Data lakehouse provider Starburst has outlined the key data management targets and practices driving AI projects, based on research conducted among enterprises.
It commissioned a report from TheCUBE Research, which surveyed 300 IT professionals from diverse industries across the US and Western Europe, confirming the “critical role” of real-time hybrid data access and “robust” security in successful AI implementations.
“Real-time data access and robust security are paramount in the successful deployment of AI technologies,” said Shelly Kramer, managing director and principal analyst at TheCUBE Research. “The insights from this comprehensive report provide a roadmap for businesses to align their data strategies with their AI innovation goals.”
The report found strong AI adoption intent, with 87 percent of organizations expressing a “strong” or “very strong” desire to implement AI within the next 12 months, with “significant progress” reported by 86 percent of respondents.
However, in terms of technical obstacles, 52 percent of organizations said they faced “significant hurdles” in organizing structured data for machine learning with AI applications, and 50 percent cited difficulty in preparing unstructured data for retrieval-augmented generation (RAG) in AI deployments.
Additionally, 49 percent said aligning business intelligence metrics with features required for predictive analytics was a challenge, and 41 percent said it was tricky to use LLMs to refine and extract structure from semi-structured information for use in SQL DBMSes.
The most significant operational barriers to accessing high-quality data for AI projects are data privacy/security concerns (28 percent) and data volume (25 percent). Other barriers cited by respondents include insufficient data quality or reliability (17 percent), and a lack of the right tools and talent – both 11 percent.
Almost two-thirds (62 percent) of those surveyed highlight real-time data access as “critical” for AI success. Building a data-driven culture across companies is also key. Strategies such as increasing awareness of data’s value (69 percent of respondents), fostering cross-functional collaboration (66 percent), and building that data-driven culture (61 percent) were identified.
Other key trends in data management are shaping the AI landscape, with 52 percent of respondents adopting data governance and federated data access strategies to improve data quality and accessibility across systems, including on-premises and in the cloud.
In addition, 59 percent are using cloud-based platforms for scalability, and 61 percent are using agile methodologies for data project management.
“With our advanced and user-friendly open hybrid lakehouse platform, customers can navigate the complexities of data management with greater ease, efficiency, and accuracy, to drive transformative AI outcomes,” said Justin Borgman, co-founder and CEO of Starburst.
To help it scale up, Starburst recently appointed Steven Chung as president, Tobias Ternstrom as chief product officer, and Adam Ferrari as senior vice president of engineering.
