Informatics
• Domain expertise
• Designing elegant experiments Science • AI algorithms Technology
• Data strategy
• Data/knowledge engineering • Data management • Data cleansing
• Data visualisation
• Machine intelligence • Machine learning • Statistical analysis
• Ontology/taxonomy management • Data lakes
• Robotic process automation
Math
Understanding the value propostion Cross-disciplinary thinking Innovation model
Experimental mindset Change management and communication
approach and “rapid short experiments where ‘good failures’ are celebrated”12.
Data The recent boom in Big Data, and the development of the management principles to exploit Big Data, is one of the driving forces for the re-emergence of AI. Without Big Data (preferably of high quality and lots of it), AI would be starved of the raw material upon which it depends. Four key areas of focus for biopharma successfully to adopt AI capa- bilities were highlighted, viz: (i) Data Strategy, (ii) Data Governance, (iii) Knowledge Representation and (iv) Data Stewardship. (i) Data Strategy focuses on establishing the over- arching strategy for how a company wants to cre- ate, manage and use its data assets. A Data Strategy provides a roadmap for a company to advance its data capabilities, and includes address- ing key questions such as: l What is the data that will generate competitive advantage, that we should maintain for the enter- prise over time? l What data do we need that is external to our company? lWhat data do we need to generate to be compet- itive?
Drug Discovery World Spring 2018
(ii) Data Governance is comprised of the overall management of the FAIR data principles (findabil- ity, availability, integrity, reusability) along with the security and confidentiality of the data used in an enterprise. A good Data Governance approach defines: l Who is the responsible owner for data. l What quality standards should be applied to data. l How data will be managed to those standards going forward. Data privacy regulations (eg the European Union GDPR) are an important
topic.
Organisations need to ensure that they were knowledgeable about the regulations and were in a position to be in compliance when the GDPR came into force in May 2018.
(iii) Knowledge representation focuses on: l How we store and represent data. l How we structure data for usability.
(iv) Data Stewardship focuses on the skilled resources required to put a Data Strategy and Data Governance into practice. It addresses the challenge of how to resource and operate on a daily basis the curation and maintenance of an
11
Figure 1 Skillsets and foundational capabilities required for AI adoption
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