Despite today’s data scientist talent shortage, organizations are not holding back from investing in Data Science initiatives. 

A recent survey produced by my company in collaboration with TechTarget’s Enterprise Strategy Group shows that most organizations understand how to overcome today’s talent shortage. 87% of respondents said that building Data Science skills across domains and lines of business departments is a critical part of their Data Science strategy. 

The survey also indicates that organizations are eager to adopt AI and machine learning technologies. The hype surrounding AI solutions is exciting, but in order to make strategic long-term use of these emerging technologies, organizations must first make foundational improvements to their relationships with their data. 

3 Key Trends Impacting the Data Science Industry

In 2023, organizations faced technological advancements and market shifts that made continuous adaptation and innovation critical. Consequently, both business leaders and IT professionals had to follow Data Science trends vigilantly in order to optimize outcomes with their data. This commitment to staying informed is expected to remain a best practice in 2024. 

With that in mind, here are three trends gleaned from our survey, each of which sheds light on the current Data Science landscape:  

Data Science and Machine Learning Budgets Are Increasing

Nearly all (92%) of organizations surveyed saw a year-to-year increase in budget allocation for Data Science and machine learning projects and initiatives. 

With budgets on the rise after a challenging few years, some companies are ready to spend. Nearly a quarter of organizations (24%) intend to allocate at least $1 million toward people, processes, and technologies associated with Data Science and machine learning in the coming years. 

Increased financial commitment signals that business leaders recognize Data Science’s ability to boost operational efficiency and power more informed decision-making, predictive analytics, and innovative product development. This resource allocation underscores the value organizations are seeing from investments in Data Science and machine learning – as well as the pivotal role these capabilities will continue to play in driving future success and company growth.

Talent Gaps Remain a Bottleneck for Organizations

While budgets are on the rise, more than a quarter of organizations (27%) shared that a lack of skilled talent stands in the way of developing and implementing Data Science projects.

Without the right talent in place, organizations suffer from limited data expertise and slow project implementation and execution. These hurdles introduce complex risks and delays, impacting the organization’s ability to achieve desired business results from data-driven investments and operationalization.

Whenever possible, organizations should invest in employees who know how to make sense of rich data. But education is only part of the equation. Modern data scientists also require a strong understanding of the business to know where their work stands to make the greatest impact.

However, with too few data scientists to meet current industry staffing demands, business leaders must simultaneously adopt analytics tools to help close the talent gap. In combination with strategic hiring, low-code analytics tools can immediately democratize data work and enable every user across the organization to leverage data in the capacity required by their department and role. 

Making data intuitive to work with, regardless of the employee’s background, frees up available data scientists to focus on more complex responsibilities.

Open Source Drives Innovation  

88% of organizations agree that open-source solutions are critical for innovation. Additionally, over a quarter of respondents (26%) cite compatibility with open-source technologies as an important factor to consider when making purchases to support Data Science initiatives at their company.

These statistics signify a growing trend toward larger-scale open-source adoption in the future. Organizations are increasingly embracing open-source solutions to better facilitate collaboration, flexibility, transparency, and cost savings. 

Additionally, open source unlocks access to an active community of developers, enthusiasts, and experts who can enhance the software, offer working examples, and help foster innovation and continuous improvements.

Make Data Accessible to the Entire Workforce

While investments in Data Science and machine learning projects are increasing in the long term, organizations still face short-term challenges when developing and implementing Data Science processes, including talent shortages.

However, by democratizing access to data tools, organizations can empower their workforce to make informed decisions and extract valuable business insights, maximizing the potential of their data-driven initiatives. 

Once these improvements are made, companies will be better positioned to make use of emerging solutions like AI in ways that are both strategic and safeguarded, today and in the years to come.