Data assetization is the cornerstone of enterprise digital transformation, and it is the only way to purify data ore into data gold. Data assets have the following characteristics:
According to the definition of CAICT, data asset management refers to a set of activity functions that plan, control and provide data assets, including developing, implementing and supervising plans, policies, programs, projects, processes, methods and procedures related to data, so as to control, protect, deliver and enhance the value of data assets.
It can be said that data asset management is a long-term and systematic work, and in order to ensure the effective development of various data asset management activities and promote the smooth progress of data asset management as a whole, strategic planning, enterprise architecture, institutional system, platform tools, long-term mechanisms and other safeguard measures are extremely important.
So how to implement data asset management, today I will share with you a general step of data asset management practice, which can refer to the method and strategy implementation of the four stages of "overall planning→ management implementation→ audit inspection → asset operation". Guided by business application goals, enterprises can customize different implementation steps according to their own data and management situation.
△ General steps for data asset management practices
Phase 1: Overall planning
The first stage of data asset management implementation is overall planning, including three steps: evaluating management capabilities, releasing data strategies, and establishing a corporate responsibility system, which lays the foundation for the subsequent data asset management and operation.
The first step is to take stock of your data assets and evaluate your data asset management capabilities.
Use technical tools to extract data, collect metadata, identify data relationships, visualize data models containing metadata and data dictionaries, and improve data asset information including business attributes and management attributes from the perspective of business processes and data applications, so as to form a data asset map. In addition, a comprehensive evaluation of the data asset management of the enterprise is carried out from the dimensions of system, enterprise, activity, value, technology, etc., and the evaluation results are used as the evaluation baseline, which is helpful for the enterprise to understand the current situation and problems of management, and further guide the formulation of data strategic planning.
△ Dimensions and key points of data asset management capability assessment
The main deliverables of the first step include: data asset inventory inventory, data architecture or data model, data asset management status assessment report, and data asset management gap analysis report.
The second step is to develop and publish a data strategy.
Based on the assessment results and gap analysis of the current situation of data asset management, the relevant stakeholders of data asset management are convened to clarify the data strategic planning and implementation plan. At the same time, in order to adapt to the rapid changes in the business, the data asset management work is carried out in a relatively agile way, and the short-term planning and execution plan of the data strategy are regularly adjusted.
The main deliverables of the second step include: data strategy planning, data strategy execution plan.
The third step is to establish a corporate responsibility system and formulate and publish data asset management system specifications.
Starting from the data strategic planning, build a reasonable and stable data asset management enterprise architecture, as well as a data asset management project team with a certain degree of flexibility, determine the data asset management responsibility system, and formulate data asset management system specifications that are in line with the strategic goals and the current actual situation.
The main deliverables of the third step include: data asset management enterprise architecture diagram, data asset management responsibility system, and data asset management related management measures.
Phase 2: Management Implementation
The goal of the second phase is to promote the implementation of the first phase of data asset management by establishing a rule system for data asset management, relying on the tools of the data asset management platform, and taking the data life cycle as the main line. The second stage of management implementation mainly includes four steps: establishing a standardized system, building a management platform, whole-process management, and innovative data application.
The first step is to formulate an enterprise-level data asset standard specification system, and establish implementation rules and operational specifications for each activity function.
The enterprise-level data asset standard specification system refers to the standardization of data technology design and business meaning under each activity function. Taking structured data as an example, standardized objects include fields, tables, and relationships between tables, as shown in the following figure. In addition, combined with the relevant management measures for data asset management, the implementation rules and operation specifications of each activity function are formed, laying a good foundation for the effective implementation of data asset management.
△ Example of a data asset standard specification system
The deliverables of the first step mainly include: standards and specifications related to the functions of data asset management activities, implementation rules, and operational specifications.
The second step is to build a big data platform and aggregate data resources.
According to the data scale, data source complexity, data timeliness, etc., evaluate the expected cost of the platform, build or purchase the big data platform by itself, and provide underlying technical support for data asset management; design data collection and storage solutions, formulate data conversion rules according to the data asset standard specification system in the first step, determine the data integration task scheduling strategy, support the extraction of data from the business system or management system to the big data platform, and realize the aggregation of data resources; combined with cloud native and AI and other technologies to improve resource utilization and reduce resource investment and O&M costs for data asset management.
The deliverables of the second step mainly include: big data platform, data aggregation solution and records.
The third step is to rely on a unified management platform to realize the whole process management of data assets.
The data asset management team carries out data resource activities, clarifies and records the specifications and expectations of data users in the data requirements for each activity, supports the implementation and application of rules in data design, responds to the adjustment of data users' rules and expectations according to the changes in the business and data of the data producer in data operation and maintenance, and discovers and rectifies problematic data in a timely manner.
The deliverables of the third step mainly include: data asset management platform, data asset life cycle operation manual, data asset project management operation manual, and data asset management business case.
The fourth step is to innovate data applications and enrich data services.
Enterprises should strengthen the innovation of data applications and services, focusing on reducing the difficulty of data use, expanding data coverage, and increasing data supply capacity. Reduce the difficulty of data use through data visualization, search analysis, data productization, product servitization and other perspectives, enable more front-line business personnel to directly participate in the data analysis process through self-service data analysis and other means, and increase data supply capacity through flexible role transformation between data consumers and data producers.
The deliverables of Step 4 include the list of data application products, the data application service operation manual, and the data application service user guide.
Stage 3: Audit and inspection
The audit and inspection stage is an important part of ensuring the effective implementation of various management functions in the implementation stage of data asset management. This phase includes specific tasks such as checking the implementation of data standards, auditing data quality, and monitoring the data life cycle. Strive to achieve three normalizations:
First, the normalization of the inspection of the implementation of data standards;
Data standard management is the basic work of enterprise data asset management, and through the implementation of data standard management, enterprises can realize the unified operation and management of the data of the whole network of the big data platform.
the second is the normalization of data quality audits;
Tackling data quality issues starts with raising awareness of data quality, which includes the ability to relate data quality issues to their material impact, while conveying the idea that data quality issues cannot be solved by technical sparring alone. Second, establish a set of processes and procedures for data quality.
The third is the normalization of flexible configuration of data storage policies;
The goal of data lifecycle management is to classify and grade the value of the enterprise according to the data to form a data asset catalog and then formulate corresponding policies in order to fully support the needs of the enterprise's business goals and service levels.
The platform tool is an effective way to normalize inspection, which saves manpower and material resources, ensures the accuracy of inspection results, and improves inspection efficiency compared with manual operation. Regularly summarizing and establishing a baseline is the key process of normalized inspection, statistically analyzing the inspection results, forming inspection indicators and capability baselines, evaluating the effect of data resources, determining rectification plans with relevant stakeholders and participants, and continuously improving management models and methods.
The main deliverables of the third phase include: data asset management inspection methods, data asset management inspection summary, and data asset management inspection baseline
Phase 4: Asset Operation
Through the first three stages, enterprises have been able to establish basic data asset management capabilities, and on this basis, they also need to have the ability to realize business value-oriented, user-centric, and provide data value to users at different levels inside and outside the enterprise. The asset operation stage is the final stage of realizing the value of data asset management, which includes the evaluation of data asset value, data asset operation and circulation, etc.
Build a data operations center to give full play to the role of the data team in supporting the business department. The data team provides support including self-service data services, AI models, and more, and enhances the digital capabilities of the business through regular advocacy and training. In addition, based on scenario-based data asset operation, all parties using data assets in business departments are encouraged to use relevant platforms to explore data, share exploration results, and put forward suggestions for improvement.
With the main goal of data-empowered business development, we will build a data asset value evaluation and data operation index system. Starting from the business side, the scale and quality of data assets covering various business lines and data scenarios are constructed from the aspects of intrinsic value, economic value, cost value, and market value. In addition, a large screen for the digital operation of data assets is established to visually display the ecological map of data assets and make the application effect of data assets explicit.
The main deliverables of the fourth phase include: data asset service catalog, data asset value evaluation system, data asset circulation strategy and technology, and data asset operation index system.
In view of the problems and challenges faced by data asset management, Yixin Huachen has created a data asset management solution with the goal of providing value-added data assets, following the principle of "global awareness, business-oriented, data-oriented, step-by-step evolution, and partial implementation", and implementing the "1+4+N" management model with management structure and system strategy as the guarantee, supported by the four major management functions of data assets, and providing N multi-data asset services, so as to help enterprises build a data asset management platform in one stop.
The solution provides four core management capabilities:
Data integration capability: Provide heterogeneous resource integration with batch and streaming, realize the collection of multi-source heterogeneous data in the enterprise, and carry out effective integration and development, so that the data can achieve more correlation and collision, break the enterprise data island, generate more valuable data that is conducive to business development and innovation, and ensure the integrity of data assets.
Data governance capabilities: One-stop process governance, build unified and executable standards, improve data quality, explore data relationships, establish data accountability and accountability mechanisms, and realize the integration of standardized data after governance into different business fields.
Asset planning and development capabilities: Build a complete data asset management template, build a unified data service, meet self-service data consumption, and build a variety of data applications such as data statistics, analysis, mining, and data models for users at the executive level, management level, and decision-making level.
Asset operation capability: Provide multiple sets of data resource portals and data product portals, display the full picture of data assets to different types of data consumption and data consumers, and provide application channels for various types of data services.
Yixin Huachen's big data asset management solution has been successfully applied to Times China, Shandong Linyi Mining Group, State Grid and other enterprises to build a data asset management platform, compile and form a data asset catalog, and realize the open sharing of data assets.
Source: Yixin Huachen Software