JD Retail Data Visualization Platform: Product Capabilities, Business Enablement Cases, and Future Outlook

The presentation introduces JD's retail data visualization platform, outlining four main parts: product capabilities, business enablement case studies, platform construction challenges and outlook, and a Q&A session.

1. Platform Product Capabilities – The platform offers a product matrix that includes EasyBI (a drag‑and‑drop visual report builder), a low‑code visual composition platform, and JDV (a large‑screen visual dashboard tool). EasyBI supports multi‑source data connections (MySQL, Presto, ClickHouse, Elasticsearch, APIs, uploads), lightweight data modeling, rich visual components, dashboard publishing, subscription, alerting, and embedding. The low‑code platform provides a visual orchestration system built on React, Webpack, and Node.js, with a state‑management framework extending Redux, layout and component composition, and code generation based on schema. JDV enables one‑click large‑screen creation with numerous chart templates, supporting real‑time data refresh and integration with other JD systems.

2. Business Enablement Cases – The platform powers multi‑domain, multi‑scenario data integration, such as unified store‑level sales, inventory, and supply‑chain analysis via EasyBI; scenario‑driven analysis through the low‑code platform; and high‑impact visual dashboards (e.g., pandemic impact board, merchant service board) built with JDV. These applications improve decision‑making, operational efficiency, and strategic insight across e‑commerce, logistics, and city‑level digital management.

3. Platform Construction Challenges & Outlook – JD aims to build a one‑stop data visualization service with a modular “big platform” architecture: a unified data source and ingestion layer, a middle layer of composable product capabilities, and an upper layer of business‑scenario solutions. Strategies focus on product (low‑threshold, high‑reuse visual tools), technology (query acceleration, front‑end rendering, security), and service (data‑driven operations, training, certification) to foster a culture where every employee becomes a data analyst.

4. Q&A Highlights – Answers cover EasyBI creator roles, low‑code adoption challenges, automatic dashboard generation using large language models and DSL prompts, data modeling options (SQL or visual drag‑and‑drop), and methods for evaluating report value and ROI.

The session concludes with a summary of the platform’s achievements and future direction.