Complexity and conext
Level of complexity
Does the data require specialized knowledge or expertise to interpret and analyze? Complex data, such as genomic or proteomic data, can offer a greater differentiation potential, as it requires specialized skills to extract valuable insights.
Complexity and context
Integration and harmonization
Is data integrated and harmonized across different sources and systems? The ability to connect and analyze data from diverse sources can lead to new discoveries and insights.
Contextual relevance
Is the data enriched with relevant metadata and contextual information? Data that is well-contextualized and linked to specific use cases is more valuable for driving actionable insights. For example, AI-automated micro tagging is important for usable multimodal data sets. It helps create usable context for voice recordings, transcripts and or insights locked up in structured reports, such as PDF or slide presentations.
Quality and agile governance
Accuracy and completeness
What’s the effort to ensure the data is accurate, complete and free of errors? High-quality data is essential for building reliable and effective AI models.
Data privacy and security
To what extent is the data managed and protected in compliance with relevant regulations and ethical considerations? Strong data privacy and security practices are essential for maintaining trust and ensuring responsible AI use.
Quality and agile governance
Data governance framework
Does the company have a clear and well-defined data governance framework in place? A robust, yet agile governance framework ensures data consistency, accessibility and accountability across the organization.
Uniqueness
Proprietary versus publicly available
Is the data unique to the company, or is it readily available to competitors? Proprietary data, such as clinical trial data or patient-reported outcomes, generally carries a higher differentiation potential.
Exclusivity of access
Does the company have exclusive access to the data, or is it shared with partners or competitors? Exclusive access enhances data differentiation.
Uniqueness
Novelty and rarity
Is the data capturing new and unique insights, or is it replicating existing information? Novel and rare data sets hold a greater potential for differentiation. This could be, for example, omics data used in discovery and development.
Volume and variety
Size and scale
Is the data set large and comprehensive enough to provide meaningful insights? Larger and more diverse data sets offer greater opportunities for AI model training and analysis.
Data types
Does the data set include a variety of data types, formats or modalities? The ability to leverage diverse data types can enhance the power and accuracy of AI models.
Data velocity
Is the data generated and updated in real-time or near real time? Real-time data streams can provide a competitive advantage by enabling faster decision-making and responsiveness to market changes.
Volume and
variety