Updated: Apr 12
We’ve all dealt with data in one way or another. In fact, every day, we collectively produce 2.5 quintillion bytes of data across the globe. That’s about 318 megabytes of data a day, per person (is it any wonder your phone keeps running out of memory?). As I write this blog itself, I am generating bytes of data that are being packaged and displayed as content that you, dear reader, get to access on our website.
In a matter of years, we’ve gone from using floppy disks that could only hold one to two megabytes of data, to USBs holding several gigabytes, to hard disks and cloud drives that hold terabytes of data! This is a glimpse into how rapidly data is being generated today: a growth largely driven by our access to and affinity with technology. In the last two years alone, we generated a whopping 90% of the world’s data.
Big data - what’s the big idea?
You’ve probably heard about ‘big data’ on the news, or seen advertisements for data science workshops. If you’re unfamiliar with these fields, especially since data science is a relatively young discipline, it’s likely you’ve been wondering just what on earth it’s all about.
Big data refers to large volumes of data that can’t be managed or analysed by typical analysis tools. A lot of these data are often unstructured, such as blocks of text, social messages, photos etc., that don’t have a fixed, predetermined structure. This is where data science comes into play. Data science uses methods in mathematics, computing, data analysis and statistics to extract useful information from both structured and unstructured data, which can then be further refined and analysed.
As exciting as this can be, the problem of talent shortage haunts this industry. More professionals with the technical expertise to mine, extract, manage and analyse these large data sets are needed for this plethora of data to be useful. But enticing youth to take on careers in this field is difficult - science and technology is a challenging path that requires specific skills and knowledge that need to be continually updated as technology evolves over time.
Paradigm shifts in research and industry
The ability to extract and process large data sets opens up brand new approaches that would have been too labour intensive to consider in the past. In the traditional sciences, humanities and social science industries, this means that data across research or large amounts of data from cohort studies can be more easily compiled and analysed. Data that would have taken months or years to analyse, can now be done in a mere few weeks or even days!
Data science isn’t just changing science and research, it’s even shaping our everyday lives. Ever wonder how an ad for a product showed up on Instagram just after you had searched for it online? Machine learning algorithms employed by tech companies such as Google, Facebook and Instagram have changed the way we approach marketing and consumerism.
From targeted advertising to market analysis, user data analysis has become an important asset in both research and business/industry. The convenience that comes with artificial intelligence and machine learning means that these developments will continue to advance and be in demand in the future.
It is clear that the demand for data science professionals will soar as our dependence on technology and data continues to grow. STEM education aims to address this very problem by inculcating essential skills, knowledge and critical thinking in our youth in the K-12 period, prepping them to do well in these fields.
Want to know more about how STEM education helps prepare our children for careers in computing, data analysis and more? Stay tuned for part 2!
Till our bytes cross paths again,