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How Will Data Science Shape the Future of Work?

Due to the increased use of social media, the IoT, and multimedia, the volume of data collected by businesses has reached an overwhelming amount. The data science platform market is expected to reach $298.16 billion by 2029, as the need for businesses to better exploit collected data grows. Leaders and C-level executives need better data science strategies to keep up with this widespread digital transformation. In this post, we’ll look at how data science will change the future of work:

Data Science and Education


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While data is having a huge impact on the workplace, it is also changing how future employees study and train for their chosen careers due to the greater focus on data at all levels of education. As early as elementary school, there are movements in place to teach elements of data science, such as the collection, organization, and communication of data.

However, it is in higher education that the biggest shift to data is happening with more students taking a dedicated data degree. Due to the demand for more data experts, more top universes are also offering data degrees as online courses. An online degree in data science follows the same curriculum as a traditional program, with a focus on Python, SQL, R, predictive modeling, and machine learning, while also allowing students to choose a more personalized education. This allows them to broadly study the essential skills needed to meet the demand of more than two million open jobs asking for analytics skills. Data science equips students with the skills needed to interpret and translate large volumes of data into actionable insights for businesses, becoming an in-demand pursuit that can lead university graduates to different opportunities.

At a career level, data science is also changing the way professionals train or upskill, even in areas that aren’t necessarily focused on data science. HR managers use big data tools and techniques to optimize employee management, engagement, and productivity tracking. Using data science, they get better insights into employees’ strengths and weaknesses to improve productivity.

 

Data Science and Hiring Practices

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With an increased number of women and girls in STEM, it’s essential to consider the impact of data science on hiring practices. In our recent roundup post, we noted the 12 best communities for women in tech and AI, as part of the 22% of women representation among AI professionals in the industry.

While the numbers suggest there are still some ways to go to truly achieve gender diversity in the tech industry, the rise in women-led communities such as Girls in Tech and Women Who Code marks a promising start. In data science specifically, the need for more women data scientists points to the threat that is biased data. Citing Amazon’s early attempt at a computer program that helps in hiring decisions, the use of submitted resumes — dominated by resumes from men — taught the program that male candidates were preferable to women. While the company ceased using the program for screening candidates, it is an example of the harm that partial data, especially in combination with AI, can do to hiring practices.

Investing in the education, training, and hiring of women data scientists can help solve the problem of inherent and unconscious biases in traditional recruitment practices. The more diverse the team that works with data, the more inclusive the actionable insights drawn from the data will be.

Data Science in the Future Workplace

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In the ever-changing future of work, data analytics and data science roles expand according to the technologies that evolve alongside them. As we introduce newer platforms and tools into work and business processes, more data will be generated — data that needs to be organized, stored, and analyzed to be deemed valuable.

As businesses become more dependent on data science, the democratization of data becomes increasingly important. When data can efficiently traverse across departments in a corporation, it can foster a productive culture of collaboration and prioritize best practices based on insights and trends in the data. On top of a more productive workplace, this also means businesses can optimize their approach to improving their products and services.

In a world where most businesses claim to make data-driven decisions, democratizing data relies on companies letting each employee handle data in real time. Proper security and privacy measures in place make the future of work more accessible to roles outside of the data science team. Part of making data accessible to all employees comes in the rise of no-code and low-code applications and software for non-technical employees, regardless of industry.

Finally, as part of the democratization process, new and emerging technologies must be accessible to consumers. This accessibility, when paired with applications that promote collaboration and efficient communication, provides consistency across large volumes of usage data so that organizations can create a tailored consumer experience.

Data Science Across Industries

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Outside of business, data science plays a prominent role across other major industries. Notably, data science is essential in the promotion of sustainable development goals. Data scientists can gather insights on poverty and hunger reduction using satellite imagery, as well as research the ocean’s largest offshore plastic zone using data from image recognition software.

Data science and analytics also played a big part in the Covid-19 pandemic, allowing healthcare organizations and government agencies to track cases and the spread of infection, which led to more effective health solutions and policies. Similarly, data science was also used to determine demographics at the highest risk of contracting the virus, helping health officials make vaccination policies that prioritize those most in need of them.

Meanwhile, post-pandemic, data science and advanced analytics are cited as key to performance improvements in the aviation industry. Operations such as the maintenance of the baggage handling system, particularly in airports that have a large digital infrastructure in place, rely on data analytics to ensure high efficiency. This also allows the airports to run with a small operations team. Similarly, passenger processing, arrivals and departures, information distribution, and air traffic control use integrated dashboards for the handling and organization of shared data analytics.

Conclusion

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Ultimately, data science has become a set of practices that are essential across businesses and industries worldwide. As work becomes more digital, data science and analytics become necessary for organizations to make sense of the large amounts of data they are collecting. Even among non-technical employees and consumers, data science can help ensure processes and functions are optimized and that the best data-driven decisions are made.

about the author

Romy Sharpe is a freelance writer interested in topics such as digital transformation and the impact of emerging technologies on the future of work. Gail is also passionate about workplace diversity and inclusion; she advocates supporting women-led businesses and endeavors. When not busy writing (and reading), Gail enjoys walking her three Labradors and biking with her children.

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