Discover+: Analytics & Insights

By Talia Tan & Michelle Yap

Discovery+ is a series of online industry panels which give students the chance to interact with working professionals and learn about the careers they aspire to enter. These panels provide youths and working professionals with the opportunity to better understand industry trends, hear first-hand perspectives from industry professionals, and gain valuable advice on entering or navigating these industries.

On 17 May 2022, Advisory hosted Discover+: Analytics & Insights, the 58th edition of the Discovery+ series. Speakers on the panel included:

  • Sagar Phadke (Moderator), Director, Global Strategic Insights, Johnson & Johnson
  • Dale Preston, Head of Client Insights CCIB DCDA, Standard Chartered Bank
  • Deepak Chandran, Head of Insights for YouTube, Asia Pacific, Google

Below are some key points shared during the session:

The career opportunities in this sector are wide-ranging and promising. For example, Singapore’s SkillsFuture report has reflected that the top 5 priority skills for tech-lite roles in the digital economy are as follows:

  • technology application (skills to operate, adopt and apply new technology); 
  • data analysis/analytics (skills covering data collection, data management, data interpretation and data visualisation, applied in research or business); 
  • market research/trend (skills to enable businesses to make informed decisions on their business directions); 
  • technology scanning/evaluation (review new developments in emerging technology as well as evaluate and determine relevance of emerging technologies to the organisation), and; 
  • automation application (applying and integrating evaluated technologies into organisation operations or processes to reduce reliance on manual tasks). 

Additionally, it is notable that career opportunities in this sector are not only limited to quantitative or technical roles, but also extend to qualitative and human-centred roles. 

No one day is exactly the same as another, but there are a few elements which remain fairly consistent throughout one’s day-to-day working life external stakeholder management, internal stakeholder management, internal team discussions, individual research, individual deliberation, etc. As organisations often rely on data to guide their decisions, employees play a key role in distilling the relevant insights through data analysis and conveying these insights to leaders of the organisation effectively, to enable the organisation to make better informed decisions. 

There are two broad groups of skillsets required: technical skills and soft skills. Examples of technical skills include competencies in Microsoft Excel, SQL, Python, Business Intelligence Tools such as Tableau, etc. Examples of soft skills include communication skills, presentation skills (especially storytelling), a naturally curious mind, a healthy dose of scepticism, etc. Notably, one does not necessarily have to be extroverted to thrive in this industry. 

The answer to this question ultimately rests on the specific job that one undertakes. Although being comfortable with mathematics and coding will certainly be an advantage in this field, these skills may not be absolutely necessary if one is dealing with non-numeric data, for example. Furthermore, not all analytics are driven by complex programming languages some of the best insights are derived from the effective utilisation of simpler tools. 

The underlying skills you need to succeed will be complementary regardless of whether you are in the public or private sector. Typically in the private sector, a large part of analytics and insights is geared towards driving business decisions, ensuring that products and solutions are profitable and beneficial to consumers. There is a commercial angle involved, so whatever analysis you do is geared towards that end objective. Analytics in public policy is targeted towards improving public welfare. Data that you collect or analyse tracks the outcome of a particular policy and helps you make projections that will be beneficial in the future.  What is interesting about working in the public sector is the unique nature of the problem you are trying to solve. You will then find the right data sets to work on and synthesise, based on how the problem is defined. Subsequently, this guides relevant authorities to make decisions that can effectively serve the needs of the public. 

This is one of the most essential skills to develop: behind each dataset, there is a real person and voice. Ultimately, whatever decision you make about a product or service is going to have an impact on all of these people. For example, predatory pricing policies in supermarkets will affect the ability of low-income households to fulfil their basic needs. It is a responsibility that should be taken very seriously.

A key part of evaluating an employee’s performance is assessing the impact they have had on the organisation. This can be broadly classified into three categories: financial impact (did you drive higher profit or sales?), improved cost efficiency (was the process streamlined in a way that led to cost savings?), and innovation (were you able to change consumer behaviour in a positive way?). Another important skill you need is the ability to work well with others, to create a cohesive story out of data. 

Strategy and analytics and insights are closely intertwined. Insights and analytics shape your organisational strategy. An insights function has a more objective perspective within an organisation, as it is not connected to a particular department like finance or operations. Working in an analytics and insights role requires undiluted representation of consumer opinion, where business recommendations are made without consideration of other constraints like cost. Those in strategy will decide whether those recommendations should be taken forward. Strategy functions also tend to be more broad-based. 

Advanced analytics, artificial intelligence and machine learning are getting even better at spotting patterns, which makes the job of pulling together disparate data sets easier. But the process of interpreting data cannot be automated.