What Makes a Clinical Data Strategy Successful?
Contributed Commentary by Raj Indupuri
January 3, 2020 | The life sciences industry is rapidly evolving to adopt emerging clinical research trends and technology. There is a growing need for precise and reliable data to deliver more targeted and cost-effective medicines to patients. As the complexity of trials is increasing, the number of data sources are multiplying, and data are exploding at an unprecedented rate. The industry faces the challenge of effectively managing this data deluge and the resulting chaos to create meaningful data assets and extract valuable insights for data-driven decisions.
The industry must make a preemptive shift and transform the management of clinical research data with a forward-looking end-to-end data strategy to address these challenges.
Strategic Thinking to Drive Key Enablers
Transformations create business disruptions, but it is a strategic priority for companies to evolve and adapt to the increasing levels of demand and complexities the industry faces. As the need for clinical trials to become digital is increasing, there is lagging inertia to overcome barriers of change and integrate newer technologies for effective data management. Organizations must also break down functional and transformational silos across people, process, and technology to enable outcome-based results. Data is the core driver for digital transformation, so a well thought-out data strategy and vision become inherent priorities to align with the organization’s transformation efforts and digitization goals.
Overcoming Barriers to Transformation
Driving transformation and creating an effective data strategy require a holistic end-to-end approach with cross-functional alignment across the organization. Real transformative change requires effective risk mitigation, reliable support, and sponsorship from top management. Also, companies must close talent-people gaps with a focus on reskilling or hiring to support these new initiatives in an ever-changing technological environment.
There is a need to invest in modern practices, along with a solid technology foundation. New processes need to be established to support these strategies and take advantage of new data technologies to help make the organizational shift while balancing and streamlining legacy processes. Last but not least, there should be great emphasis on data governance practices to enforce standards, policies, and procedures, and to secure the environment for governed access every step of the way.
Implementing a Successful Data Strategy
With a clear understanding of the organization’s strategic priorities for data and transformation, focus on creating immediate value becomes the first step. It is crucial to identify the outcomes needed by the business. With those in mind, develop a vision and an initial approach to test the effectiveness of the methodology considered. With success, continue to iterate and scale across the organization while learning and adapting quickly in the journey. Here are some essential considerations for the effective implementation of a clinical data strategy:
1. Align goals and benefits
Develop a deep understanding of the strategic priorities of the organization and the core business drivers. Assess business benefits, risks, and trade-offs to prioritize and realize those goals and objectives by leveraging data. Involve key users and stakeholders from the start of the process to ensure alignment. Map and create data strategy goals in alignment with the established business objectives. Bearing in mind the highly regulated nature of the life sciences industry, the focus is always to minimize risk and ensure regulatory compliance. So, data security and governance need to be incorporated every step of the way.
Build a plan and a cohesive data roadmap with short-, mid-, and long-term goals prioritized based on business needs that remain flexible as the company transforms and the industry evolves and changes.
2. Map use cases and data needs
For each of these goals, identify high business value use cases prioritized based on criticality, viability, level of innovation, and ease of implementation. Next, assess the data sources and data flow requirements in support of those use cases. Consider source, format, structure, mapping requirements, and user roles with opportunities to publish as data becomes available.
Create end-to-end automated workflows that begin with acquisition and cleaning to ensure high-quality data is available for analysis and actionable decision-making with an ability to archive at the end. Consider questions such as who the data consumers are, the collaboration requirements, and the value derived from each step.
3. Define analytics strategy
Based on the goals and data flows, review each use case to determine driving factors in the decision-making process. Consider adoption readiness and what supplemental information and underlying technical infrastructure are needed to support the desired outcomes. Drive adoption with rich interactive visualizations and easy to use data-driven applications.
Define metrics to measure efficacy and track progress towards the business goals outlined, along with value delivered and efficiencies gained. Cutting through data chaos may seem overwhelming, but there is light at the end of every tunnel.
Transformation is never over. It is essential to take on a future-forward approach and adopt strategies that evolve continuously with technological advancements and ever-changing expectations from patients. Change is the only constant.
Raj Indupuri is the CEO of eClinical Solutions and is passionate about introducing and implementing solutions and technologies to further clinical research development. With a unique blend of hands-on technical and data management experience, Raj works to advance the eClinical Solutions strategic vision and the delivery of cloud-based solutions. You can contact him directly at rIndupuri@eclinicalsol.com.