The biggest challenges to the future of data science are viewed as increasing security issues, a skills shortage, and moral quandaries.
The yearly 2022 State of Data Science report, published by data science platform supplier Anaconda Inc., details the broad trends, possibilities, and perceived barriers that the fields of data science, machine learning (ML), and artificial intelligence (AI) are confronting. Three cohorts of academics, business people, and students served as the target audience for the global study, which focused on the open source community.
Open source software was created by and for developers, but it is also a vital part of the development of commercial software and the cornerstone of continued commercial innovation. The affordability of open source software and its rapid rate of innovation, according to 20% of survey respondents, are two of its most coveted benefits. When asked about the main threats to ongoing innovation and growth within the open source community, respondents focused on a variety of issues:
Open Source Security Concerns are Increasing
Given the incidents that have afflicted the industry over the past year, such as the Log4j hack and the rise of protestware, open source security remains a top concern. As a result, 40% of professional respondents said that their companies’ use of open source software had decreased over the previous year due to security worries. Furthermore, according to 31% of professionals, the open source community’s largest problem right now is “security vulnerabilities.”
Of the 8% of respondents whose firms do not use open source software, 54% stated that the main reason is concern over potential vulnerabilities, exposures, and hazards. This represents a 13% rise over the 2021 report and confirms the industry’s increased security awareness in 2022.
Organizations Are Stressed by a Talent Shortage
Organizations trying to scale their data science initiatives and quicken technological developments and acceptance have encountered difficulties due to a skills scarcity. 64% of professional respondents stated that they were most concerned about their organization’s capacity to attract and retain technical talent. 90% of professional respondents said their organisations are concerned about the potential consequences of a talent shortage. One of the main obstacles to the successful organisational adoption of data science, according to 56% of respondents, is a lack of skill or manpower in the field.
More attention is needed for regulations, bias, and ethics, particularly in education
Although ethical issues with AI, ML, and data collection are more prominent than ever in the public eye, more work has to be done in this area. 70% of professional respondents said they would support increased funding for STEM and tech-based education, and 75% agreed that the government should play a bigger role in fostering technological innovation and production. In contrast, only 19% of student respondents reported that they are currently taking ethical seminars in AI/ML/data science lectures, and 32% reported that they have only seldom or never learned about prejudice in these courses. These results show that educational institutions must modify their learning curricula to better reflect and prepare those entering the industry and influencing the direction of data science.