Enterprise Data Assetization Governance Service

Service Overview

Data Middle Platform and Data Governance Service is a professional service designed for enterprise-level clients. It aims to help enterprises build a unified, standardized, and high-quality data asset system, addressing core pain points such as data silos, poor data quality, and lack of data standards, ultimately achieving manageable, shareable, analyzable, and operable data.

The core value of this service is: Transforming scattered and chaotic enterprise data into trustworthy and usable strategic assets. Through a systematic data governance methodology combined with a mature data middle platform technical architecture, we provide end-to-end services from data collection, storage, and governance to application. Unlike standalone data governance tools or data middle platform products, we emphasize the practical implementation of "service"—our senior data expert team delves into the client's business scenarios to tailor data governance strategies and middle platform construction plans, ensuring the service outcomes genuinely support business decisions and innovation.

This service is particularly suitable for: Medium-to-large enterprises with large data volumes, complex data types, and the need to integrate data from multiple business systems, as well as organizations advancing digital transformation and aiming to build a data-driven culture. Whether in finance, retail, manufacturing, or the internet industry, if you face challenges of chaotic data management and difficulty unlocking data value, this service can provide practical solutions.

Service Content

Clients will receive the following clear and verifiable deliverables and service content:

Basic Services

1. Data Current Status Assessment Report: A comprehensive inventory of the client's existing data assets, including data source sorting, data quality assessment, and data standard status analysis, resulting in a detailed assessment report that identifies governance priorities.
2. Data Governance System Design Plan: A complete set of institutional documents, including data standard systems, data quality rules, data security policies, and data lifecycle management specifications, ensuring governance work follows established guidelines.
3. Data Middle Platform Architecture Design Plan: Based on the client's business needs and technical status, design the middle platform technical architecture, including the data collection layer, storage and computing layer, and data service layer, outputting architecture design documents and selection recommendations.
4. Data Model and Standard Definition: Define core business data models (e.g., customer, product, order subject areas) and establish unified data standards (including coding specifications, naming rules, field definitions, etc.) to ensure cross-system data consistency.
5. Data Quality Cleansing and Governance Implementation: Perform data quality cleansing, deduplication, and completion governance operations on key data domains, providing a before-and-after data quality comparison report to quantify governance effectiveness.
6. Data Service Interfaces and APIs: Provide standardized data service interfaces (e.g., data query, data subscription, data synchronization) to support business systems in quickly accessing middle platform data.

Value-Added Services (Optional)

7. Data Governance Platform Deployment and Configuration: Assist clients in deploying data governance tools (e.g., metadata management, data quality monitoring, data lineage analysis platforms) and performing initial configuration.
8. Data Operations Training and Knowledge Transfer: Provide specialized training for the client's team on data governance and middle platform operations, including methodology, tool usage, and daily maintenance, ensuring the client has independent operational capabilities.
9. Continuous Optimization and Maintenance Support: Provide remote or on-site support for a certain period after service delivery, assisting clients in resolving issues related to data governance and middle platform operations, and performing continuous optimization based on business changes.

Delivery Process

This service adopts a standardized four-phase delivery process to ensure orderly project progress and controllable risks:

Phase 1: Research and Assessment (2-4 weeks)

• Key Activities: Conduct in-depth interviews with the client's business and IT teams to understand the data status and business needs; perform data source inventory and initial quality assessment.
• Participants: Client's data lead, business key users; our senior data governance consultant, business analyst.
• Deliverables: "Data Current Status Assessment Report", "Project Implementation Plan".
• Milestone: Assessment report review approved, project officially launched.

Phase 2: Solution Design (3-6 weeks)

• Key Activities: Based on the assessment results, design the data governance system and data middle platform architecture; define data standards and models; develop an implementation roadmap.
• Participants: Client's technical lead, business experts; our architect, data governance expert.
• Deliverables: "Data Governance System Design Plan", "Data Middle Platform Architecture Design Plan", "Data Standard and Model Definition Document".
• Milestone: Solution review approved, entering the implementation phase.

Phase 3: Implementation and Governance (4-8 weeks)

• Key Activities: Perform data cleansing and governance implementation according to the plan; deploy core components of the data middle platform; develop data service interfaces.
• Participants: Client's IT operations, data administrators; our data engineer, development engineer.
• Deliverables: Governed data assets, data service APIs, governance effectiveness comparison report.
• Milestone: Data governance results accepted, middle platform functionality tested and passed.

Phase 4: Delivery and Training (2-4 weeks)

• Key Activities: System integration testing and User Acceptance Testing (UAT); deliver all documents and code; conduct knowledge transfer and training.
• Participants: Client's end-users, operations team; our training instructor, project manager.
• Deliverables: "Project Acceptance Report", "Operations Manual", "Training Materials".
• Milestone: Project formally accepted, entering the maintenance support period.

Note: The above timelines are typical project references. Actual timelines may be adjusted based on project scale and complexity and will be confirmed with the client at project initiation.

Service Commitment

We provide the following quantifiable Service Level Agreements (SLA) to ensure the quality and timeliness of service delivery:

Commitment Item	Specific Metric	Description
Project Delivery Timeliness	≥95%	Ratio of on-time delivery according to project plan milestones
Data Quality Improvement Rate	≥80%	Improvement in completeness, accuracy, and consistency of key data domains (e.g., customer, product) after governance compared to before, not less than 80%
Issue Response Time	Within 4 working hours	Initial response to technical issues or change requests raised by the client during the service delivery period within 4 working hours
Issue Resolution Time	2 working days for normal issues, 1 working day for urgent issues	Provide solutions or complete fixes within the specified time based on issue severity level
Deliverable Acceptance Rate	100%	All deliverables must be formally accepted by the client, ensuring compliance with agreed standards
Satisfaction Commitment	≥90%	Client satisfaction survey score not less than 90 points (out of 100) after project completion

Note: The above SLA metrics are general commitments. Specific metrics can be fine-tuned in the contract based on actual project circumstances. If committed metrics are not met, service compensation or fee reduction will be applied according to the contract terms.

Team Qualifications

This service is delivered by an experienced and professionally certified team specializing in data governance and middle platform construction. Core members possess the following backgrounds:

• Team Size: The project delivery team typically consists of 5-10 members, including 1 Project Manager, 1 Senior Data Governance Consultant, 1 Data Architect, 2-3 Data Engineers, 1 Business Analyst, and 1 Quality Assurance personnel.
• Professional Certifications: Team members hold CDMP (Certified Data Management Professional) certification, DAMA (Data Management Association International) related certifications, and data middle platform architecture certifications from major cloud platforms (e.g., Alibaba Cloud, AWS, Huawei Cloud).
• Industry Experience: The team has an average of over 8 years of experience in data governance and middle platform construction, having served 50+ enterprise clients across various industries including finance, retail, manufacturing, healthcare, and internet, successfully delivering multiple large-scale data governance projects (data volumes reaching PB level).
• Core Experts:
  • Chief Data Governance Consultant: 15 years of data management experience, has led the construction of data governance systems for several Fortune 500 companies, specializing in data standard formulation and quality improvement.
  • Data Architect: 10 years of experience in big data platform architecture, proficient in mainstream technology stacks such as Hadoop, Spark, and Flink, has designed multiple middle platform architectures processing terabytes of data daily.

We commit that core project members will remain stable during the service period. Any changes require prior communication with and consent from the client.

Pricing Model

We offer flexible and diverse pricing models to suit different client needs and budgets:

1. Fixed Project Price: Suitable for medium-to-large projects with clear requirements and defined scope. A one-time total price is quoted based on the assessed workload and complexity. Fees are paid in stages (e.g., 30% at start, 40% mid-project, 30% upon acceptance).

   • Reference Price: Typically between 300,000 and 1,500,000 RMB, depending on project scale.

2. Time and Material Billing: Suitable for projects with flexible requirements or potentially changing scope, or for services requiring only partial expert support. Billed based on the actual number of person-days invested.

   • Reference Price: Senior consultant daily rate: 5,000-8,000 RMB/day; Data engineer daily rate: 3,000-5,000 RMB/day.

3. Annual Subscription Model: Suitable for clients requiring long-term, continuous data governance and middle platform maintenance. Service contracts are signed annually, including periodic governance assessments, optimization support, and emergency response.

   • Reference Price: Annual subscription fee typically ranges from 200,000 to 600,000 RMB/year, adjusted based on service scope and data volume.

Note: The above prices are market reference ranges. Actual quotations will be determined after a detailed assessment based on the client's specific needs, data scale, service period, and other factors. We commit to providing transparent quotations with no hidden fees.