Knowledge structures and techniques have undergone significant transformations during the AI period, driven by advances in machine learning, natural language processing, and data analytics. With the rise of big data, AI has enabled organizations to analyze vast amounts of information, uncover patterns, and make more informed decisions.
Tapping into AI’s Full Potential
Identifying the Crucial Elements of Achievement
- For constructing modern architectures, a multifaceted approach is crucial, as IT executives acknowledge the importance of data lakes or lakehouses in processing enormous amounts of unstructured and semistructured information necessary for AI model training. Two-thirds of respondents concurred that knowledge lakehouses played a crucial role in simplifying pipeline complexity.
- A staggering 90% of survey participants grasped the critical importance of harmonizing their data knowledge lifecycle within a unified framework, recognizing it as a vital component of effective analytics and artificial intelligence applications. Almost 46% of surveyed IT leaders reported that their teams interact with every stage of the information lifecycle process. IT leaders can unlock AI-driven innovation by achieving seamless management and unobstructed visibility across all informational aspects.
- Going forward, a holistic approach to knowledge management – integrating both on-premise and public cloud-based infrastructures, along with cutting-edge technologies – appears poised to be the most viable and effective strategy from a long-term perspective. While only a third of respondents currently leverage multicloud or hybrid knowledge architectures, a staggering 93% concur that such capabilities are crucial for companies to thrive in today’s dynamic environment.
