then decides on the right architectural models to support compliance (centralized vs. democratized) and provides direction for the operational layer of governance on the appropriate tagging and data rules that can be implemented in and across the data management functions. In this way, governance provides the process arm for organizations to comply with privacy law, leveraging both the decision-making structure of data governance operational models as well as the roles, processes, and tools of modern data governance.

Data democratization

There is another real-world trend in the R&D space that can only be accomplished through the implementation of successful data governance. At the top of the priority list for many CDOs/CDAOs is executing on the business desire for democratized access to data. As previously described, life sciences business leaders understand the value that data can offer to build the competitive advantage for discovery and innovation. But how do we go from siloed data stores, shadow IT teams, inconsistent tracking of patient consent, poor-to-no understanding of data ownership and access criterion, and data hoarding to the utopian world where the right people have access to the right data at the right time? Data governance is the only practice that provides the tools and framework to systematically address and document data assets, data ownership, access criteria, tagging, and process design to get to a scalable attribute model for access provisioning, in other words, democratized access to data.

FAIR

A closely related top priority for life sciences CDOs/CDAOs is the implementation of the FAIR principles, which aim to make data findable, accessible, interoperable, and reusable. You will not find a life sciences organization today that is not clambering to realize these principles within its data management functions. Like privacy, FAIR data principles are ideals that life sciences organizations want to achieve that can be realized through the implementation of modern metadata management, a primary function of data governance.

The path to success for data governance

If data governance is the path to unlocking innovation and providing competitive advantage, how do we get there? The saying, “If this were easy, everyone would be doing it,” comes to mind. While the path to successful modern data governance might not be as easy as turning on a light switch or buying the fancy new tool (although tools are needed — more on that later), it’s not impossible and is worth the effort and investment to do it right. If you are not spending time on building data governance within your organization, you are most definitely spending time recreating the wheel on every initiative, project, and study that relies on data.

Data governance, if done well, plays offense rather than defense for organizations and can streamline an effective utilization of data through clearly defined processes.

From experience across sectors and industries, and specifically life sciences organizations, there is a path to successful data governance for those willing to take the journey.​

1. Make committed business engagement a priority. The success of any data governance initiative depends heavily on co-creating the capability with active business involvement and accountability across the full lifecycle of its enablement.​ Focus first on solving the data side of a key business initiative and your data governance program will be seen as an enabler rather than a barrier to getting things done.
2. Start small, adapt, and scale fast. No innovative products have been built without lots of experimentation and design iteration. Data governance initiatives have a target North Star, but people and processes adapt along the way to get there.​ Don’t fear failure. It will come. What matters is that you adjust and move forward with that learning.
3. Avoid technology mismatches. Not all technologies, even the most modern ones, co-exist smoothly. Understand the limitations of the options and adapt where needed. Some decisions can prove expensive without intelligent vetting and due diligence. Build your processes first, and then choose the tools that will scale your processes. The right tools are critically important, but you must do the work to understand your desired capabilities and processes first. Tools cannot implement a foggy vision. And a human-centered design approach is key to data governance tooling success.
4. Be fiercely human. Focus on the impact on people. Illuminate the impacts that your teams drive for their customers by delivering initiatives tied to key impact drivers. Highlight the potential to engage in meaningful work to recruit and retain the right talent.​ Change is scary. And data governance really shakes up how people view their role in data management and ownership. Be kind. Be consistent. Educate. Provide training. Acknowledge and formalize people’s existing roles in data leadership where you can. And those folks having a hard time? Challenge yourself to bring them close to the work and co-create with you. It will pay dividends.
5. Accept that there is no one-size-fits-all governance model. Data governance starts with a framework; its implementation comes in a variety of combinations and permutations reflecting unique aspects of your leadership, culture, and technical landscape. Focus on the business priorities, then build a data strategy that gets you there with data governance as a key pillar.

Every journey starts with a single step. Connect with us to learn more about our approach, tools, and accelerators to unlock the value that modern data governance can create for your organization.

Amber Sexton, Life Sciences Leader of Slalom Global Technology, has spent over 15 years creating scalable, interoperable solutions to support advancements in medical research. She’s a frequent thought leader in data strategy, data governance, and optimizing clinical trial performance. Reach her at amber.sexton@slalom.com.