More than 1,000 data professionals gathered in Tel Aviv for the sixth annual hayaData conference, held at the David InterContinental Hotel and organized by Israeli fraud-prevention company Riskified as a nonprofit community initiative. The event brought together executives and experts from companies including Amazon Web Services (AWS), Google, Wix, NVIDIA, Microsoft, monday.com, Databricks, Snowflake, HoneyBook, Cato Networks and AI21 Labs to discuss the growing role of artificial intelligence, data infrastructure and automation in the global economy.
The conference took place as demand for data processing and storage continues to accelerate, driven largely by the expansion of AI technologies and the infrastructure needed to support them. According to real estate services firm JLL’s 2026 forecast, the global data center industry will require nearly $3 trillion in investment by 2030 to accommodate approximately 100 gigawatts of additional capacity, effectively doubling the sector’s existing capacity. JLL projects the sector will grow at a compound annual rate of 14%, a trend expected to place growing pressure on electricity grids and increase demand for innovative energy solutions. Major technology companies are expanding their computing infrastructure through a combination of leased data centers and facilities they develop themselves.
Data and AI as strategic infrastructure
Noa Lubin, CTO at the National AI Directorate in the Prime Minister’s Office, emphasized AI’s strategic importance: “Artificial intelligence isn’t just technology — it’s a foundational infrastructure reshaping the rules of business, society and geopolitics,” Lubin said. “Leadership and sovereignty in this field are vital prerequisites for Israel’s national strength.” According to IDC figures cited by conference organizers, roughly 90% of the world’s data was generated within the past two years, while global data storage volumes approximately double every four years. The rapid growth presents challenges for companies seeking to process information efficiently while maintaining accuracy, security and reliability.
Agentic AI, data quality and trust
Guy Machlev, vice president of research and development at Riskified, warned that the rise of autonomous AI systems raises new requirements for reliable data. “The data landscape is at a critical turning point,” Machlev said. “The AI revolution doesn’t lessen the importance of data — it makes it more critical than ever.” He described a move “from systems that primarily analyze information to an era where AI agents make decisions and execute actions on behalf of users.” “This shift introduces new challenges in trust, identity and risk management, underscoring the vital need for high-quality data and precise, real-time decision-making,” he said. Machlev pointed to Riskified’s own experience: “Over the past five years, the volume of data we process at Riskified has grown more than fivefold, accompanied by significant improvements in processing speed and efficiency,” he said. “These capabilities serve as the foundation for navigating the new challenges of the agentic AI era.” Agentic AI refers to artificial intelligence systems designed to pursue goals and carry out sequences of tasks with varying degrees of autonomy, rather than simply responding to individual prompts. The technology is expected to expand the range of activities that companies can automate, while creating new challenges involving accountability, fraud prevention and the verification of automated decisions.
How AI is reshaping data roles
Amit BenDor, head of AI and data science at Unity, discussed how more accessible AI tools are changing the skills required of employees who work with data. “The data space is evolving rapidly, and AI is redefining job roles, scope of responsibility and the skill sets required for professionals,” BenDor said. “On one hand, more people across various roles can now build dashboards and execute tasks that once demanded dedicated expertise.” He said the democratization of data tools did not eliminate the need for specialists but shifted the emphasis toward deeper analytical judgment and the ability to use AI effectively. “Because of this democratization, two types of expertise become critical,” BenDor said: deep knowledge of a particular field to identify underlying problems, formulate relevant questions and draw meaningful conclusions, and proficiency in applying AI tools across the data ecosystem, including analytics, data engineering and data science.
The hayaData program included more than 50 technical presentations and sessions organized into four tracks—Data Engineering, Data Science, Analytics and Business Intelligence, and Data Products—offering practical guidance drawn from industry experience. This year’s event placed particular emphasis on the intersection of data and artificial intelligence, examining how AI is transforming the way organizations collect, build, manage and use information and addressing the changing roles of data professionals as companies increasingly integrate AI into their operations.
