The sheer amount of data produced each day is almost overwhelming. Businesses and individual users churn out roughly 2.5 exabytes of data every 24-hours. That’s 2.5 billion gigabytes. Every single day. There’s so much data being conjured that experts believe we’re heading toward a ‘Data Apocalypse’, where we’ll literally run out of physical space to store the world’s digital information.
But data is useless without someone to organise, categorise, sift through and translate raw numbers into real-world business solutions. Which is why Data Analysts and Data Scientists are two of the fastest growing professions in the world.
The rise of the Data Scientist
Data has been called the ‘new oil’. PWC now predicts an extra 2.7 million data jobs globally by 2020. But the rapid growth of Big Data, and its proliferation through pretty much every industry, has caught the education sector unawares. In the US alone, the number of data scientists has grown by 650% since 2012. And recent IBM study says we’ll need 28% more data scientists worldwide by 2020 to keep up with booming demand. The fact is that traditional tertiary education models simply aren’t churning out skilled graduates fast enough.
The impact of regulatory changes
Demand for data skills, particularly good data governance, is also been driven by governments. Big Data is no longer the Wild West that it was five years ago. In 2018, the EU introduced its General Data Protection Regulation, which made businesses more accountable for how data is used, stored and protected. In Australia, new shifts like Open Banking and the updated national privacy regulations are forcing companies to invest in data governance and qualified data scientists. In a world where customers have more say over their personal data, the onus is on industry to comply and keep up.
Data jobs are tough to fill
Slow graduate numbers are only one part of the skills gap. Another factor is the unique alchemy of professional skills that makes a good data scientist. As Senior Web Technology Manager Erik Berger told Udacity, it requires more than Python fluency and an ironic t-shirt, “You obviously need the technical skills to be able to extract data and run statistical analyses,” he says, “but there is the more intangible ability of finding patterns or irregularities to report on. To be good at it, you need to fully understand the nature of the business that you’re analyzing—just looking at the numbers is only half the story.”
This combination of technical knowledge (Python and R coding, SQL experience, statistical distributions, machine learning etc), business acumen and good communication skills is pretty rare, which makes entry-level data jobs tough to fill.
Employers are looking in the wrong place
KJ Kim from Tableau Software agrees that data is more than just analyzing numbers. It requires an inquisitive mind, creativity and a grab-bag of soft skills. And senior executives often misunderstand what it means to be a data scientist, ignoring good candidates because they lack high-level tech fluency. “Engineers or data scientists are going to need specialised skills that are relevant to their industry or area of analysis,” says KJ. “However, there’s also a need for business professionals that understand how to explore and gather insight from the data. Their knowledge of the data’s business relevance is critical to weave a coherent story, backed by good numbers.”
What’s the solution?
Although the demand for qualified data scientists is sky-rocketing, closing the skills gap isn’t impossible. Education institutions are already starting to adapt, offering targeted online shortcourses in data analytics, data science, machine learning and AI Python coding. The idea is to offer the existing workforce an upskill alternative to cumbersome bachelor degrees. Something cheap, nimble and quick, that can be done outside work hours. The Australia government’s new APS Data Skills and Capability Framework is another step in the right direction, partnering industry with the education sector in an effort to close the skills gap.