When scientists conduct experiments, they always record data; such information isn’t the end game of any experiments, though – rather, scientists use data they obtain from such experiments to make inferences and draw conclusions. This concept holds true for data science, as well; data unarguably is worth nothing by itself, though it becomes very valuable when its owners properly scrub, sort, prepare, and analyze it.
But how exactly does data science add value to businesses? Understanding data science, its closely-related concept of big data, and applications of such swaths of information in the real world is far from simple.
To best understand the reasons for data science’s inherent involvement in business, let’s first define a few terms and explain what they consist of.
Related resource: 20 Best Data Science Certificate Programs
What are data science and big data?
These two concepts are closely related, though not at all the same. Big data, according to Investopedia, is the “growth in the volume of structured and unstructured data, the speed at which it is created and collected, and the scope of how many data points are covered.” In short, big data simply refers to tons and tons and tons of data.
Data science, for example, can very well use big data. Yet again, according to Investopedia, data science is coined as “a field of big data geared toward. providing meaningful information based on large amounts of complex data.
This field – the one of the latter concept, data science – has existed for roughly 17 years on its own and away from other fields like computer science. Although the field was first attached to computers and its theoretical application, data science has since shifted towards business use.
How does data science add value to a business?
Data science is an incredibly broad field with countless applications. While covering every single popular utilization of data science is entirely out of the scope of this piece, there is room for several valuable applications of data science. Let’s take a peek at a few of them in the name of better understanding data science.
Google became Google through countless analyses of big data
Google uses data science for countless applications – even off-the-wall things most people wouldn’t think about like improving its self-driving car – to find out what its users are interested in. For example, if someone is using Google to look up “food near me,” Google will garner all demographic information from that web user in an attempt to find links between age and click path, the population density of the area the Internet user lives in and how far they scrolled down, and tons of other potential positive or negative correlations that most people don’t think of.
Have you ever felt like Amazon, eBay, or another e-commerce site knows too much about you based on your search history? Data science is to blame
Digital advertising is one of the most popular uses of data science in today’s world. On social media sites like Facebook, advertisers can reach out to specific groups based on things like race, age, workplace, and college enrollment status. These advertisers will later be given reports on their efforts in an attempt to find what worked and what didn’t.
Data science might be difficult to understand – well, maybe it’s easy to grasp the concept of if you’re a statistician – though its use is far more common than what most people realize. Businesses profit from a virtually endless list of benefits related to using data science.