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On October 28, 2026, Miles summarized an eight-step multimodal data cleaning solution for FDE implementation projects at beauty companies. The solution processes text, image, audio, and video data collected from Xiaohongshu, Douyin, WeChat Channels, and Weibo through eight steps: defining data specifications, preserving original content, mapping data relationships, multimodal normalization, conflict and quality cleaning, generating structured data, performing acceptance checks, and storing data by rules and versions. The original text emphasizes that cleaning results must not overwrite the original text, and that OCR, ASR, and model labels serve only as newly added results; when ASR and OCR values conflict, the original evidence is traced back, and if it cannot be confirmed, the item enters a pending-verification queue. This pipeline has also been organized into a general solution that can be reused in scenarios such as consumer goods insights, automotive word-of-mouth, restaurant site selection, and brand public opinion.
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Miles summarizes the eight-step multimodal data cleaning solution for FDE at beauty companies
Source:Smart City Industry Analysis
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Oct 28
Smart City Industry Analysis
A Beauty Company's FDE Implementation in Practice: An Eight-Step Pipeline for Cleaning Massive Multimodal Data into Trustworthy Data
A beauty company's FDE implementation project processes text, image, audio, and video data collected from Xiaohongshu, Douyin, WeChat Channels, and Weibo through eight steps: defining data specifications, preserving original content, mapping data relationships, multimodal normalization, conflict and quality cleaning, generating structured data, performing acceptance checks, and ingesting according to rules and versions. The original article emphasizes that cleaning results must not overwrite the original text, and that OCR, ASR, and model labels are only added results; it also describes the practice of tracing back to the original evidence when ASR and OCR values conflict, and placing cases that cannot be confirmed into a pending-verification queue. This pipeline has also been organized by the author into a general solution reusable in scenarios such as consumer goods insights, automotive word-of-mouth, restaurant site selection, and brand public opinion.