“Data is a precious thing and will last longer than the systems themselves”, Tim Berners-Lee, inventor of the World Wide Web
“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades”, Hal Varian, Chief Economist, Google
The above statements encapsulate the promise and possibilities that data offers.
Many of the world’s thought leaders believe that data will unlock a huge transformation in the way that businesses operate and thrive in the 21st Century.
Data Proliferation
Data is information organized in a form that can be efficiently used by people or computers to make decisions. Recent technological advancements like social media, smartphones, the internet of things (IoT), and Artificial Intelligence have dramatically increased the amount of data being produced, a phenomenon known as ‘data proliferation’.
In 2021, the world created more than 2.5 quintillion bytes of data every day, the equivalent of more than 100 gigabytes per person per year. The US Chamber of Commerce estimates that 90% of the world’s data was produced in just the last 2 years.
Due to the amount of time people spend online, many routine daily activities lead to data creation: sending an email, sharing an update on Twitter, searching Google, sending a text message, or merely visiting a website. This new reality has led to the rise of ‘big data’.
The amount of data being created is now so vast that it can no longer be processed and analyzed using traditional methods.
In this environment, two important fields have emerged: Data Science and Data Analytics. Although both involve working with big data, there are important differences to be aware of if you are interested in pursuing a career in these fields.
Data Science vs Data Analytics
Data science is an umbrella term which includes data analytics. It refers to a collection of disciplines required to collect, store, analyze, and use big data: Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence.
Data science involves designing new processes for identifying patterns in big data (i.e. data mining), understanding important properties of the underlying data (i.e. data inference), and predicting the future behavior of individuals or groups (i.e. predictive modeling).
Data science is made possible by machine learning and artificial intelligence, which use advanced computer systems that can adapt and learn from data without prompts or explicit instructions. Although all data is field specific, for example health care data collected from a hospital, data scientists can often operate without specific domain expertise as they apply powerful statistical methods to uncover meaningful correlations in large datasets.
Data analytics is a subfield of data science that involves examining large data sets to identify trends, develop charts, and create visual presentations to help business leaders and government officials make better decisions. In this context, data analysts must have specific domain expertise so that they can identify trends, gain meaningful insight, and provide actionable recommendations.
Data analysts often work closely with key business functions including marketing, product development, information technology, finance, and management to identify consumer trends, anticipate issues, and present evidence-based solutions to support strategic decision-making.
Required Skills
The roles of data scientist and data analyst are both suitable for individuals with good problem solving and critical thinking skills.
A data scientist must be well versed in Mathematics and Statistics and have expertise in machine learning and software development in languages like Python and Java.
A data analyst must be able to undertake database management, data mining, data modeling, and data analysis, as well as create reports and visual presentations that effectively communicate key information. A data analyst would need to be comfortable using tools like SAS and languages like R and SQL.
Job Market Prospects
Data scientists and data analysts both appear to have extremely strong job prospects over the coming decade. According to the U.S. Bureau of Labor Statistics (BLS), the demand for data scientists, data analysts, and related occupations is expected to grow by more than 30% per year between 2019 and 2029. This is much faster than the average growth rate for all occupations of around 5%. In terms of pay, both roles offer highly competitive salaries. For example, in 2021 the median salary for data scientists in America was around US$100,000 per year. This compares favorably to the median wage for all workers of around US$45,000.
The bottom line
If you are a university student or young professional with an interest in numbers, statistics, and computer science, then data science and data analytics both offer promising career pathways with interesting work, high average salaries, and strongly growing demand coming from businesses and government.
Prateek Natani is an Associate Consultant at KPMG, and completed a Computer Science Engineering degree in June 2020. He is passionate about consulting and entrepreneurship, and his research interests include institutional strategy, technology, human development, and consumer behaviour.
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