You watch Netflix, I watch Netflix, everyone watches Netflix
Let’s start off with an assumption: you’re a Netflix user*. Otherwise, you’ve at least entertained the idea of becoming one because the amount of good quality shows they have is insane.
(*Well, given that half of Netflix’s 94 million subscribers were from outside the USA in 2016, this isn’t a bold claim at all. Nonetheless, it’s a guess that we might be wrong about.)
What happens though, if we had access to way more data that we can use… and know how to use it well?
And the walls came tumbling down
In 2011, Netflix struck an expensive deal to acquire two seasons of what would become its first piece of original content. In a bold move, the streaming service put in a reported 100 million for 26 episodes without needing a pilot. House of Cards would go on to make history: it was the first online-only web TV series to receive major nominations at the Emmys, amassing 33 throughout its 6-season run. 16% of Netflix’s US audience tuned in to catch season 2 on the first day of release, a huge jump from 2% during season 1. The series even commanded decent viewership from China and Scandinavia.
Needless to say, House of Cards was a hit…and data analytics was Netflix’s trump card. How? From their data, they realized that
1) Many users seemed to enjoy the original BBC House of Cards adaptation.
2) Many users tended to watch The Social Network, directed by David Fincher (House of Cards’ director) from start to end, and
3) those that fell under #1 would also watch David Fincher films and/or stuff starring Kevin Spacey.
Today, Netflix continues to leverage on predictive data analytics to inform their decision-making and with that, uncover new growth opportunities.
Recall our question: so what happens when we have data and know how to use it well?
As Netflix shows (see what we did there!), remarkable things do.
The undisputable power of Data
Data is a powerful tool when it’s properly harnessed and like Netflix, more and more organizations are becoming aware of the advantages it affords. Resultantly, the demand for individuals adept in data technology, across many sectors, has surged. In 2018, LinkedIn’s Singapore Emerging Jobs Report noted that data scientists grew 17x more within the last 5 years. With rapid digitization, it’s a field full of possibilities and career growth.
The reality, however, is that there isn’t enough talent to go around just yet: in 2015, for example, the Institute of Management Accountants (IMA) and Robert Half (a global HR resource consulting firm) surveyed close to 500 financial managers and execs. They found that many leaders viewed big data/data-related skills as important to success, but their accounting and finance teams faced a skill gap or did not possess them.
So, what’s to be done?
How do organizations get their talent, and how do individuals interested in picking up data-related skills get a foot into the field?
The most direct way, really, is to read data technology at the tertiary level. In fact, it’s fairly standard these days for entry-level data technology jobs to require candidates to have a bachelors’ degree in a related field at minimum (some of which we introduce in this handy listicle).
An upper-level job, proportionately, requires a Masters Degree and schools have begun to offer such programmes here. Not too long ago (Jan 2018), Singapore Management University (SMU) rose to meet the demand, homing in on the accounting and finance industry.
The university’s Master of Science in Accounting (Data and Analytics) is currently the first of its kind in Asia to incorporate data & analytics into its postgraduate curriculum. Recently, the programme received industry recognition in the form of the UOB-SMU MSA scholarship, a full-tuition scholarship that’s also the first of its kind.
We managed to speak to the Programme Director, Associate Professor Wang Ji Wei, about what this particular programme has to offer.
“In addition to the partnership with UOB, we are also driving industry partnerships through an SMU-X course that is part of the MSA programme,” Associate Professor Wang shares.
“Unique to SMU, SMU-X is an award-winning pedagogy based on collaborative experiential learning. The MSA SMU-X course encourages students to leverage data provided by organisations from different industries to solve real-world issues. Students have opportunities to be mentored by both SMU’s faculty members and industry professionals. They are also able to apply their accounting and data & analytics skillsets to implement solutions for partner organisations—just like they will need to in order to thrive in the business world”.
What if university isn’t really a possibility for me at the moment?
Fret not, there’re alternative paths that can help you pick up big-data related skills! Several of these fall under the TechSkills Accelerator (TeSA), a SkillsFuture initiative to help reskill and/or upskill individuals from both ICT and non ICT backgrounds. The initiative caters to several demographics through various programmes, such as the Earn and Learn programme for fresh polytechnic and ITE grads, the Tech Immersion and Placement Programme for non ICT professionals, as well as the TeSA pilot immersive for mid-career PMETs.
The future, in this age of digital transformation, is inseparable from big data. Are you ready to rise to the challenge and unleash its fullest potential? We hope you are.
P.S: We found this pretty interesting article sharing more about the representation of data science in House of Cards, which is worth a read. Wink.