Data governance
1. Background
With the popularization of information technology, the amount of data generated by humans is increasing at an exponential rate. Such a large amount of data requires the use of new methods to manage. Data governance is the use of data from an organization (a business or government department) as a strategic asset To manage, a set of management mechanisms from data collection to processing and application are needed to improve data quality, achieve extensive data sharing, and ultimately maximize the value of data. At present, the research on big data in various industries is relatively hot, but for big data The research of data governance is still in its infancy. The correct decision of an organization cannot be separated from good data governance.
Most organizations do not consider the important impact of data quality on the construction of big data platforms, analysis and applications, and blindly invest, lack of overall planning and comprehensive governance of big data resources, and ultimately lead to the termination and failure of some projects.
The importance of data governance The premise is to build a unified and shared data platform. Data governance must not only regulate data, realize the value of data and manage risks, but also protect privacy.
2. The current state of data governance
2.1 Definition of Data Governance
So far, there is no unified standard definition of data governance. IBM's definition of data governance is that data governance is a quality control procedure used to add new rigor and integrity in the process of managing, using, improving, and protecting organizational information Discipline[6]. DGI believes that data governance refers to the distribution of decision-making power and related responsibilities in enterprise data management[6]. The goal of data governance is to improve data quality in general, and to reduce corporate risks while achieving Maximizing the value of data assets includes:
• Building a flexible, standardized, and modularized multi-source heterogeneous data resource access system;
• Building a standardized, procedural, and intelligent data processing system;
• Building refined data governance System and organization's data resource fusion classification system;
• Build a unified scheduling, precise service, safe and available information sharing service system.
Secondly, we also need to understand the functions of data governance-data governance provides what is needed to manage data as assets Finally, we
must grasp the core of data governance-the allocation of decision-making power and the division of responsibility for data asset management [7].
Therefore, data governance is essentially the process of evaluating, guiding, and monitoring (EDM) the data of an organization (enterprise or government department) from collection and integration to analysis, management and utilization. By providing continuously innovative data services, Enterprise creates value[6].
Data governance and data management are two very confusing concepts. Governance and management are essentially two completely different activities, but there are certain connections. Let's explain these two concepts in detail below.
Management is based on The direction set by the governance organization is to carry out planning, construction, operation and monitoring activities to achieve corporate goals[6]. Therefore, the governance process is the evaluation, guidance and supervision of management activities, and the management process is the planning, construction and monitoring of governance decisions. Operation.
Specific analysis: First, data governance and data management include different activities and functions. Data governance includes evaluation guidance and supervision, and data management includes plan construction and operation; second, data governance is to answer relevant questions about corporate decision-making and formulate data specifications , And data management is to implement the decisions made by data governance and give feedback; finally, the responsible bodies of data governance and data management are also different, the former is the board of directors, and the latter is the management.
2.2 Big data governance
The most authoritative definition of big data governance in the industry is:
- Big data governance is part of the broad information governance plan. It formulates strategies related to big data optimization, privacy and monetization by coordinating the goals of multiple functional departments [10].
This definition states:
- Big data optimization, privacy protection, and business value are the key areas of big data governance. Big data governance is a new stage in the development of data governance. Compared with data governance, the solution of various needs has become more important in big data governance. Important and challenging [6].
Mass data storage: According to the actual local data level and storage processing capabilities, combined with centralized or distributed data resource storage methods, it is constructed to provide PB-level data storage and backup capabilities for the big data platform to support
processing efficiency: big data governance Provides diversified massive data access and processing capabilities, including fast computing and search capabilities for various batch, real-time, quasi-real-time, and streaming structured and unstructured data. For the search capabilities of big data, In order to ensure data security, the storage method of big data on the cloud computing platform is generally ciphertext storage. Therefore, researchers have designed many ciphertext search algorithms to protect privacy.
Data reliability: Around the industry data element related standards and regulations, based on the industry The metadata system builds a whole-process, end-to-end data quality audit and control system for big data platform collection and aggregation, processing integration, and shared services to ensure data accuracy and reliability.Data
security: data value is the core value of big data platform, so data Security is the basis for ensuring the operation of the platform. Data security includes the security of data storage, the security of data transmission, the consistency of data, the security of data access, etc. Data security measures mainly include data desensitization control[30], data encryption control, Anti-copy management, anti-leakage management, data authority management, data security level management, etc.
