What’s Next? Key Trends and Predictions for Data Science and Machine Learning in 2021

What’s Next? Key Trends and Predictions for Data Science and Machine Learning in 2021

Following the COVID-19 pandemic in 2020 that changed the world as we know it, one thing is still certain in 2021— companies will still be making a great effort to get machine learning and artificial intelligence initiatives into business decisions and company workflows. Data science, ML, and AI are beginning to drive all aspects of our society, and businesses are looking to prioritize and increase their investments in these technologies to cut costs, reduce risk, and increase efficiency. 

So looking forward to 2021, what are we going to expect when it comes to data science, machine learning, and artificial intelligence?

One thing that is especially clear is that now is the time to invest in artificial intelligence and machine learning. Companies that don’t invest in AI and ML will struggle to struggle to compete with others in their respective industries. A 2019 report from Accenture found that 75% of executives and business leaders believe they will risk going out of business in about 5 years if they don’t scale AI. Similarly, the survey found that 76% of executives and business leaders acknowledge that their company struggles to scale AI and ML initiatives across their companies. Companies are aware of the significance of incorporating AI/ML into their workflows and business decisions to reduce loss and optimize profits/efficiencies, yet they are failing to do so. A 2019 article published by Pactera and Nimdzi Insights found that 85% of AI initiatives are failing. Fast forward to 2021, the Algorithmia third annual report on “2021 Enterprise Trends in Machine Learning” displays how the priority and budgets for AI and ML are increasing significantly. Specifically, 76% of organizations are prioritizing AI/ML over other IT initiatives, with 64% of organizations increasing their AI/ML priority in the past year. In the light of the COVID-19 pandemic and its fiscal and economic impact on all industries, companies are showing a renewed sense of urgency to operationalize AI/ML initiatives. Even the White House is launching a new AI initiative office to “ensure America’s leadership in this critical field for years to come,” further demonstrating how AI is everywhere and is growing!

As a result of this increased desire to integrate AI/ML strategies into company workflows, 2021 is the best year to be a data scientist. Compared to last year, the Algorithmia report found that the average number of data scientists employed has increased by 76%. In prioritizing and investing in more AI/ML initiatives, companies are expanding their data science teams with the hopes of seeing quicker results. Along with creating larger data science teams and prioritizing AI/ML as a whole, businesses are also expanding their AI/ML use cases across a wide range of applications, with customer experience and process automation being the most common. Data science and machine learning are being incorporated in areas that have never been explored before. In light of the COVID-19 pandemic as well, organizations, specifically in the healthcare industry, are finding more unique use cases for machine learning and AI to combat the virus and change the face of Healthcare as we know it.

Overall, 2021 will be a breakthrough year for data science, machine learning, and artificial intelligence. As companies continue to invest more into this space, there will also be an increased demand for skilled and talented data scientists to lead the way. And as the pandemic rages, more changes will be made to our daily lives, which will also create new opportunities for the use of AI and Machine Learning to improve society as a whole.

For more of our blog posts and content, check out our website at The Data Standard, the premier community of data scientists, enthusiasts, and thought leaders. We aim to foster conversations and share insight among the leading professionals in data science through empathy and mindfulness during the pandemic.

About the Author

Koosha Jadbabaei is a Data Scientist and Technical Writer working with the Data Standard. Koosha is currently a student at the University of California, San Diego majoring in Data Science and minoring in Entrepreneurship/Innovation. Along with his work at The Data Standard, Koosha is an undergraduate researcher at UCSD’s Data Science Department, working to detect political bias and misinformation in Twitter Tweets through sentiment analysis and clustering. He is interested in data analysis, machine learning, and data visualization, and is passionate about using data to tackle difficult problems and make a positive impact on the lives of others.

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