The People Who Work for AI
AI success depends on people who prepare data, build access pathways, and validate results through human oversight. These human roles create structure, trust, and real business value in the age of AI.
DataCraft
Data Product
Data Product transforms data from stored assets into user-focused products, enabling real business value through clear ownership, usability, and AI-ready quality standards.
Time Series Data Management for AI Memory
AI memory relies on time series data design. By managing state over time, storing meaningful events, recording intent, and summarizing timelines, AI can remember context and work continuously as an intelligent agent.
Is 80% Accuracy Good Enough for NL-SQL?
NL-SQL accuracy matters more than it seems. This article explains why 80% accuracy is risky for real business analytics and how semantic layers, query structuring, and clarification loops enable near-100% reliable Natural Language to SQL systems.
AI Application Implementation Models
AI application implementation models explained: application-centric AI vs AI-centric orchestration, key differences in control, data management, scalability, and how hybrid architectures shape the future of enterprise AI systems.
Data Analysis Agents
AI-powered Data Analysis Agents automate the entire analytics lifecycle—from data extraction and analysis to visualization, validation, and insights—enabling faster, scalable, and more accessible data-driven decision making across organizations.
How AI Uses Data: Bring Data to AI vs. Bring AI to Data
Bring Data to AI centralizes data for scalable analytics and model training, while Bring AI to Data deploys AI where data is generated for real-time, secure decisions. Modern AI architectures increasingly combine both approaches.
A Clear View of Data: A Practical Data Visibility Model
Data visibility model for AI using Johari’s Window. Learn how known, unknown, used, and unused data impact data governance, analytics, and AI success, and why clear data visibility is essential for trustworthy AI.
Why Big Data Needs Smarter Management
Big data is rapidly expanding, and effective management with strong governance is crucial for future competitiveness.
How AI Understands and Uses Structured Data Effectively
AI can better analyze structured data with purpose-specific queries and AI-generated data catalogs, boosting performance in DB analysis and utilization.