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A.11 OCBC 'Big Data Rockstars' prize

It was a great opportunity and a good experience working with the data from The Open Vault at OCBC. However the team quickly realised that it was not going to be straightforward. Writing code to automate the simple data extraction process using the Open Vault APIs was the first obstacle that the team has to overcome quickly. The team had to learn enough software programming within a day to get the data out of the vault as quickly as possible. No data, no project!

Being constrained by the availability, quality, and quantity of the data available was the next key challenge that the team has to overcome. In the class, the objectives of assignments were well-contained and the data provided were assumed sufficient. This project has opened the team’s eyes to reality where real-world data do not come in nice packages that is all ready to provide the answers.

Another challenge for the project was developing the objectives and scope. This required the team to understand the characteristics of the available data in order to determine what kind of business questions could be addressed. The team also decided that to extract real value out of the given data, it was important to link it to external data sources that were related to industry-wide challenges, or social and national issues. Next, although some of the methods, models and algorithms introduced in A11 were already familiar, the assignments, lab exercises and discussions in class gave the team more inspirations on how these methods could be applied to solve business problems in financial institutions. This problem-driven approach has helped the team nail down on the project objectives and scope quickly.

The team also felt the deep passion in data analytics whenever different perspectives on the approach to tackle the same business problem were battled over sometimes very heated arguments. The coursework in A11 supplied various analytical methods, techniques and tools to use. However, being inexperienced and the lack the business domain knowledge, it was through some sparks of ingenuity, made possible through the team's collective effort, that the innovative pieces were put together into a coherent analytical process to achieve the objectives of the project.

Finally, the key takeaway from this project was appreciating that data analytics was ultimately about uncovering the stories hidden in the data, and presenting them to address to the business challenges and opportunities. It has been a tremendously fun and fulfilling experience for the team.

What is A.11 Big Data Analytics in Financial Services?

Big Data Analytics in Financial Services is primarily for the students who are keen to discover and hone their analytics skills in the context of financial services sector. The course promises to be practical, interactive and hands-on utilizing tools from the SAS Analytical suite to Hadoop, to help students learn various types of analytics techniques in the financial services context. The course is powered by OCBC Bank. The course has been co-designed with our industry partner, so the course is updated with the current industry perspective. The students have the opportunities to work with real-world data for the project and will involve evaluators from OCBC Bank’s Group Customer Analytics and Decisioning team, giving students the opportunity to demonstrate their key learnings in the team’s presence.

Last updated on 17 Oct 2018 .