Informatics
organisation’s data and the continuous improve- ment of data quality and value.
Intellectual property and data pose new chal-
lenges in the context of AI. As an industry, the fol- lowing questions arise: (i) When are we able to share pre-competitively? (ii) When are we willing to share pre-competitively? (iii) When does sharing put intellectual property at risk? The biopharma- ceutical industry has, in general, considered knowl- edge of software and technology to be pre-compet- itive and sharable, while data and information about specific compounds, and the specific pro- cesses by which they are developed into drugs, is considered proprietary and not sharable. The ques- tion is: with an algorithm trained on proprietary data sets, would biopharma companies be willing to share the algorithm independently of the data? For many companies, the answer may well be ‘no’. If the data cannot be shared, then the algorithm trained on that data cannot be shared. However, the untrained algorithm could potentially be con- sidered sharable. AI thrives on training data sets. The richer and
more diverse the training data set, the richer and more diverse can be the interpolation from the AI machine. Cross-industry opportunities to share data can create richer training data sets and allow AI algorithms to function better. The IMI e-TOX Project13 provides one example of such a pre-com- petitive, collaborative, data-sharing initiative.
Figure 2 Centralised versus
decentralised skills in a hybrid model
Responsibility
Technology Platforms
Data Stewardship
Data Standards Information Architecture
Data Analysis Data Reporting
IT centralised R&D embedded
✓ ✓ ✓
Organisation With the adoption of cloud-based technologies, the role of the IT organisation in the enterprise is evolving rapidly. The boundary between IT and other functions is changing as IT becomes a facili- tator and more deeply embedded within business functions. This changing landscape comes even more sharply into focus as new skills and so-called ‘double deep’ resources (ie personnel knowledge- able in both business functions and the technology) are required to leverage data and the underlying technologies of AI. There is no simple answer to the question of
whether an enterprise should adopt a centralised or a decentralised approach, eg a single Chief Data Officer and Centers of Excellence versus a decen- tralised approach with data scientists embedded in the functions. Success depends instead on many factors ranging from company size and culture, to process, technology and data maturity. Companies vary in the way they support tech-
nology innovation such as AI. Regardless of model, centralised or decentralised, successful AI technol- ogy implementation requires a focus on cross-func- tional partnership and teams. While the lines of responsibilities may be blurred, there are primary responsibilities that are clear and a hybrid model with platforms and centralised skillsets serving embedded domain experts is illustrated in Figure 2. Platforms and data standards should be main- tained at an enterprise level by IT organisations
✓ ✓ ✓
12
✓ ✓
Drug Discovery World Spring 2018
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