Big Data Governance Platform: 5-Layer Architecture, 9 Modules & 3-Phase Implementation

Core Goal

The platform aims to turn scattered, messy, dirty, and unsafe data into trustworthy, findable, usable, and controllable assets.

Four Problems to Solve

Data invisible : Unclear which systems hold data, who owns it, or where it resides.

Data inconsistent : Same metric defined differently across reports, causing conflicts.

Poor data quality : Null values, duplicates, format errors, untimely updates.

Data unsafe : Unclear permissions, high risk of sensitive data leakage.

Ultimate objectives: unified collection, unified standards, unified quality, unified catalog, unified service, unified security.

Five-Layer Architecture

1. Data Source Layer

Ingests business databases, files, APIs, logs, IoT, internet data, and third-party sources. Supports both batch and real-time ingestion.

2. Storage & Compute Layer

Follows a layered data warehouse model: ODS (raw) → DWD (detail) → DWS (summary) → ADS / data marts. Underlying storage can be data lake, data warehouse, or lakehouse depending on scale and real-time requirements.

3. Data Integration & Development Layer

Handles collection, cleansing, transformation, joining, masking, and distribution. Supports visual development plus SQL/script coding. Development and production environments must be isolated.

4. Governance Center Layer (Core)

Nine modules: metadata, data standards, data quality, data asset catalog, master data, data security, data lineage, task scheduling, monitoring & operations.

5. Service & Application Layer

Provides data catalog, API services, data request approval, dashboards, indicator analysis, tagging & profiling, model serving. Tailored entry points for business users, analysts, developers, and executives.

Two cross-cutting assurance lines: security system (authentication, authorization, audit, masking, encryption) + standards & governance system (policies, processes, accountability, assessment).

Core Functional Modules

1. Metadata Management

Automatically collects table structures, fields, types, constraints, ETL jobs, reports, indicators. Builds an enterprise "data map" supporting search, versioning, change tracking, and impact analysis.

2. Data Standards Management

Manages business terms, indicator definitions, code dictionaries, naming conventions, field standards. Key is enforcing standards down to tables and fields and validating deviations.

3. Data Quality Management

Covers completeness, accuracy, consistency, timeliness, uniqueness, validity. Rules include non-null, enum values, phone/ID format, cross-table consistency, volatility. Issues follow a closed-loop work order: detect → assign → rectify → recheck → archive.

4. Data Asset Catalog

Makes data "findable and understandable." Tables, indicators, tags, reports, APIs can be cataloged, searched, previewed, requested, approved, bookmarked, rated. Catalog targets business users, not just technicians.

5. Data Lineage & Impact Analysis

Tracks data from source to report, ideally at field level. Upstream view shows origin; downstream view shows affected reports — enabling rapid impact assessment when a field changes.

6. Data Development & Scheduling

Unified development, testing, release, scheduling, monitoring. Supports offline, real-time, quality audit, API invocation tasks. Includes dependency management, failure retry, backfill, alerting.

7. Data Security Control

Classification & grading, sensitive data discovery, dynamic masking, access control, operation audit, watermarking, encryption. Focus on ID numbers, phone numbers, bank cards, addresses.

8. Data Sharing & Service

Exposes data via API, file, subscription, push. Includes service catalog, request approval, call monitoring, traffic control, SLA monitoring to prevent uncontrolled data copies.

Three-Phase Implementation Roadmap

Phase 1: Inventory & Foundation

Current state assessment, organizational roles, standards & policies, metadata, data catalog, quality rules, core system onboarding. Goal: clear view of data landscape.

Phase 2: Focus on Key Scenarios & Close Loops

Select 3–5 high-frequency scenarios (e.g., operational analysis, risk control, customer profiling, regulatory reporting). Build cleansing & processing, quality audits, unified indicator system, master data, lineage, service enablement. Goal: produce first batch of trusted data.

Phase 3: Expand Scenarios & Build Operations

Roll out to more departments and subsidiaries. Establish data asset operations, value assessment, cost measurement, AI-assisted governance, open data sharing. Goal: sustain business value from data.

Organization & Governance Mechanism

Three-tier structure:

Decision Layer : Data Governance Committee — sets direction, allocates resources, defines assessments.

Management Layer : Data Governance Office — manages standards, quality, security, progress.

Execution Layer : Data Owners, Data Stewards, Developers, Business Experts — responsible for accountability and remediation.

Required policies: Data Standards Management Policy, Data Quality Management Procedure, Data Classification & Grading Policy, Data Sharing & Opening Process, Data Accountability Mechanism, Data Quality Assessment Method.

Success Metrics

Data visible : Core systems, tables, key fields entered into catalog.

Data findable : Business users can self-service locate needed data.

Data trustworthy : Quality issue closure rate and remediation rate improve.

Data consistent : Key indicator definitions unified; reports no longer conflict.

Data fast : Data request, API development, report delivery cycles shortened.

Data secure : Permissions, masking, audit fully covered; compliance risk reduced.

Industry-Specific Selection Guidance

Government / Group enterprises : Prioritize metadata, catalog, data exchange, security audit.

Finance / Healthcare : Prioritize standards, quality, classification & grading, lineage, compliance.

Manufacturing / Energy : Prioritize IoT real-time data, master data (equipment, materials), quality alerts.

Internet / Retail : Prioritize tagging, indicators, real-time computing, feature data.

Closing Statement

Platform value lies not in feature breadth but in turning "data no one trusts or uses" into "data that is owned, standardized, traceable when issues arise, and measurable for value."