Data Resource Survey AI Smart Body: Automating Data Governance

I. Traditional Data Governance Pains

As the era of data governance 2.0 arrives, traditional manual and semi-automated methods for data resource surveying reveal numerous pain points in increasingly complex data environments. These pains severely hinder the release of data value and the improvement of governance efficiency, while also exacerbating costs and risks. Specifically:

(1) Efficiency Low, Time-Wasting

Highly dependent on manual searches, recognitions, and records of data sources and metadata, offline survey results are difficult to quickly convert into online results, with the process being cumbersome and time-consuming. A single comprehensive survey may require several months or longer, making it difficult to perform frequent surveys, leading to significant resource consumption.

(2) Depth Insufficient, Insights Limited

Metadata management typically only records basic technical metadata such as table names, field names, and data types. However, there is a lack of deep understanding and recording of data content, business semantics, sensitive information, data quality, and value.

(3) Inaccurate, Low-Quality

Data environments (e.g., data structures, meanings) are constantly changing. Manual survey maintenance of metadata and directories is easily outdated, losing reference value, with manual record-keeping prone to errors or inconsistencies.

II. Intelligent Body Autopilot Route

To break through traditional data governance limitations, SunwayLink developed an AI smart body based on the company's SunwayLink platform. This intelligent body can automate data directory generation, content analysis, and metadata updates, effectively driving data governance models from "human governance" to "intelligent governance," significantly improving automation and intelligence levels.

(1) Data Directory Self-Generation

Based on collected metadata and offline survey results, the intelligent body can automatically extract key elements, quickly generating a data resource directory, thereby significantly shortening the survey period, reducing manual costs, and helping enterprises precisely grasp their data asset status.

(2) Data Content Intelligent Insights

Using NLP and LLM technology, the intelligent body can automatically infer field business semantics (semantic labels), recognize sensitive data types, identify data domains (customers, products, finances, etc.), and discover potential data quality issues (null values, abnormal value patterns). Self-generated or enriched business term descriptions can be generated, automatically analyzing data structures, business meanings, and bloodline relationships, enriching survey results.

(3) Metadata Intelligent Updates

Monitoring metadata changes, data distribution changes, the intelligent body can detect metadata deficiencies, inconsistencies, and outdated issues, and update metadata or trigger alerts according to AI suggestions, ensuring consistency between technical and business metadata, improving metadata accuracy and freshness.

III. Intelligent Body Implementation Route

(1) Text Analysis and Content Generation

Extracting non-structured document content information, recognizing text, images, tables, performing text content analysis based on natural language instructions, summarizing and abstracting content according to specific templates and formats, generating a data resource directory structure.

(2) Semantic Understanding and Relationship Analysis

Using NLP to automatically recognize data structures, understand code logic, extracting richer technical metadata (tables, columns, views, stored procedures, job dependencies), and preliminary business context (comments), performing content analysis, completing business metadata information, and generating bloodline relationships.

(3) Abnormal Detection and Dynamic Updates

Listening to data source metadata information, using LLM to detect metadata structure changes, configuration changes, semantic changes, performing abnormal scanning, recognizing metadata change points, updating changed metadata and triggering warnings.

IV. Intelligent Body Application Value

As an example of a certain military unit, after implementing this intelligent body:

(1) Survey Period Shortened: Single business domain data resource survey time reduced by over 60%, reducing manual labor for engineers and experts, and decreasing human involvement hours by 70%.

(2) Metadata Enrichment: Automatic filling rate of field-level business semantic labels, sensitive data identification, quality rules increased from 20% to 85%.

(3) Data Discovery Efficiency Improved: Average time for business users to find required data significantly decreased.

(4) Bloodline Coverage Rate Improved: Automatically constructed bloodline chain coverage rate of key data links increased from 10% to 75%.

(5) Automated Coverage Rate Improved: Intelligent body automated task steps accounted for over 80%.

Data resource survey is a critical link in data governance. The intelligent body not only upgrades technical capabilities but also revolutionizes corporate management paradigms. Based on SunwayLink, the data resource survey AI smart body is accelerating enterprise data governance processes and driving digital transformation success.