The case is set in June 2014, when Lau Wing Chew, Chief Transformation Officer (CTO) at Alexandra Health System (AHS) in Singapore was highlighting the achievements of the data analytics initiative made by the Health Analytics Unit (HAU) at AHS to his management team. Although the initiative was still in its initial stage, multiple medical and administrative areas, such as the operation theatre dashboard, ageing-in-place and population health initiatives targeting patients' health expenditures and outcomes had been improved with the help of data analytics. The journey to develop the data analytics initiative had commenced in 2011, when Lau reviewed a set of data presented by the Accident & Emergency (A&E) Department, and realised that non-emergency cases at A&E were taking up too much of hospital resources. He also noted other pressing resource-related issues, such as acute bed shortages and patients not showing up for their appointments at specialist outpatient clinics, causing a sub-optimal utilisation of critical resources like doctors and appointment rooms. Lau knew that given the right metrics; predictive analytics could improve the situation. In the following three years, the HAU had leveraged the capabilities of data analytics to roll out initiatives such as population health and ageing-in-place programmes and implementing solutions into patient medical billing and operating theatre dashboards. Although these unremitting efforts were showing results, the challenges ahead were significant. The HAU team not only needed to get buy-in at an operational level with different business units, but also needed to increase health awareness in patients. How could AHS improve the data analytics initiative further and plan its resources optimally

1. Considering the five stages of analytical progress proposed by Davenport et al (2010), evaluate the current status of AHS. 

2. Discuss the critical factors for an analytics project? How can an organisation implement analytics successfully? And what are the challenges that AHS faces? 

3. What are the benefits of analytical decision making in the AHS case study? 

4. For each analytics project described in the case discuss: 

a. The data sources required, 

b. What analytics methods/techniques would you recommend solving the problem? 

c. What are the performance measures or indicators for the analytics projects? 

d. How do you measure the success of the project? 

5. What are the suggestions that you would give to AHS to move to the next stage of its analytics' journey?