Interviews with PayPal's Experts
Featuring Ling Yang Feng, Business Analyst at PayPal
I got my diploma in Electronic Systems from Nanyang Polytechnic Electronics but switched to Computer Science when I studied at Nanyang Technological University (NTU). I enjoyed the creative freedom software development offered me as opposed to hardware. During my last year in university, I specialized in data analytics with courses like data mining, and machine learning. I began my career in analytics in the banking sector. At the time I graduated, other sectors like ecommerce and SaaS were in their infancy, and the role of data was not as highly valued as it is now. Fast forward to today, that situation has turned on its head. I eventually moved into my current role in PayPal because it offered me the freedom to experiment with different ways to use data to help the business identify risks and opportunities.
In PayPal I was given the freedom to come up with my own projects to use data in any way I thought might add value. One project I’m proud of is the. A classifier in machine learning is an algorithm that synthesizes data into one or more of a set of “classes”, like how email providers determine what emails look like spam.
The goal of the project was to help PayPal understand which businesses and merchants were suffering a severe decline in business due to COVID-19.
The classifier informs PayPal’s underwriting teams to approach these merchants to enact buyer protections, and also not withhold payments from sellers, immediately disbursing funds to avoid impacting cash flows. As PayPal’s goal is to ensure a positive buyer experience by guaranteeing refunds to dissatisfied buyers, this classifier helps to prevent bad buyer experiences.
I think it is important for aspiring data analysts to start with an idea of where they want to be, or what role they want to play in terms of the data value chain in any organization. That will in turn help to guide your decisions on how you plan to use data, and therefore what hard skills you will need to do that job.
At the very fundamental level of the value chain, you have data engineers who build databases. They design, build, integrate data from various resources, and manage their company’s big data ecosystem by making sure it is easily accessible, and works smoothly. For such roles, languages common to data warehousing like SQL, Pearl or Java will be essential.
Moving up the value chain, you have data analysts – which is where my role sits. Data analysts query and process data, provide reports, summarize and visualize data to inform decision making in a company. Basic descriptive statistics are all that’s required for such roles. Knowing SQL is beneficial, as well as Python and R. Analysts will also need to know how to build dashboards using visualization tools such as Tableau and Microsoft Power BI.
At the very top of the value chain, particularly in larger organizations, you have the data scientists. Data scientists are the ones who apply advanced statistical analyses, and machine learning to help organizations turn their volumes of big data into valuable and actionable insights. The problem-solving skills of a data scientist requires an understanding of traditional and new data analysis methods to build statistical models or discover patterns in data. For example, creating a recommendation engine, forecasting models, or classifying customers into categories. In such roles, data scientists will need a strong grasp of Python, statistics, as well as visualization tools.
Firstly, curiosity is one important trait of a good data analyst. Like in the project I described, a good analyst is not limited by your existing access to data, and willing to find new data streams to enhance the logic of the data. It is also important to keep upgrading yourself through refresher courses. The drive for continuous learning, and self-improvement is needed because data analytics is always constantly evolving, and the real learning happens outside of school.
Secondly, I think a good analyst needs to have an end-to-end understanding of the entire data value chain, both downstream and upstream. Of course, it is not necessary to be technically competent in all segments of the value chain, but possessing knowledge of the entire value chain allows you to be able to work across different departments responsible for their own segments of the data value chain. For instance, data analysts should also have a good understanding of data structures and engineering. Otherwise, it would be difficult work cross-departmentally without a good understanding of the data pipeline.
Lastly but certainly not least, business acumen is important. It is not sufficient to be technically competent in the tools of the trade. Being a good data analyst that can produce valuable, actionable insights requires that one first know what is valuable to the business. When I was in college, we did not have the opportunity to learn basic economics and business concepts like demand and supply. The growth of interdisciplinary courses in universities are a step in the right direction. After all, data is only valuable insofar as it can help drive managerial decisions.
With regard to soft skills, mid-career switchers should leverage on their commercial experience to understand how data can be used to add value to their industry, and specific processes or functions within their organizations.
As for hard skills, getting a degree in business analytics will give you the best chance of being employed in a data-related role. But of course, that option is often not available because it can be pricey and requires a year or more of being out of work. A good alternative to a full degree is to get professional certifications from the providers of tools like SAS, or Tableau. Lastly, online courses such as TechCareers’ where courses are paired with apprenticeships with top companies can give individuals a significant competitive edge over their peers. Combined with professional experience from their past roles, that should be more than sufficient to allow a mid-career professional to be able to land a role using data. Finally, if you have the time and the resources, a degree in data analytics would be ideal.
Side projects are also a great way of demonstrating your skill and interest in using data. Most governments like Singapore publish free, public datasets where people often do some really cool analytics projects like public housing pricing models, or traffic analytics. Or you can find something that you are passionate about like sports, or maybe even fashion to predict the outcome of a game, or how well a new sneaker release will contribute to share price growth. Working on a side project you are passionate about is a great way to demonstrate competence and interest in the applications of data analytics.
I think there is a growing awareness among companies about the value of data-driven decisions. Smaller companies are starting to build the foundations of good data structures that are required before you can even begin to do analytics. I estimate about 50% of companies are catching up in this respect. Another 25% of companies probably have a good foundation of data engineering, but are underutilising their data to create dashboards to inform decision-making. Another 20% or so are already actively using data to create value in their organizations, and the remaining 5% are the top-tier tech companies like FAANG to whom data is a core part of their business model. The race is never ending, and it is never too late to start looking at how data can create a competitive edge for businesses.
The demand is definitely driven by larger, more mature tech firms, but the headcount requirements are low and entry requirements are highly competitive. I think the bulk of the demand for data analysts will come from ecommerce which is a rapidly growing and highly fragmented industry. As more traditional firms move their sales processes online, there will be a growing demand for data analysts, from those companies that are going digital.
Featuring Dominic Ng, APAC Country lead, Database Marketing at PayPal
I majored in marketing at the Singapore Management University. My start in analytics really began in doing web development gigs to pay off my college tuition. I had the benefit of a highly entrepreneurial community, where I got together with a few friends to run our own web development agency for small businesses looking to go into ecommerce in the early 2000s. We developed POS system interfaces, and ecommerce platforms like an auction site for art gallery, or a user-generated content publishing system. At the time, the internet was barely out of its infancy, and web builders like Shopify didn’t exist. We were entirely self taught, reading “for dummies” books on web and database programming. The fact that the money was better with more complex client requests also helped propel our learning.
When I graduated, I went to work for a few years at various advertising agencies on digital marketing and SEO/SEM campaigns, before finally getting into Cisco systems where I remained for about 5 years. At Cisco, I focused on demand generation by driving leads using different digital marketing channels. After that, I took on a brief stint as the Vice President of DBS’ online business, before moving to help the grocery retailer, NTUC Fairprice transition to ecommerce. Today, I head PayPal’s demand generation for Asia Pacific, Japan and China.
In the early part of my career at advertising agencies, Google had just launched AdWords. I used data to optimize media buying by understanding the right websites, right keywords and right channels to bid for to enhance our return on ad spending.
When I moved into a broader role in demand generation at Cisco, I used data to build a lead prioritization model with propensity modelling. We built models to predict the likelihood that web traffic visitors, leads, and customers will perform certain actions, and thus use it to make better decisions about which leads our sales representatives should prioritize in our customer relationship management software tools like Salesforce. That is also very similar to what I do at PayPal today.
I think some of the basics like SQL, Python are especially important for backend work. Depending on the role you intend to take on in an industry or your specific organization, you may even have to pick up web development languages like Java, or HTML as APIs drive much of the external data feeds that provide the ability to pull data from the web. Technical skill sets are constantly evolving, so I think it is important to have some basic foundation that allows you to gain an entry point into the field, just like I did. Starting somewhere will help to reduce the learning curve when you have to learn new languages or skills.
Learning how to learn is critical. You need the ability to go into a totally unknown landscape, absorb everything like a sponge, identify the skills needed to do your job well, and learn those skills in a time efficient way. That’s been true of my own personal career as well. I started out as a self taught web developer with no formal education in coding. When I was tasked with creating Fairprice Online, I joined the supermarket giant with zero grocery retail knowledge, and knew nothing about ecommerce fulfillment logistics. Yet I had to maximize razor thin profit margins, while keeping the product mix evolving and relevant, especially for a social enterprise.
Adaptability and tolerance for uncertainty is also critical. The business environment is constantly subject to change whether due to technical innovations, new cybersecurity threats, the regulatory environment, or macroeconomic factors. The plan is always changing, and a good analyst needs to be quick to adapt – especially in a post-pandemic world.
I think it is important to be clear why you want to enter into the world of data analytics.
You have to believe in the value which data creates in this world. I’ve always used data to justify what I do throughout my entire career, and in my personal life. That empiricism drives everything I do, outside of my job, as much as it does on the job – from personal finance, to raising my kids. Data is more than just a tool to me:, it’s a worldview. To borrow the words of the late astrophysicist Carl Sagan: “Science is more than a body of knowledge, it’s a way of thinking. A way of skeptically interrogating the universe with a fine understanding of human fallibility”.
Having this values-driven mindset is also critical for people pursuing a career working with data because the industry is constantly disrupting itself. Just as platforms like Shopify and Canva are reducing the barriers to entry for web development and graphic design, it is not unthinkable that the world of data analytics will also come to be dominated by low-code/no-code platforms. The line between technical and non technical functions is coming down, and data analytics tools are going to become more highly accessible, which will mean that only a small proportion of people in an organization will need to be true data scientists and statisticians.
So what you should focus on in your learning journey is understanding the fundamental principles and logic that undergird back-end processes like data engineering or computational logic. Just like Shopify and Canva will never make good web developers and graphic designers obsolete, pursuing a deep understanding of data architectures and analytical concepts will allow you to be more proficient and efficient than your competitors. You will be able to introduce higher order tools or more complex functions, or allow you to better evaluate the low-code options by understanding what you are looking at. Understanding how things work at a deep level, allows you to move faster and make decisions when technologies evolve.
I think there are two main trends. Firstly, it’s the development of sophisticated content recommendation engines. We can already see it in action on the Chinese tech platforms. For instance, my wife and I share the same Taobao account. Naturally, we have very different search preferences and patterns. I tend to look for tech-related consumer electronics, while she tends to browse cartoon merchandise. But it’s apparent how the content recommendation engines are sophisticated enough to synthesize our different browsing preferences in real time, like serving me consumer electronics branded with cartoon characters my wife likes.
Secondly, I think data governance and security are no longer an afterthought. Legislation like the GDPR in the European Union means that companies have to be increasingly transparent about what data they collect and how they use it. Ongoing consent is increasingly something that platforms will have to think of. The migration of some users away from WhatsApp over privacy concerns is one prominent example of such a shift in the landscape.
Cybersecurity is also another issue. Hackers, both state and non-state actors also appreciate the value of data. As more aspects of our lives become digital, the security of our data will be something that firms will have to reassure their customers of.
Industries which are tech-heavy like the FAANG stocks naturally spearhead most of the demand for data analysts. Following which, you have tech-related companies like fintech, digital services, or esports which are born digital. Lastly there are the traditional businesses and industries which are going online and adopting more digital processes accelerated by the pandemic. At some point, every function in an organization will need to have a deep understanding of data, and how it can be used to optimize processes or create new opportunities and actionable insights.
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