December, 20208 IN MY V EWArtificial Intelligence (AI) is one of the hottest topics today. This is a good thing as AI has the ability to drive tremendous value if applied appropriately. It is not just companies that are taking advantage of AI. Many countries have published National AI Strategy. In May 2019, forty-two OECD and partner countries formally adopted the first set of intergovernmental policy guidelines on AI. However, this attention has also generated so much hype that makes it difficult to separate what is real from what is wishful thinking. In February 2019, O'Reilly published the results from their survey on AI adoption in the enterprise. "Lack of data" and "lack of skilled people" remain key factors that slow down AI adoption within many organisations. Two other common obstacles pertain to organisational challenges: 23% cited "company culture" and 17% cited "difficulties identifying use cases."There are many fundamentals that need to be in place to maximise the value of AI. The fundamentals can be organised into four strategic thrusts: (1) data access, (2) data product, (3) data education and (4) data collaboration. Lack of access to data is a key barrier to the adoption of AI in most organisations. The needs of different groups of data consumers differ. For example, basic users may not even want to see the data points, but instead prefer to see charts and infographics. Intermediate users may want to analyse derived data e.g. length of stay in hospital, whereas advanced users may prefer to analyse raw data e.g. date of hospital admission and discharge. It is thus important to structure the data based on how the data consumers are using it. Structuring the data correctly is necessary but not sufficient. The data has to be discoverable. The common complaint from data consumers is that they are not aware of what data is available. There is often little information about the datasets. It is therefore important to develop data catalogue and make it available, similar to how Amazon provides information on the products they sell. Data consumers need the right tools to make sense of the data that they have access to. Most organisations do not give employees the privilege needed to install software and such a practice creates friction to data consumers who usually require specialised tools. Furthermore, open source analytics tools e.g. R, Python, require the download of up-to-date packages on an ongoing basis. Some data consumers work around the challenge by downloading the data onto their personal devices and this in turn introduces data security risks. The data analysis process can be codified end-to-end, thereby allowing it to be shared and reused. There are tools that enable data consumers to work collaboratively and share codes so that institutional knowledge can be democratised. SETTING THE FOUNDATION FOR ARTIFICIAL INTELLIGENCEBY SUTOWO WONG, DIRECTOR, ANALYTICS & INFORMATION MANAGEMENT DIVISION, MINISTRY OF HEALTH ­ SINGAPORESutowo Wong
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