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Practical Tutorial

Practical Tutorial Overview: Activating Data Value with Intelligent Analysis and Connectivity​

This tutorial focuses on the Data module in the SERVICEME NEXT platform. Through two hands-on cases, it helps users master how to build data-driven intelligent applications using the platform. Whether you want to quickly generate visual reports or create intelligent Agents with data connectivity and analysis capabilities, this tutorial will provide you with clear ideas and practical methods.

📊 Case 1: Rapid Construction of Sales Intelligent Analysis Reports​

This practical case aims to help users quickly build an intelligent analysis report based on sales data, enabling visualization and natural language insights of sales data. Through this tutorial, you will master the following key operations:

  • Import or connect sales data tables (such as Excel, SQL, or third-party systems)
  • Quickly configure data fields, metrics, and dimensions
  • Apply intelligent recommended charts and natural language analysis
  • Publish and share interactive reports

This case is suitable for roles such as marketing operations, sales management, and data analysis, helping users more efficiently gain business insights, evaluate performance metrics, and support decision-making.

🤖 Case 2: Rapid Construction of Work Order Statistical Analysis Agent​

This case will guide you to build an intelligent Agent capable of dynamically accessing and querying data sources. Unlike static reports, this Agent can connect in real time to specified data sources (such as databases, APIs, or spreadsheets) based on users' natural language requests, and return structured results or generate visualized answers.

You will learn how to:

  • Configure data connectors to access business systems or databases
  • Design the Agent's data query intents and parameter recognition methods
  • Implement multi-turn interactive data Q&A processes
  • Return graphical analysis results using chart components

This practice is suitable for scenarios such as building a "Sales Data Agent," "Inventory Query Bot," or "Business Analysis Agent," significantly enhancing the intelligence level of data services.

Through learning this module, users can not only "see the data," but also make data "move" through intelligent Agents, transforming complex data queries and business insights into natural conversational experiences, truly unlocking the value of data assets.