Data Strategy
Ever since the term CDO became popular, there have been plenty of discussions about data and how it is an invaluable asset for organizations seeking to gain competitive edge.
The rise of artificial intelligence technologies in the past year and a half, such as large language models (LLMs), knowledge graphs, and generative AI (GenAI), has unlocked unprecedented opportunities for leveraging data to drive innovation, enhance decision-making, and create value. However, realizing the full potential of these cutting-edge technologies necessitates a strategic shift in your current data strategy.
To harness the true power or both your data, and the new technologies, organizations must re-prioritize the development of their data strategies as a core component of their overall business strategy.
From an IT perspective, the data strategy must address these key elements, in order for your business data strategy to succeed.
- Data Interoperability: Facilitating seamless data flows and interoperability across various systems, platforms, and data sources is essential for enabling AI technologies to access and process relevant data effectively.
- Data Infrastructure: Building a scalable and flexible data architecture and infrastructure capable of handling the massive volumes and diverse formats of data required by LLMs, knowledge graphs, and GenAI is a necessity.
- Data Readiness : Implement processes and tools for preparing data ready to be consumed by LLMs, and GenAI. Curation plays a crucial role to ensure that the data consumed by AI technologies is clean, structured, and formatted appropriately for optimal performance.
- Knowledge Representation and Management: Developing strategies for representing and managing organizational knowledge in a structured and machine-readable format, such as knowledge graphs, can greatly enhance the capabilities of LLMs and GenAI systems.
- Ethical and Responsible AI: Incorporating ethical principles and responsible AI practices into the data strategy is essential to mitigate potential risks and ensure the fair, transparent, and accountable use of AI technologies.
This article is the beginning of a new series, where I will present some ideas and use cases suitable for the Pharmaceutical industry.
