How Big Data Is Fuelling The Future Workforce By: Kaylyn Frecker & Eva Bieniarz March 19, 2020 Updated November 4, 2020 Estimated reading time: 4 minutes. What is data science and why do we keep hearing that phrase? A full definition from IBM states that data science is “the process of using algorithms, methods, and systems to extract knowledge and insights from structured and unstructured data. It uses analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision making.” That’s a mouthful. But to put that into perspective, being a data scientist was named as one of America’s best jobs for 2020. Jobs in this field are also up by an impressive 29%. When IBM, Amazon, Microsoft and Facebook employ well over 1,000 data workers each, that means something. Nowadays, data is collected from every single activity we do, whether we know it or not, so it makes sense that there is higher demand for this data to be collected, aggregated and used to enhance numerous industries including healthcare, finance, and digital media. What’s more, according to the U.S. Bureau of Labor Statistics, 11.5 million data-related jobs will be created by 2026. Data is crucial in the 21st century, especially when it comes to understanding the analytics and building blocks of our world. Unfortunately, there could be a shortage of trained professionals to handle all of this data. The Demand For Data Is Increasing So, what’s pushing data to the forefront? For starters, the developing role of artificial intelligence and its use in data collection is not slowing down; a PwC report finds that artificial intelligence could add $15.7 trillion to the global economy by 2030. Put into perspective, a lot of the things around us are becoming automated — we can talk to our phones to set our alarms or reminders for us, we can instruct our Google Homes to play music, and we can even start our cars from inside the house. The world has never been so automated and there’s still much more to come. At the core of artificial intelligence is machine learning, which is a function that simply analyzes large chunks of data faster than humans ever could. “Machine learning has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced traditional statistical techniques.” Machine learning and data science have a lot in common. Consider shopping on Amazon or watching something on Netflix: The personalized recommendations we see is machine learning in action as well as data collection. Who’s Affected By Data? Artificial intelligence and big data are revolutionizing numerous industries by completely reshaping how they operate. One example is healthcare. A neat development within the industry was the creation of IBM Watson, an AI technology that “helps physicians quickly identify key information in a patient’s medical record to provide relevant evidence and explore treatment options. It then takes in a patient’s medical records and provides its evident-based and personalized recommendation fueled by information from a curated collection of 300 + journals, 200 textbooks, and 15+ pages of texts, [giving] doctors instant access to a wealth of information personalized [to the patient].” Apart from robots, there’s also the case for quicker diagnosis. According to Forbes, “AI algorithms diagnose diseases faster and more accurately than doctors. [For instance], in late 2019 Google’s DeepMind trained a neural network to accurately detect over 50 types of eye diseases by simply analyzing 3D rental scans.” Without AI, would this have even been possible? Certain cancers are also tough to detect, but artificial intelligence can bypass the hurdles much faster than humans. “AI algorithms can scan and analyze biopsy images and MRI scans 1,000 times faster than doctors, and these algorithms can diagnose with an 87% accuracy rate.” These examples are only the tip of the iceberg. Other aspects of healthcare to consider include drug discovery and personal health assistants. The benefits that machine learning has on our lives is endless, and now, we can begin to make sense of all the data that is collected and learn from it far more effectively than we have ever been able to. Finance is another industry to consider. A study called “Global AI in Financial Services Survey,” supported by EY and Invesco found that roughly “64% of respondents anticipate employing AI in all of the following categories: generating new revenue potential through new products and processes, process automation, risk management, customer service and client acquisition – within the next two years.” So, will this impact jobs? It depends on what you do, exactly. While it’s true that artificial intelligence and machine learning can take over certain tasks, they are still machines at the end of the day and will require humans to analyze the data and ensure that things are running smoothly. Cybersecurity is another major industry seeing massive changes. Image recognition? That’s something that didn’t exist a few decades ago. If you unlock your phone with your face, you are part of the change seen in this industry. Apart from just ourselves, the changes in cybersecurity have been employed on a governmental and organizational level. Think cyber attacks: they can be detected much quicker now, and this is a good thing. Computers can help us analyze data and identify a threat before it happens — they can also follow algorithms based on previous data and keep providing improvements to a whole security system. How Data Can Be Leveraged For Competitive Advantage So is data science useful for us when it seems like computers can do so much? Absolutely! Computers aren’t perfect, which is why people need to fill in the blanks. People can interpret the data and provide an empathetic perspective on what is found. With data scientist being the fastest growing job in America, there are set to be 1.4 million new job openings this year — and this isn’t just for the tech giants — this is everywhere. Learning and comprehending the skills needed doesn’t need to take years either. A great starting point can be to learn more about Python, Spark or even make the most of existing tools such as Excel and Tableau. Having an analytical skill set is a great way to set you apart from others in your field. Anyone can collect data, but the person who makes the most of that data is further ahead. Analytics have also become an integral tool for many companies — according to Forbes, “Companies can leverage data to improve cost savings, redefine processes, drive market strategy, establish competitive differentiators and, build an exceptional and truly personalized customer experience.” Widen Your Skillset And Try Something New So, whether you are interested in learning more about data and analytics or are just starting to do your research, it’s important to understand the skills needed for the future and technical skills top the list. Will you fall behind, or will you adapt to the needs of a changing workplace? Explore our suite of mentored, hands-on, accelerated learning programs designed to develop your skill set for the future of work.