JD Retail Data Visualization Platform: Product Practice and Insights

The presentation introduces JD Retail's data visualization platform, outlining its four‑part structure: product capabilities, business empowerment case studies, platform construction challenges and outlook, followed by a Q&A session.

Product capabilities cover a matrix of tools: EasyBI (a drag‑and‑drop reporting and analysis platform), a low‑code visual composition system, and JDV (a large‑screen visual dashboard solution). Core functions include data connection to sources such as MySQL, Presto, ClickHouse, Elasticsearch and APIs; lightweight data modeling; rich visual component libraries; dashboard publishing, subscription, alerting, and embedding capabilities.

The platform enables visual presentation of data, interactive exploration, real‑time monitoring, business performance evaluation, and data‑driven decision making, supporting scenarios from internal data warehousing to real‑time streaming via Flink and OLAP queries.

Technical architecture is described in four layers: data connection, data modeling, visual configuration (including custom components, layout, filters, and DSL‑driven generation), and dashboard publishing/management. The low‑code platform builds on a React‑Webpack‑Node.js stack, offering modular components, state management based on Redux, and code generation via schema‑driven React code.

Business case studies illustrate how EasyBI and the low‑code platform empower retail, logistics, and store management by unifying data across domains, enabling multi‑dimensional analysis, and improving operational efficiency. Specific examples include multi‑domain reporting, pandemic impact dashboards, and automated insight generation using large language models and vector databases.

Challenges discussed include high entry barriers for low‑code adoption, the need for unified data access, performance optimization of query engines, front‑end rendering efficiency, system stability, and data security. Future strategies focus on product simplification, high reusability, modular “building block” design, and low coupling between components.

The Q&A addresses creators of EasyBI dashboards, low‑code platform adoption hurdles, automatic dashboard generation via LLMs and DSL prompts, data modeling options (SQL vs. visual drag‑and‑drop), and methods for evaluating report value and ROI.

Overall, the platform aims to create a one‑stop data visualization service that lowers the threshold for analysis, promotes a data‑driven culture, and supports scalable, modular, and reusable visual analytics across JD’s business units.