Data Governance Framework & Asset Roadmap: Unlocking Enterprise Data Value

Data Governance Framework and Data Asset Roadmap Planning

Enterprises must transform their perception of data asset value, establishing a data asset value system within operations and management. Data ownership should be assigned to specific departments and roles, with data asset value incorporated into overall operations and performance assessment, embedding digital asset value into every digital process.

After building a data asset management system, organizations need to further explore and leverage data asset value. Beyond common decision analytics, data value must permeate all digital business links, shifting from production-driven to data-driven value and transforming traditional manufacturers into data service enterprises.

Data Governance

Based on enterprise data management practices, form an enterprise data strategy, governance organization and talent, norms and processes, providing a foundation for data-driven business operations.

Unified Data Assets

Includes data asset catalog, data standards, enterprise data model, data distribution, and data map. These provide design guidance throughout the data lifecycle: generation, lake ingestion, connection, and application.

Data Operations

Provide data operations mechanisms and responsibilities, establish an operations indicator system (e.g., data service construction cycle, data demand response cycle), ensuring continuous virtuous operation of data management.

01 Data Awareness Capability: Transforming Perception of Data Assets

Currently, only a few Chinese enterprises value big data in decision-making; most managers have not truly recognized big data's value. Mining factors highly correlated with profit improvement can provide strategic decision support, demanding keen insight from leaders and challenging traditional thinking. Making the enterprise, especially management, realize data's importance is the primary goal.

(1) Implement Data Awareness

Data governance appears technical but actually involves updates to management, systems, and concepts. It requires enterprise-wide planning and coordination to permeate data awareness through every person and business process.

(2) Team Building / Establish Data Management Department

Led by the enterprise's top leader, the IT department proactively collaborates with business units to form a "federated" data governance mechanism. Through sincere cooperation, both sides create synergy, ensuring effective governance while improving IT staff's business understanding and business staff's IT literacy.

(3) Focus on All Personnel's Skills and Tasks

Data's breadth means no single person can handle it alone. The first goal is to eliminate fear of data. Leadership must focus on skills and tasks, including:

Formulate data governance principles to enhance data transparency.

Break organizational and data silos that hinder collaboration and data optimization.

Use wise change management methods to promote data culture acceptance.

(4) Integrate Data Management into Culture

Provide employee training to foster a positive attitude toward data.

Prove insights and results from data through strong use cases.

Conduct data training in bootcamps.

02 Assetization Capability: Data Asset Management Roadmap Planning Strategy

1. Enterprise Data Asset Application Scenario Planning & Full-Spectrum Data Experience

Through current-state research and scenario design, build a real-time online operations platform supporting full-reach user data-driven business operations.

2. Data Governance System Construction

(1) Data Governance System Planning Preliminary Plan

Data control includes building a data management framework, data control activities, management roles and responsibilities. First, build the framework, clarify core responsibilities, and build the data organization. The data organization is positioned as an entity data management professional organization responsible for building and managing the data management system, achieving "clean data achieves excellent operations, smart data drives effective growth" through continuous improvement. Suggested core responsibilities focus on four areas: data architecture construction, change control mechanism, data quality management, and incorporating data quality indicators into data owners' performance appraisal.

Main action steps for data control by the data organization:

(2) Data Asset Sorting

Data Architecture: Enterprise data architecture describes, in a structured way, the overall component specifications of various data types and their relationships needed in business operations and management decisions. It includes four components: data asset catalog, data standards, enterprise data model, and data distribution.

(3) Implement Metadata Management

Metadata is "data about data", describing data attributes to support functions like storage location indication, historical data, resource lookup, and file records. It is like building archives for data. Metadata management is a critical foundation for data asset management, involving planning, implementation, and control to obtain high-quality, integrated metadata. Main work includes standardizing metadata generation, guiding metadata collection, and controlling metadata registration.

Standardize metadata generation: Define metadata classification and entities, clarify design principles, standards, and methods, guide platform/tool selection and construction, support data and IT personnel in metadata design, management, and application.

Guide metadata collection: Define standards and interfaces for the collection process, unify collection mode, ensure efficient and accurate metadata collection.

Control metadata registration: Through metadata asset management and standard registration specifications and unified methods, connect business and technical metadata, enabling business personnel to understand data and achieve data sharing and consumption via the data foundation.

(4) Data Security Management

Data security management sets security levels, establishes systematic security policies, and comprehensively controls security to ensure data asset security in "storage, management, use" phases, achieving "controllable before, during, after". Key work includes understanding legal/regulatory requirements, defining data security standards, classifying security levels, defining sensitive data objects, defining security policies, defining security controls and measures, security audit, emergency response plans, and security education and training.

The big data security system is the overall control architecture, including control framework, technical architecture, and operational mechanisms. It establishes big data security control capabilities, supports security capability assessment, and formulates security system construction planning and roadmap. Referencing the "Data Security Capability Maturity Model" and "Public Security Bureau Information Security Level Protection 2.0" meeting Level 3 requirements, comprehensive planning covers data collection, transmission, storage, use, exchange, and destruction stages.

03 Data Application Technical Capabilities

Data integration, data governance, service development, data services, and visualization tools realize functions from collection, storage, management, computation, analysis/utilization, to data interaction.

1. Data Integration

Data integration completes data lake ingestion, not simple data migration but backup per methodology. Prerequisites are six data standards: clear data owner, published data standards, defined data classification, clear data source, data quality assessment, and metadata registration. These are completed by data representatives before lake ingestion and asset registration on the governance platform. Technically, it must support efficient ingestion of multiple heterogeneous data sources.

Data enablement platform architecture diagram:

2. Data Governance

Data governance is the collection of activities exercising authority and control over data assets (planning, monitoring, execution), guiding other data management functions. Main goals: consistent information architecture and standards, single trusted data source, reliable external data, data architecture and IT handshake, cross-domain data convergence and integration, report/indicator data servicable, business monitoring process data visualizable, and manageable.

Data governance framework formulation:

3. Data Servicization

Establish a unified data open service platform providing internal data development and service support, and external unified data open services. Capabilities include data service permission management, privacy management, data service development and API encapsulation, and API usage statistics, fully releasing data value. Example: zero-code multi-data source API publishing, supporting data out-lake to serve upper applications.

4. Data Asset Development

The true meaning of data is the discovery and utilization of data value. To leverage massive collected data, build rich data development tools, models, components, interactive analysis tools, data analysis mining platforms and tools, data annotation and label management tools. Provide a series of data analysis and AI algorithms and model libraries: classification, clustering, regression, relationship analysis ML algorithms, and deep learning pattern recognition, image/video computer vision, speech/text NLP. Also publish common tool components from app development as public service components (e.g., form auto-recognition and info filling, face recognition) to guarantee rapid upper-layer app development.

5. Data Visualization

Wide application of data visualization helps present data value, facilitates business and management use, and helps business analysis drive decisions.

04 Planning and Construction Recommendations

1. Construction Planning

Based on enterprise development strategy and business needs, guided by digital transformation, formulate an overall enterprise data management plan, clarify data management strategy and goals, optimize the data management system, formulate a data quality assessment model, identify data enablement platform construction needs and solutions, and guide the enterprise digital construction process.

Accelerating data asset value realization and monetization is the foundation for new business models, roughly divided into three phases:

2. Quick-Win Pilot

Example: a company's marketing digital upgrade based on a data enablement platform BI planning and high-level design project. In marketing system building, helped the client form a multi-dimensional indicator system from results to process based on the data enablement platform, layering and grading indicators per the RACI mechanism (R: executor, A: accountable decision-maker, C: consulted, I: informed) and key users. Ultimately, each indicator was clearly defined, forming an indicator dictionary with definitions, alert rules, and responsibility attribution. KPIs and indicators are crucial for tracking progress; data governance requires enterprise management approval and overall layout, so goals and expected benefits must be fully articulated. This is also vital for finding future improvement methods.