We all know the enormous power of data, of having statistics and numbers to back a claim or inform a decision. Yet, the immense quantity of data produced today is overwhelming without the help of skilled experts to decipher its meaning.
Ninety percent of existing data was generated in the last two years. Sure, much of it came from cute cat videos shared online, snaps of avocado toast and comments in praise of a good selfie. It certainly does not mean that 90 percent of the wisdom accumulated by humankind has occurred in the past two years. Far from it. Yet, the quantification of not only the quirks of human behavior, but of how other biological life, climate patterns and even outer space works serves as the raw material for the ability to better grasp what’s happening in this strange universe of ours.
As American astronomer and teacher Clifford Stoll once said: “Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom.”
Simply having raw data does not lead to improved performance. However, a new space has opened up for the wizards with the skills to alchemize data into insights. According to the Harvard Business Review, this has even become the sexiest profession of the 21st-century. Rock stars out, data scientists in.
Rafif Srour Daher, Academic Director of IE University’s new undergraduate program in Data and Business Analytics said students “go far” with this kind of degree. “Students can become a data science generalist, and this is where most of the job market is, which means being hired by a company that is not necessarily a data company, but that’s looking for people who truly understand data so that they can gain insights from that data and make the best use of it. However, students can also become business intelligence specialists, data architects, data mining engineers, and much more.”
The competitive advantage of data
According to IDC, organizations that take advantage of data analytics stand to achieve $430 billion in productivity benefits by 2020. But the 2016 PwC Data and Analytics Survey found that most executives say their next big decision will rely mostly on human judgment instead of data.
That means that it isn’t so easy to derive insights from data, even in some of the biggest companies. Especially with the rise of Artificial Intelligence (AI) and machine learning, which are highly fueled by data, the companies that make decisions based on hunches or inertia are increasingly at risk of falling behind.
“Data is the difference between failure and success for a company right now,” continued González del Regueral. “All young data scientists need to have knowledge in terms of how to use the data, how to extract value from the data, and not only that, but how to gather data from different sources to make meaningful decisions… The ideal data scientist is someone who wants to solve challenges and who is willing to tackle those challenges using data.”
Some of this century’s top-performing companies are heavily reliant on data. Take Airbnb, the sharing economy hospitality platform that was started in 2008 and has seen its business scale up by more than 43,000 percent. Its success is attributed to many factors, but it was also one of the few companies at the time that included a data scientist within its initial team.
Riley Newman, Airbnb’s former head of data science at Airbnb, has said that the company looks at data as the voice and decisions of the customer. “Our role as data scientists is to keep the company in touch with those decisions and experiences that guests and hosts have,” he explained.
Beyond that, Airbnb’s data scientists are not just crunching numbers in isolation. Instead, they partner with other members of the team like UX (user experience) designers, engineers or product managers to ensure that the data are democratic and seep into the decisions being made across the board.
Internet giant Google is also heavily reliant on data. Even in areas like design, the company looks to the numbers for what works best. Famously, they used A/B testing on 41 slightly different shades of blue for ad links to see which one would generate the most clicks. This idea even caused Google’s lead visual designer to quit, saying data was being used as a crutch. However, shortly after, the company estimated that choosing the blue based on data generated an extra $200 million for the company each year.
“We saw which shades of blue people liked the most, demonstrated by how much they clicked on them. As a result, we learned that a slightly purpler shade of blue was more conducive to clicking than a slightly greener shade of blue, and gee whizz, we made a decision,” said Google UK’s former managing director Dan Cobley.
With the potential benefits of data becoming so clear in the business world, it makes sense that the right number-crunching could help governments and public institutions make better decisions as well. As Airbnb uses data to understand its customers’ needs and voices, the government also has the capacity to use data to better understand citizens.
Through tools such as the census, healthcare records, smart cities and other information collected by various public sector programs, governments have traditionally had access to some of the world’s richest data sets. But with limited resources and expertise, they have been slow to capitalize on the data. That seems to be changing, however, as governments like the UK have been exponentially ramping up spending on data scientists in recent years.
While data scientists are being employed in nearly all government fields – agriculture, education, justice, infrastructure – healthcare is one of the most exciting sectors. According to a report by the EMC and IDC, by 2020 there will be around 2314 exabytes of global healthcare data, which is more than all the written works of humankind in every known language 46,280 times over.
These vast amounts of data combined with data-driven technologies like AI, promise to improve healthcare outcomes and efficiency in several key areas, according to the World Economic Forum (WEF).
For one, the WEF believes that AI can be used to optimize clinical trials, enabling faster development of life-saving drugs, which will save billions of dollars, and most importantly, many lives. In practice, a few years ago a startup programmed a supercomputer to analyze millions of medicines to predict their effectiveness against Ebola. That program saved money on physical tests in the field, as well as lives by allowing doctors to repurpose existing drugs.
Furthermore, a 2019 study published in the Journal of the National Cancer Institute found that a commercial AI system was as accurate in detecting breast cancer as the average radiologist. While the program was outperformed by the most accurate mammographers, it was better performing than the majority. While no one is insisting on replacing radiologists with AI (at least for now), it could assist them in reading X-rays more quickly and accurately, while also freeing up time for them to focus on more complex issues.
Colin Banas, chief medical information officer at an award-winning medical center in the U.S. notes how data is becoming increasingly central in the healthcare industry. “I am reminded of a quote from one of our senior leaders. She even puts it at the bottom of her meeting minutes. It says, ‘In God we trust. Everyone else must bring data.’”