DataFocus_Cloud's profile picture. company name:  DataFocus .PTE .LTD   location:Singapore, address:5001 BEACH ROAD #07-37 GOLDEN MILE COMPLEX SINGAPORE

DataFocus Cloud

@DataFocus_Cloud

company name: DataFocus .PTE .LTD location:Singapore, address:5001 BEACH ROAD #07-37 GOLDEN MILE COMPLEX SINGAPORE

Data shouldn’t hide behind SQL. Business questions deserve natural answers. 💬 Talk. Ask. Analyze. The future of Business Intelligence is conversational. #AI #BI #DataAnalytics


AI can sound confident — even when it’s completely wrong. That’s AI hallucination: fluent, but false. In data analytics, one “hallucinated insight” can cost real money. 🧠 #AI #DataAnalytics


I just published Deep Dive into Text-to-SQL Hallucinations in Large Language Models: Challenges, Impact, and… medium.com/p/deep-dive-in… #dataanalysis #dataanalytics #datavisualization #aihallucination


DataFocus reframes the problem with a dual-engine architecture: XiaoHui AI: interprets natural language into structured keywords humans can read and verify. FocusSearch: a deterministic parsing engine that converts those keywords into accurate SQL. #dataanalysis #dataanalyst


Common fixes add layers: RAG retrieves schemas and examples; Agentic flows generate, validate, then regenerate. They help, but introduce new costs: multiple model calls (latency + $$$), heavy index maintenance, and residual security risk (free-form generation still leaks).


Common fixes add layers: RAG retrieves schemas and examples; Agentic flows generate, validate, then regenerate. They help, but introduce new costs: multiple model calls (latency + $$$), heavy index maintenance, and residual security risk (free-form generation still leaks).


Text-to-SQL promises true data democratization: ask in plain language, get answers without writing SQL. But as LLMs became the engine behind it, a core flaw surfaced — hallucinations. Models can confidently generate queries that don’t match your schema, business logic, or intent.


I just published AI-Driven Paradigm Shift in Data Analysis: How Focus Search Fundamentally Solves the Text-to-SQL… medium.com/p/ai-driven-pa…


I just published Collaboration in Data Analysis: Why Teams Need a Shared AI Assistant medium.com/p/collaboratio…


I just published Ask Questions, Get Insights: The Power of Natural Language in Data Analysis medium.com/p/ask-question…


I just published From Dashboards to Data Applications medium.com/p/from-dashboa…


I just published Why Accuracy Matters in AI Data Analysis medium.com/p/why-accuracy…


One type of chart a day, today we have a histogram. It is particularly useful for understanding the shape, central tendency, and spread of a dataset. #dataanalytics #dataanalysis #dataanalyst #datavisualization #datavisualisation #visualization #visualisation #dataviz #charts


One type of chart a day, today we have a chord diagram. It shows relationships and connections between entities in a dataset, and is particularly effective for illustrating complex relationships. #dataanalytics #dataanalysis #dataanalyst #datavisualization #datavisualisation


One type of chart a day, today we have a parallel coordinate chart. It is suited for exploring and comparing multidimensional data and helps gain deeper insights into complex data relationships. #dataanalytics #dataanalysis #dataanalyst #datavisualization #datavisualisation


One type of chart a day, today we have an extended Sankey diagram. It is similar to Sankey diagram, the difference is the extended one requires 3 attribute columns, rather than 2 attribute columns and 1 measure column. #dataanalytics #dataanalysis #dataanalyst #datavisualization


One type of chart a day, today we have a Sankey diagram. It represents the flow of data, energy, or resources between different entities. #dataanalytics #dataanalysis #dataanalyst #datavisualization #datavisualisation #visualization #visualisation #dataviz #charts #graphics


United States トレンド

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