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0621 CDAO FSI AsiaOnline Logo-1

9.30am - 1.00pm SGT

Dr Rita Chakravarti, Graduate Program Chief, MTech EBAC, Institute of Systems Science, National University of Singapore

Rita Chakravarti

1. Tell us a bit about yourself...

I have practised Analytics all my career ( PhD in Multivariate Analysis). Career started as a Professor in US. Later moved to the corporate world. Served as Asia Pacific Head for Analytics in companies like Citibank, FICO, Experian, IBM etc. Had a calling for teaching and hence decided to move to University and now head the program for data science ( Enterprise Business Analytics).

2. What is the biggest challenge you face within your role today and how are you looking to tackle it?

Technology is changing very fast. So the curriculum needs to be upgraded much more frequently to remain relevant. How to tackle: Constant research and feedback from industry

3. What has been your biggest success for 2020 so far?

In spite of COVID there is no shortage of take up by executive education & MTech students even though we had to go online. When I joined NUS-ISS in 2016 there were only 12 Part-Time (primarily local) students whereas this year ( starting Jan 2021) we have 90+ students. In addition in 2020, we ran multiple ( much higher than previous years) runs of Executive training on various topics of Data Science.

Trust, privacy

4. Based on what you’re seeing what do you think the big trust issues are that will create barriers or opportunities?

As more and more companies are going to rely on data-driven decision making, storage, retrieval, fast processing and security of data will have big trust issues. Many companies may not have agile, nimble and secure infrastructure to handle this.  

 

5. What is required to provide reassurance/ trust that government-held data is being managed and used responsibly and safely?

Sophisticated & secure data storage & governance

 

6. How do you address the ethical considerations and customers concerns regarding the secondary use of data? ( the example being the use of my health record for pharmaceutical companies) 

If contact information and name etc. are not used, no way it is possible to have the breach of privacy. So the data ( anonymized) can be safely used for pattern recognition and the findings can be used for the betterment of treatment of the human race. Similar comments for other domain.

 

Real-time, collaboration, open data

7. What questions are you able to answer with real-time data that you were not able to answer before?

Real-time forecast ( using advanced analytics) of future behaviour so that customer can be offered appropriate product. This enables profit maximization

 

Getting up the value chain - How do you demonstrate value in a time of uncertainty?

8. How do you move up the value chain to ensure your data and analytics team is achieving its full potential?

Infrastructure up-gradation to ensure 360degree data storage at customer or business level, Usage of advanced  analytics techniques, Well trained analytics team and continuously upgrade them with new knowledge

 

9. How do you move from reporting function to strategic analytics function?

Start with small analytics projects and move up the ladder. Never try a big bang approach

 

10. How do you ensure you are working on the most important business problems?

At the planning stage estimate the revenue impact if the project is successful. Rank order by revenue and choose from the top. However, have an idea of the feasibility of success. The too difficult problem should not be attempted first.

 

11. The idea of analytics team being and order taker – how do you become a partner in business problem solving that can be commoditised?

In a successful organization where it is strongly analytics-driven, this is never the case. In many cases, people confuse analytics with IT. IT team can be a typical order taker, but not a sophisticated analytics team. Analytics team works hand in hand with business users to formulate the problem and in providing solutions.

 

12. During a recession, how would you run the business as usual data teams on a smaller budget?

Recession or no recession: usage of data never ends. In fact, during the recession, you remove fluff ( like an advertising budget etc.) and rely on data to run the business as this is the cheapest way to get high ROI. So in business planning, data and analytics team should be largely untouched. In the normal course of things they should not be grown rapidly so that in the time of recession many become a liability

 

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