A practical guide to getting data “AI-ready” [Whitepaper]

Enablement of effective AI – A practical guide to getting data “AI-ready” [Whitepaper]

Overview

Unlock the potential of Artificial Intelligence (AI) in the life sciences industry with our comprehensive guide to achieving AI-ready data. Enterprise-wide structured and FAIR (Findable, Accessible, Interoperable, and Reusable) data is essential for maximizing the value of data assets within life sciences organizations. Our whitepaper outlines the benefits of successful FAIR implementation and provides a detailed roadmap for preparing your data for AI.

Introduction

AI has the potential to revolutionize drug discovery, creating a $50 billion industry over the next decade. However, poor data quality remains a significant obstacle. This guide emphasizes the importance of high-quality data and the adoption of FAIR principles to ensure accurate and reliable AI outputs.

The importance of FAIR data

The FAIR principles are designed to improve data quality and facilitate better decision-making. Despite the challenges of achieving fully FAIR data, even incremental improvements can offer substantial benefits. Our guide explores the practical steps needed to implement FAIR data principles, making your data “FAIRer” or “FAIR enough” for AI applications.

Practical steps to AI readiness

Define and communicate the vision: Establish a clear, long-term vision with tangible objectives. Secure senior leadership support and communicate the benefits of FAIR data to the entire organization
Develop a robust business case: Focus on the “why” rather than the “what” by demonstrating the tangible business benefits of improved data management practices
Define and communicate a roadmap for FAIR: Adopt an iterative, agile approach with well-defined “quick wins” to maintain momentum and demonstrate success
Define, document, and prioritize use cases: Clearly define the questions to be answered and prioritize use cases based on their value to the business
Put together the team: Assemble a dedicated core team with the necessary expertise and capacity to support the FAIR initiative
Establish governance processes: Implement a governance structure to manage data standards, including change control processes
Implement technology to manage standards: Utilize dedicated systems to manage data standards and ontologies, ensuring a single point of truth for the organization
Communicate progress & maintain momentum: Regularly communicate project progress and key achievements to keep the organization engaged and informed

Conclusion

AI and GenAI hold immense potential for transforming the pharmaceutical industry, but their success hinges on high-quality data. Our guide provides a robust framework for achieving AI-ready data through the adoption of FAIR principles. By following these practical steps, organizations can unlock the full potential of AI applications and drive innovation in the life sciences.

Take the first step towards revolutionizing your data management practices and unlocking AI’s full potential in your organization. Download the full whitepaper and contact one of our experts today to learn more.

Download Whitepaper

Share this article