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Data Application Scenarios

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

This tutorial focuses on the Data module in the SERVICEME NEXT platform, guiding users through two hands-on cases to master building data-driven intelligent applications. 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: Building BI Analysis Reports from Scratch​

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 chart recommendations 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: Building a Data Agent from Scratch​

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 to specified data sources (such as databases, APIs, or spreadsheets) in real time based on user 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 flows
  • Return graphical analysis results using chart components

This practice is suitable for scenarios such as building a "Sales Data Assistant", "Inventory Query Bot", or "Business Analysis Assistant", 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.