What is data management, data governance, data center, data middle platform, and data lake?

Hello everyone, I am Dugufeng, the author of Big Data Flow.

Several concepts have frequently appeared in everyone's field of vision recently.

What is data management, data governance, data center, data middle platform, and data lake?

What is the difference and connection between them?

These concepts are often confusing, and today we will analyze them in detail.

1. Data management

Data management refers to the planning, execution and control of an organization's entire data life cycle in order to maximize the value of data. It covers the entire process from data acquisition, storage, processing to final use.

Good data management requires comprehensive strategic planning, including determining the organization's data needs, designing data architecture, clarifying data collection methods, and establishing data security and monitoring measures. At the same time, specific execution plans are needed, such as the construction of data acquisition system, the selection of storage medium, the setting of data processing flow, and the development of data analysis and application platform.

In the process of data management, we must focus on the management of data quality. It is necessary to monitor and improve data integrity, consistency, accuracy, timeliness and other indicators to ensure that data quality meets business needs. In addition, it is necessary to manage data services, data security, data life cycle, metadata, etc., and establish strong technical support.

Successful data management also requires the cooperation of management organizations, such as setting up a data management department, or setting up data management positions in IT and business departments, and clarifying the division of responsibilities for data management. It is necessary to form an efficient data governance structure and carry out continuous data governance work.

Data management needs to be closely integrated with the company's business goals to serve business development. It needs to maximize the value of data while reducing the cost of organizational data management, and provide a solid foundation for enterprise operations and decision-making. A mature organization must establish a scientific, systematic and continuous data management system to improve its core competitiveness.

In other words, data management is a systematic project that requires planning and construction in terms of strategy, organization, process, and technology to manage and control the entire data life cycle. Only in this way can the supporting role of data for enterprises be truly utilized and greater commercial value created.

In the vernacular, data management is the actual work related to data management, specific things.

2. Data Governance

Data governance is an important part of an organization's data management, which provides the decision-making, supervision and control capabilities required for data management. The goal of data governance is to formulate data usage specifications, optimize data systems, and ensure data availability, consistency, quality, and security.

The first step in establishing data governance is to set up a data governance organizational structure. This usually includes the establishment of a data governance committee, composed of executives and heads of business, IT and other departments, responsible for setting data policies and standards. At the same time, data governance roles such as data owners and data administrators need to be established, with a clear division of labor.

The main tasks of data governance include formulating data governance strategies and frameworks, registering data assets, establishing data catalogs, and data maps to fully understand enterprise data assets. It is also necessary to continuously monitor and assess the data, measure the quality of the data, and carry out risk assessment and treatment. Establishing clear data usage specifications and responsibilities is the focus of data governance.

In addition, data governance also needs to establish a supporting technical system, such as metadata management system, data quality management platform, etc. It is necessary to strengthen the governance of business intelligence and big data platforms to ensure the data reliability of analytical applications. There is also a need to focus on data security controls and auditing.

Data governance requires the attention of management and the active participation of business departments. It is necessary to create an atmosphere of paying attention to data management and adhering to data standards from the perspective of corporate culture. At the same time, continuously optimize and iterate the data governance process to make it consistent with business needs. Only by continuing to promote in this way can data truly become an important strategic asset of the enterprise.

Data governance is a systematic measure to manage, control and govern organizational data. It needs to establish a comprehensive mechanism in terms of organization, process, technology, etc. to implement effective data management and release data value.

Data governance is a mechanism. There is a saying that is very appropriate. Data management is implemented by the CEO, while data governance is the board of directors, which needs to be supervised.

Data governance is about making sure data is governed.

Of course, due to the importance of data governance, the word is now enlarged. Data governance in a broad sense includes everything about data governance and data management.

3. Data center

The data center is the physical infrastructure used by enterprises to store and manage data. It includes IT infrastructure such as servers, storage devices, and network devices, and provides hardware support for data management. The core function of the data center is to centralize the storage and unified management of enterprise data.

Building a data center requires the preparation of computer room space, which has strict requirements on temperature, humidity, anti-static, fire prevention, etc. At the same time, it is necessary to invest in the establishment of basic operation and maintenance facilities such as power and cooling. In terms of servers, a large number of cloud servers and virtualization technologies are needed to flexibly allocate computing resources. The storage system should have a large enough capacity and consider redundant backup. The network system needs to provide high-speed internal switching connections and external link bandwidth.

The data center also needs a monitoring system to monitor the infrastructure in real time, and establish a complete security protection system, such as access control, firewall, intrusion detection, etc. To develop a detailed disaster recovery plan and drill mechanism. In addition, it is necessary to configure a professional operation and maintenance team for daily management.

After completion, the data center will carry the transaction system, ERP system, CRM system, data warehouse and other key enterprise information systems for centralized data storage. At the same time, massive data from channels such as websites, apps, and IoT must be aggregated. Resource optimization is carried out through technologies such as virtualization and cloud storage to realize centralized management of data.

A high-quality data center also provides backup services and disaster recovery services. Provide IT resources such as storage space and computing services for internal customers of the enterprise. Improve management efficiency through automated operation and maintenance.

The data center is an important cornerstone of enterprise data management. It needs comprehensive planning and construction in terms of infrastructure, security system, operation and maintenance process, etc. to provide stable, safe and efficient data storage and management services and win the trust of customers.

4. Data center

The data center is a set of platforms built on top of the data center, including data management, analysis and services. With data as the core, the data center is committed to building unified and standardized data capabilities and providing enterprises with higher value data applications.

The first step in building a data center is to plan a unified enterprise data architecture, plan all kinds of discrete data in the enterprise, and determine the central data warehouse and data set market. Then, according to different business scenarios, a standardized data integration model and data service model are constructed. Enable data from different systems to communicate with each other.

In terms of data governance, the data center integrates data from different systems into a unified platform, establishes data standards, data evaluation systems, and data security systems, and centrally manages internal data. Ensure data quality is controllable and data applications are credible.

The data center also has enterprise-level data application and analysis capabilities. It can collect, clean, and convert internal and external data, build high-quality analysis data sets, and help enterprises make business decisions and optimize through reports, analysis models, and data visualization. Use AI and other advanced technologies for intelligent analysis.

In addition, the data center also opens service interfaces to different departments and external systems to realize data service. It can provide data services such as accurate customer portraits internally, and open data products to the outside world. Build an ecosystem centered on data.

Building a data center requires enterprises to upgrade their technical architecture and adopt emerging technologies such as big data and cloud computing. It is also necessary to plan the organization of the data center, allocate specialized data modeling, analysis and other talents, and formulate policies for data openness and utilization.

The data middle platform builds a hub platform for enterprise data management and application, which helps to release the value of data and promote business innovation. It is an important basis for digital transformation and the key to enhancing the core competitiveness of enterprises.

5. Data lake

Data lake refers to the architectural concept that enterprises store all kinds of raw data directly in a lake-shaped data pool. It can store and manage large volumes of structured, semi-structured and unstructured data in different formats.

The data lake emphasizes direct storage of data samples or raw data, rather than transforming or dividing data. It uses a flat shared data directory for each user to find the data they need. Users can interactively analyze and explore data to discover correlations between different data sources.

The first step in building a data lake is to establish a centralized basic data storage, such as the Hadoop system. Then, various data sources of the enterprise, including databases, sensors, logs, documents, etc., are directly loaded into this open storage without previous data cleaning and conversion. Next, build the data catalog and mark the feature metadata of different data. Finally, analysis tools are provided to facilitate users to analyze and query data by themselves.

Unlike traditional data warehouses that store only refined data, data lakes store raw detail data directly. It has no strict restrictions on data input and can be flexibly expanded to support richer analysis applications by including more data. However, the accuracy and refinement of the data in the data lake is not as good as that of the data warehouse, and users need to convert it themselves. It is more suitable for data scientists to explore and analyze.

When building a data lake, the main challenge is how to manage all kinds of messy data. It is necessary to manage metadata such as data sources, formats, attributes, and establish security controls. There is also a constant need to add analysis and visualization tools to make them easier to use.

The data lake provides an environment for enterprises to directly store and analyze all data, and can more fully explore the value of data. It lowers the threshold of data integration, but it also needs to actively address the challenges of data governance. Data lake represents the development trend of enterprise data management towards openness and decentralization.

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