Getting Started with Data Analytics
Insights from PayPal's Experts
How to Kickstart Your Career in Data Analytics
Data, Digitalization & the Post-COVID World
From e-commerce to digital banking, from ride-hailing to social media: our economies and societies are increasingly digitalized. 90% of the world’s data has been created in the last two years alone. This wave of digitalization has only been accelerated with COVID-19 related concerns, as cities go into lockdown, and social distancing measures make digital platforms the default way to buy and sell safely. As more of the daily activities in our lives are lived out online, we generate large volumes of data about our behavior and preferences. Data that can be used to understand who we are, what we want, and how we want it.
There is also another growing source of data – from the smart devices and machines that are becoming common in our businesses and places of work. By 2020, there were more than 50 billion smart connected devices in the world, collecting, analyzing and sharing data. Our things are talking to each other more, generating data about the movement of goods in real time, monitoring performance, reporting faults, and sensing information about the physical environment. From smart buildings to autonomous vehicles, from medical diagnostics to logistics: both machines and men are generating large sets of data that can tell us how to make things better.
With all of this data comes a growing demand for people who can analyze it, and extract valuable insights that can be used to optimize processes, identify opportunities, manage risks, and prioritize resource allocation. A 2017 report from Dresner Advisory Services concluded that 53% of companies were adopting big data analytics, up more than 3 times in just two years.
We speak with two experts at PayPal – Ling Yang Feng, a Business Analyst, and Dominic Ng, APAC Country lead, Database Marketing, who share with us how their ability to use data has helped to drive their careers across different industries.
Using Data to Make a Difference
Ling Yang Feng, Business Analyst at PayPal, explains how he uses data to mitigate risks and ensure buyers feel secure. “One project I’m proud of is the “Trend Classifier”, says Yang Feng. 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”, Yang Feng explained.
Dominic Ng is the APAC Country lead, Database Marketing at PayPal. He shares how his skills in data analysis have helped him remain relevant throughout different industries in his career. “When I moved into a broader role in demand generation at Cisco Systems, 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”, Dominic said.
Growing demand for data analysts in the digital services & e-commerce
So data is valuable, and analysts are in demand – but who’s hiring? Our data experts at PayPal weigh in on where they think the jobs are to be found.
For Yang Feng, e-commerce companies will account for most of the job creation for data analyst roles. “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”, he explained.
Dominic notes that it is also worth considering companies who are using technology to disrupt traditional industries. “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”, he adds.
Additionally, he believes that as more traditional firms move their sales processes online, there will be a growing demand for data analysts from companies that are going digital. “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”, says Dominic.
Choosing the right skills
There exists a dizzying array of programming languages, statistical analysis software, and data visualization tools out there. Our experts at PayPal offer their suggestions on how aspiring data analysts can choose the right skills to learn.
For Yang Feng, starting with the end in mind is critical. “I think it is important for aspiring data analysts to start with an idea of what role they want to play within the data value chain of any organization. That will in turn help to guide your decisions on how you approach the use of data, and therefore what hard skills you will need to do that job”, he says.
Depending on what roles you want to take on in an organization’s data value chain, the tools of the trade may differ slightly. He explains, “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, Perl or Java will be essential”.
For analysts, having the right skills to query and present data are important, says Yang Feng. “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”, he explains. Such roles do not necessarily rely on deep programming skill, or advanced statistics, says Yang Feng. “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”, he said.
While such general principles are useful guides, Dominic highlights the importance of understanding how specific companies may use data. “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”, explains Dominic.
Traits of a good data analyst
But knowing the tools of the trade is not enough, explain our experts. They highlight some important character traits that define good data analysts.
“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”, says Yang Feng.
Business acumen is also important to have. 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.
Yang Feng says, “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”. Knowing the needs of the business is critical, for him. “After all, data is only valuable insofar as it can help drive managerial decisions”, he explains.
Lessons on Getting a Headstart in Data Analytics
Outside of the classroom, Yang Feng encourages aspiring data analysts to pursue their passions. “Side projects are also a great way of demonstrating your skill and interest in using data”, he says. Most governments like Singapore publish free, public datasets where people often do some really cool analytics projects like public housing pricing models or traffic flow modelling.
Thinking of where to start? Work on something you love, says Yang Feng. “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”, he explains.
For Dominic, people looking to enter the industry should start with “why”. “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”, says Dominic. For him, this is more than a matter of philosophical drive. “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 or no-code platforms”, he cautions.
Dominic says that what aspiring analysts should focus on 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”, says Dominic. “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 better decisions when technologies evolve,” he explains.
How to get started with learning data analytics
Dominic believes it is more important for aspiring data analysts to start somewhere, and focus on the core principles behind each skill set. “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” he said.
“Getting a degree in business analytics will give you the best chance of being employed in a data-related role”, Yang Feng laughs. But of course, that option is often not a viable one 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 those offered by TechCareers, when paired with apprenticeships with top companies, can give individuals a significant competitive edge over their peers,” he suggests. “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,” Yang Feng says.
The data analytics course under TechCareers, a collaboration between Ngee Ann Polytechnic and Tribe Academy offers a good headstart. Currently open to mid-careerists who graduated before January 2019, the data analytics course consists of 4-months of intensive classroom learning, followed by opportunities to gain a 6-month apprenticeship program with hiring partners such as PayPal for the first intake.
Candidates will be equipped with essential skills such as Python and will learn various concepts such as data visualization, which are the fundamentals analysts require before adopting tools such as Tableau and Power BI.
In addition, to help candidates tide over the course during these post-Covid times, SkillsFuture Singapore will offer a training allowance of $1,200 per month, totalling up to $12,000 over the 10-month period for those who are enrolled in the course.
To read the full interviews with Yang Feng & Dominic, click on their names to visit their individual interview links.
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