Why Exactly is Data Science So Important?
Data science is a field devoted to the use of scientific principles to understand, interpret and gather data. Rising to prominence alongside the adoption of the internet, it is now considered an extremely important scientific field.
Harvard University recently named Data Science as the ‘sexiest’ profession to have emerged in the 21st Century. Whether or not this was an inside joke about the physical proportions of the average data scientist, we will never know. Regardless, the prestigious university was making the point that data scientists are indeed desirable.
They were damn right, too. Data science drives growth, responsibility, and strategy in all fields of business and governance. It is a field that is important because it allows us to navigate the fast-paced world that our adoption of technology has catalyzed.
Why is data science so important? This list is not a complete guide to the utility of the field but should provoke some consideration of why data science has become so important.
We Live in the Data Age
Data Scientists help businesses and governments negotiate a new epochal paradigm: the data age. The data age, often referred to as the age of information or the computer age, is a term used to describe the way in which our society has adapted to computer technology.
In recent years, the advent of the internet and social media platforms has bought the data age into a new stage – a hyper-networked data age. In this current social stage, humans are constantly producing networks and data through their use of the internet.
There is a crisis of data accumulation that makes it very hard to spot trends or forecasts without the clever use of data science. There is simply too much data out there – you cannot accurately generate conclusions about the meaning of this material without taking a look at the bigger picture. In scientific circles, the crisis of data accumulation is known as the data deluge.
Looking at the bigger picture is the job of the data scientist. Developing models and mathematical formulae with the aim of finding the correct data or processing all of the available data is complex work and cannot be undertaken by unqualified programmers or mathematicians.
Specialist training allows scientists, mathematicians, and engineers to develop their data science praxis. Although many universities offer in-person courses, professionals who already have a foothold in an industry may want to top up their training with an online Masters in data science.
Businesses Need Market Insight
One of the most important commercial functions of data science is the creation of deep market insight. Long term business strategy is reliant upon the analysis of data in order to be justified.
Data scientists create models and algorithms that allow for the analysis of the huge deluge of data businesses receive. They are a crucial cog in the business planning machine. Companies utilize data scientists’ handiwork to develop new ways of looking at the mountains of personal data they receive from interaction with their products, websites, and advertising online.
The use of data science in business has, of late, been given a name. Companies strive to harness what is now called ‘big data’ for gain. The private use of browsing data has been controversial at times and manipulated by nefarious actors at others. Strict laws about the use of data in business are being rolled out across the world.
Governments Need Demographic Insights
For reasons both good and bad, governments need to know about the people under their leadership. Data science has been the often controversial key for governments who want to snoop on or understand their constituent citizens.
For reasons both good and bad, governments need to know about the people under their leadership. Data science has been the often controversial key for governments who want to snoop on or understand their constituent citizens.
Governments have come under fire for their often poor use of data. This just goes to show how much of a difference data science can make to the wellbeing of a country’s citizens.
Data science is so important to some governments that they are willing to pay educational grants to talented practitioners. Countries such as the United Kingdom enroll students in data science acceleration programs aimed at producing the next generation of highly useful scientific data wranglers.
Data scientists are also key players in efforts to improve cybersecurity. They are able to write models that predict likely attacks so that cybersecurity experts can focus on the most important areas to improve defenses.
Automation Relies Upon Data Science
Since 18th Century mill owners started installing spinning mules in their mills, automation has been pursued by industrialists and business people fervently. Modern automation, however, would not be possible without the work of data scientists.
This is because robotics and AI – two key developments in automation – require the harvesting and analysis of data. Interestingly, there is a possibility that data science work itself may one day be automated.
Some publications have shrugged off this possibility, claiming that data science will always need human innovation and input. Regardless of what happens, data science will remain relevant no matter what person (or non-person) is doing the work.
Data Analysis Relies Upon Data Science
Data analysis is part of any business strategy worth its salt. Data analysis, however, would not be possible without data scientists.
Data analysts are often talented engineers or problem solvers, but they tend not to come from a purely scientific background. Data scientists are needed to prepare the ground for good data analysts. While the analysts make sense of the data, the scientists create the algorithms and models necessary for the data to be made sense of.
In a well-run organization, the data science team and the data analysis team work together in a closely collaborative atmosphere. By combining the rigorous scientific method with astute conclusory analysis, an organization should make wise decisions.
he two fields are by no means in opposition. Instead, they complement each other perfectly. There is some crossover of data science and data analysis personnel.