KNIME & ESG’s State of Data Science and Machine Learning Report Highlights Critical Opportunities for More Effective AI & ML

As organizations invest heavily in AI initiatives, the new report outlines the most pressing challenges and opportunities currently faced by data professionals and IT/business decision makers

Today KNIME, the software company focused on making working with data intuitive, published new research findings in collaboration with Enterprise Strategy Group (ESG). The State of Data Science and Machine Learning Report shows how organizations are prioritizing, investing in and operationalizing data science, machine learning and AI, and specifically cites the most pressing challenges in the data science process and how to address them.

Advancements in AI have moved data-driven decision making to the top of the agenda for virtually every organization. That’s why KNIME and research firm ESG surveyed more than 350 data scientists, business and IT decision makers involved with data science and machine learning technology and processes to show the current state of AI and machine learning operations (MLOps) initiatives.

The report identifies organizations’ investment plans, key objectives, stakeholders, priorities, and challenges relating to their current data science and machine learning initiatives. Key findings include:

• Investments in data science point to staggering growth, but a lack of skilled talent presents a challenge – While 92% of organizations saw a YoY increase in budget allocation for data science and machine learning initiatives, nearly a third of organizations (27%) say a lack of skilled talent stands in the way of developing and implementing data science projects.
• The shortage of data scientists isn’t holding organizations back from embracing AI and data science – To alleviate pressures of the shortage of data scientists, organizations are empowering more stakeholders to build, deploy and manage models. In fact, 87% of organizations agree that building data science skills across domains and business users will be critical to foundational data science strategies. To best support this shared responsibility model, 73% of these organizations have data science teams mostly, if not completely, centralized, meaning one data science team is responsible for supporting all data science-related projects across the organization.
• Organizations are prioritizing open source capabilities to make data science pervasive – Over three quarters (88%) of organizations cite open source as critical for innovation, and over a quarter consider compatibility with open source technologies one of the most important factors to consider when making purchases, likely foreshadowing a larger open source deployment trend moving forward.

“The survey results confirm that there are not enough data scientists to meet the demands of businesses looking to adopt the latest and greatest AI and ML technologies,” said Michael Berthold, founder and CEO of KNIME. “This is why it’s critical to implement analytics tools that make working with data intuitive. When we allow people to focus on what truly matters, everybody is enabled to work with data at their level of expertise and we free up data scientists to take on cutting edge technology. Only then will we see these investments moving the needle for enterprises.”

Later this fall, KNIME’s CEO Michael Berthold and ESG’s Principal Analyst Mike Leone will be hosting a webinar to discuss the findings in-depth. Those interested in tuning in can register to attend the webinar here.

To see an eBook with a summary of the findings from the State of Data Science & Machine Learning survey, please visit here.

About KNIME

KNIME helps everybody make sense of data.

Its free and open-source KNIME Analytics Platform enables anyone--whether they come from a business, technical or data background–to intuitively work with data, every day.

KNIME Business Hub is the commercial complement to KNIME Analytics Platform and enables users to collaborate on data science and share insights across the organization.

Together, the products support the complete data science lifecycle, allowing teams at all levels of analytics readiness to support the operationalization of data and to build a scalable data science practice.

View source version on businesswire.com: https://www.businesswire.com/news/home/20231011239046/en/