Overview

Overview

Feature Description

Self-service Data Retrieval refers to flexible report building and ad-hoc querying capabilities, allowing end users to build custom data reports based on templates through a visual interface, completing self-service data retrieval and ad-hoc queries.

Use Cases and Value

Self-service Data Retrieval effectively helps users with zero data development experience easily obtain data. Facing frequently changing data demands, there is no need to wait for the development department's schedule; drag-and-drop operations allow for simple and quick data retrieval.

• For Technical Staff: Reduce Tedious Work

Technical staff can use Self-service Data Retrieval (custom reports) to create basic analytical wide tables for business colleagues; business colleagues can select fields based on templates to build their own custom reports, meeting daily data retrieval and analysis needs.

From then on, technical staff no longer need to frequently create numerous reports for business colleagues to meet their analysis needs with different dimension combinations, saving time, reducing workload, and focusing on data architecture itself.

• For End Business Users: Lower the Barrier to Business Self-service Analysis

The data retrieval and analysis needs of end business users are often uncertain due to different purposes or rapid business iteration. Moreover, most end users focus more on business decisions and may lack technically related knowledge and skills, encountering obstacles during querying or analysis due to insufficient database knowledge.

Using the custom report module allows technical personnel with more database knowledge to design a "semantic layer," encapsulating data metric calculations within custom report templates. End business users only need to use the pre-arranged custom reports, select the required metrics, and complete their current data query tasks without worrying about the implementation of metric calculations.

• Low Cost, Short Cycle, High Output

Low production cost: No need to understand complex business analysis logic or master the UI capabilities for creating refined dashboards;

Short cycle: After completing the basic template setup, there is no need to spend too much time; a large amount of data analysis work can be performed based on datasets;

High output: Once completed, it can meet the combined analysis needs of numerous complex dimensions and metrics in future business development.

Getting Started

To help users systematically master the Self-service Data Retrieval feature, we have organized the following workflows for the two major business scenarios of creating and using Self-service Data Retrieval.

Building and Managing Self-service Data Retrieval Templates

• Target Audience: Enterprise IT personnel, data analysts, etc.
• Workflow:

For details, please refer to Creating Self-service Data Retrieval and Managing Self-service Data Retrieval.

Using Self-service Data Retrieval for Ad-hoc Queries

• Target Audience: End business personnel
• Workflow:

For details, please refer to Using Self-service Data Retrieval.