Big Data. It's one of those terms that is now so widely—and, let's be honest, indiscriminately—used in the business world that even the least cynical among us are probably getting sick of it. It seems to get thrown around to fill gaps in waning meetings and to make businesspeople sound like they're up with the digital times.
But the thing about "Big Data" is that although it might be overused—and occasionally misused—it isn't a meaningless cliché. Digital technology has swept away the analog world we once worked within and inundated us with data. And the more sophisticated the digital technology gets, the easier it makes it for us to send information or for others to track our buying habits. In other words, more and more data is generated.
It's an often-repeated statistic: In 2013 IBM claimed that "90% of the data in the world today has been created in the last two years." More recently, in 2015, Cisco predicted that the amount of data created in 2019 will eclipse the total data created in all prior Internet years combined. And, within two years, each person will apparently be producing, on average, 1.7 megabytes of data every second.
Big Data is... well... that: an enormous amount of information. Specifically, information that can be collected and analyzed (which is a generally accepted broad definition of data).
But is there a more precise meaning? And what are the implications for marketing?
'Big Data': A More Specific Definition?
"Big Data" really began its rise to prominence in the early 2010s, but it was, according to a word sleuth from the New York Times, being used in Silicon Valley as far back as the 1990s.
In 1997, NASA used the term to refer to a difficulty it faced: "[It's] an interesting challenge for computer systems: data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of big data."
Indeed, although there seems to be no agreed-upon definition of "Big Data," most definitions mention the problematic nature of the size of datasets. They refer to the logistics of accommodating the data, but, more significantly, also the trouble that old technology has with sorting and analyzing the new, gargantuan volumes of data.
Less formal definitions also talk about Big Data as an opportunity. Yes, the argument goes, we're swimming in figures, stats, demographic numbers, and so on, but if we could harness them then we could build an analytical picture superior to anything we've ever seen before—and in just about any discipline. Or, to quote a blog post by data experts import.io, "more data is better, if you know what to do with it."
How Does Big Data Apply to Marketing?
Big Data applies to marketing in countless ways. Here are just a few of the most important:
1. Advertising and content. The digital revolution may have made the promotion of products more involved—with new media, new platforms, new techniques—but it has made tracking and monitoring the performance of that promotion simpler. A good data scientist can help an organization experiment with its advertising and content, using digital information to determine what methods and messages are resonating with customers and which ones are falling flat. And then you've got the entire field of customer insights, which is so vast it really deserves its own article to do it even a hint of justice.
2. Pricing. Professional analysis of data can change the way organizations approach pricing. In 2014, McKinsey estimated that "30% of pricing decisions companies make every year fail to deliver the best price." Its suggested solution to the problem was data analysis: "For those able to bring order to big data's complexity, the value is substantial."
3. Optimizing spending. Another major McKinsey report discussed business as a series of trade-offs—price for volume, for example—and said that "in the past, many such trade-offs have been made with a little data and a lot of gut instinct." The "new world," according to McKinsey, allows businesses to use "advanced analytics—particularly more real-time data—[to] eliminate much of the guesswork" when attempting to ensure spend is spot on for any number of marketing activities: from social media to call-center investment, or traditional advertising to store fit-outs.
4. Seeing into the future. The very best data analysis doesn't merely help marketing teams make incremental improvements (as important as they are); it can be utterly revolutionary. We're starting to see examples, all over the world, of Big Data being used as an extraordinary tool for prediction and forecasting trends.
As the import.io blog puts it: "Forget copycat trends, corporate espionage, or stealing the competitor's best workers. Data science taps into the information that's already out there, the information that's pointing the way a trend is headed."
What all these things have in common is this: The information alone, provided by an era in which half the world's population has access to the Internet and 50 billion devices are connected through the Internet of things, is essentially useless unless it's carefully and expertly analyzed.
Data scientists therefore become critically important. But, at the moment, and for the foreseeable future, there aren't enough of them.
The Data Scientist: A Precious Resource?
Big Data can't be a fad, in part because the market tells us it isn't.
It's been more than half a decade since the Harvard Business Review described the role of data scientist as "The Sexiest Job of the 21st Century," yet there are still massive shortages throughout the world. Industry wants professionals who can separate the wheat from the chaff, and there simply aren't enough to go around—not even close.
In 2016, a report on data science by CrowdFlower found that 83% of American data scientists surveyed reported a shortage of qualified people in their field. It produced a similar report in 2017 but didn't ask the same question; instead, it inquired about how "in demand" respondents were. In total, 89% of data scientists said they were contacted at least once a month for new job opportunities; 30% said they were contacted several times a week.
The remarkable market shortfall is tough for business, but (as the CrowdFlower study hints) brilliant for data scientists and those who will become data science professionals in the near future.
Becoming Qualified
In a newspaper article in 2015, Jodie Sangster, CEO of the Institute of Analytics Professionals of Australia, described a data scientist as "somebody who not only understands data but understands business and can make the bridge between using data and driving business outcomes."
That, among many other things, is what a Master's in Data Science will help you to become: adept at applying data analysis in specific business and marketing contexts.
If you're interested in becoming a part of this booming field, you'll study subjects across the mathematics, information technology, and business domains. At James Cook University, all subjects are taught by respected interdisciplinary academics and industry experts. And you can also choose to complete your master's degree entirely online, in your own time, exactly when it suits you.
You can find out more about the Master of Data Science degree program at James Cook University right here.
