What is a data center?
Data center is a brand-new structural change. In the past three decades, enterprise data management has been based on traditional IT architecture. Whenever the technical department solves a problem for the business department, it needs to build a new system from top to bottom from the exploration of business needs and the opening of technical barriers. The establishment of each system is self-contained, and each meets the needs of business departments. This situation not only consumes a lot of energy in various departments, but also makes it difficult for various systems to get through and manage, and it is impossible to form more powerful data capabilities. 
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In addition, the current IT auxiliary management system is based on the developed manufacturing industries in Europe and the United States. Its more function is to collect data. The data of each system exists in isolation and there are many chimneys. In today’s new industrial Internet era, companies need to quickly respond to external changes and establish multi-dimensional data to reshape the application of DT. Therefore, the traditional architecture is not suitable for the current market development. The architecture of the data center has overturned the past 30 years. Years of traditional IT data management architecture.
IT cannot grow DT. IT is an information technology for data collection by software management systems such as CRM. DT is an intelligent application technology based on data generated by IT. The former is based on information technology and the latter is based on data technology. As a traditional data management architecture, IT cannot grow DT.
If it is explained by a popular life case, the working principle of the data center is similar to the working process of a five-star hotel to meet the needs of diners. The data is similar to the basic ingredients such as fruit, vegetable and meat used in large restaurants. Management systems such as CRM and ERP collect the data and put it in the database. In order to meet the needs of different users (diners' meals), companies need to put the data summarized in the database in the central kitchen for preparation by business personnel (chefs). The synchronization function of the data placement process divides the data into categories, just like transporting different ingredients to the ingredient warehouse through a logistics system, and dividing the ingredients into categories. At the same time, data governance technology needs to clean the data, and for some special requirements, it also needs deep processing. 
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Through the above processing methods, the data can be provided to the business or technical staff, which is equivalent to handing over the sorted and cleaned ingredients to the chef for production. Business personnel or technical personnel will combine and match data according to different needs when using it, that is, model the data. In the same way, the chef also adds condiments and recipes when cooking to make the dishes more delicious. The data generates different application directions through modeling, and then beautifies them through visualization, such as the chef's adding oil and vinegar, etc., and finally presents application products (delicious dishes) that meet the needs of users (diners).
Before the enterprise had a "central kitchen" for data, the generation of application products in a certain direction needed to be done from the beginning to the end of data governance, cleaning, modeling, etc., only forming the processing chain of this product. The direction data is not synchronized and cleaned, so as to get through, and the chimney-like barriers are created. When the business needs of another user overlap with the application direction of the product previously done, it still needs to be done from scratch.
In the era of IT management systems, such a long period is three to six months, and there cannot be too many changes in the process, which means that users cannot adjust at any time according to business changes. Such application research and development not only has a long schedule, but also repeatedly builds some common functions. Common functions cannot be precipitated, and some capabilities cannot be shared, which ultimately results in a waste of enterprise resources and costs.
Nowadays, if an enterprise establishes a complete data middle-office system, it is equivalent to establishing a central kitchen system integrating logistics systems and warehousing systems. The system processes and assembles food materials, and business personnel can directly "order" on demand, and customized applications are immediately generated. It can be seen that the data center can provide end users, such as business staff (cooks), such as users (diners) with a low-cost and highly flexible business processing platform. If an application is warmly welcomed by users, you can rely on the diversified and shared functions of the data center to provide continuous services for it. In this way, companies can rely on the powerful data center service capabilities to enhance their competitiveness and maintain a favorable market position.
The data center is a capacity sharing platform. Nowadays, many product application research and development initially emphasize functionality, and each function has more or less repetitiveness. However, companies have different definitions of these product functions. When customers generate certain requirements, due to different definitions, product functions and data between functions are difficult to connect, and capacity sharing cannot be realized.
Application development based on the data center does not emphasize functionality, but pays more attention to the sharing of capabilities. This capability can be directly exported and used just like water, electricity and coal to meet the different needs of business departments and users.
The data center is an organic integrated platform. The data center is an enabling platform that includes model assets, application assets, tool assets, and technical assets. It is not a purely technical concept. The data center is not only the output of technical capabilities, data capabilities, asset capabilities, application capabilities, and institutional capabilities are also the value output of the center. The core point of the data center lies in empowering business departments and users, taking applications as the starting point, quickly responding to external needs, helping business departments generate performance and forming business growth.
The data center is a new generation of data architecture thinking. Its working principle is based on application as the starting point for data integration, and the final result is a data application platform. As science and technology become more and more advanced in the future, people's needs are ever-changing, and the emergence of various applications will be natural. However, it is difficult for a pure technology-oriented data center to quickly respond to external application needs.
The data center is an end-to-end technology platform, rather than a bunch of API interfaces, it pays more attention to the use of business end and the embodiment of business value. The traditional API capability output model cannot meet the ever-changing needs of enterprise application products. It requires technical transformation at the middle layer and cannot produce applications quickly and efficiently. When traditional enterprises build a data center, if they only complete the creation of the API interface, it is only one part of the data center. Therefore, the data center is not an end-to-end technology empowerment platform.
It can be seen that the construction of the data center requires the flexible application requirements of the business department, the powerful data governance and data modeling capabilities of the technical department, and the multi-dimensional cooperation of various departments and assets of the company. It is an organic combination of business, technology and company assets, not a combination of one-sided modules.
Data center is a new technology construction idea. As a new construction idea, the data center has broken the traditional functional and integrated construction ideas of enterprises. The company's previous product creation process first relied on engineers to build the basic technical architecture, and then added application functions after the architecture was completed. This kind of construction idea is more suitable for companies with stable product models, and it is not the best choice for companies whose application requirements are changeable and the application starting point cannot be unified for a while. This kind of product built out of engineering or infrastructure will ultimately fail to provide more value to the business department. Therefore, the construction idea with application as the core thinking point is the key to the long-term vitality of the enterprise, and the construction of the data center will help the enterprise change the traditional way of product application construction.
In the past, traditional enterprises deployed various management systems such as CRM and ERP to improve management efficiency. This management system provided certain reference value for business owners in terms of basic data management and simple business analysis. However, when the external demands of the enterprise become more and more changing, the simple data collection of the enterprise management system cannot meet the market demand.
Business owners strengthen management by opening up multiple management system accounts and adding various analysis tools. This integrated construction concept still cannot truly empower business objects. Data applications are diversified, and a large number of temporary, instant, and decentralized needs arise from time to time. Data models need to be adjusted frequently according to business priorities. Business owners cannot respond to needs only through Unicom's various management system accounts.
Although the integration of multiple management systems and analysis tools has solved some problems for business owners, the construction ideas of each system product are inconsistent. Products and products have overlapping functions and boundary divisions, and the definitions between products are not the same. Form a unified and seamlessly integrated data asset. Technological discrepancies between products will lead to application errors and ultimately affect the trust of users in the product. As a result, the integrated construction method has created huge maintenance costs and governance costs for the technical department, and has not achieved the true purpose of data center construction.
Basic capabilities of data center
The data center has the capability of data service. The data center helps business departments to establish a workbench, through which data-related services can be quickly obtained, including data extraction, data analysis, data push, data return, and other services; the data center can process and manage dirty and messy data , Segmentation, modeling, labeling, etc.
The data center can enable business personnel to have the ability to develop data applications. Business personnel can do in-depth application development according to the needs of their business units, such as precision, intelligence, wisdom and other related applications. These applications can be turned into products independently.
The data center has powerful mass data processing capabilities. The data center provides powerful basic support for data applications, such as data governance capabilities, fusion capabilities, collection capabilities, and synchronization capabilities. Regardless of different dimensions of data such as enterprise production, operation, consumer traceability, supplier maintenance, external public data, etc., the data center can achieve data connection and sharing through different functions.
Different business scenarios require computing platforms of different scales to process massive amounts of data. The construction of the data center helps business personnel to schedule computing capabilities at any time according to application requirements.
The data center has the ability of data development. The settings of different data tools such as analysis tools, mining tools, and cleaning tools in Taichung can help upstream and downstream enterprises and external users to directly develop applications. The data center can package upstream and downstream tools in a fool-proof manner, such as application development, application replication, application use, application evaluation, application sharing and other function settings to help users in different fields of the enterprise realize data sharing and application sharing.
The data center has the ability of self-learning and automatic improvement. The characteristics of empowering business personnel in the data center determine their ability to self-learn. Through continuous ability superposition, the middle station can carry out a virtuous cycle and return of data and company assets, empower the business and technical departments of the enterprise, and build a self-learning platform with rolling, growing, and changing capabilities for the enterprise.
The data center has the ability to deposit assets. In the process of using data, users will automatically deposit high-value data, and through the integration capabilities of the data center, these valuable data will be virtuously cycled and returned. Therefore, enterprises have a deeper understanding of their own user data, membership data, and human data. Such precipitation ability can improve the company's core competitiveness. In addition, high-value assets within the company, such as model assets, IT assets, DT assets, data assets, application assets, and user assets in application assets, portrait assets, etc., can be deposited through the middle station to provide the company for future applications More support. The long-term precipitation has helped the company build its core competitiveness, enabling the company to take the lead in digital transformation and quickly deploy digital market competition.
The data center has the ability to automatically track data quality. Data is often used by multiple departments and multiple roles. Each department will define a variety of data indicators, labels and usage methods. In the long run, the data governance system will become more and more complex. Once the data cannot be tracked, it will lead to errors in the front-end data application, and ultimately make the company's decision-making errors and pay a higher price. The data center can avoid the above problems. Intelligent data quality tracking and blood relationship analysis will track the blood relationship system of the data to ensure data quality.
The data center has the ability to integrate data. As enterprise business changes, data interconnection becomes more and more important. The data center keeps the definition and meaning of the data consistent, so that the data is truly connected in real time.
The data center has the ability to isolate the risks of IT systems and DT systems. IT systems play a role in enterprise data collection and management. Business requirements change rapidly, but IT systems cannot change with the DT system. The DT system has its own meaning. The different goals and positioning of the two systems will inevitably lead to differences in data applications. The establishment of a data center can help companies isolate data risks and ensure that one party does not affect the other party.
Data center application method
The first application method of data center is to help business departments use data analysis flexibly. The data center has changed the previous dilemma of insufficient data analysis technology capabilities of business departments. Before the emergence of the data center, the business department could only rely on the assistance of the technical department when facing analysis needs due to the lack of technical capabilities. The communication and cooperation between the business department, the technical department, the analysis department and other departments in the intermediary department consumes a lot of time and communication costs, and also delays the technical department's opportunity to do more valuable projects.
The data center breaks the complex format of data, enables data sharing, and opens the green light for business departments in terms of data analysis technology, and business personnel can freely perform data analysis.
The second way of data center application is to help technical units, business units, and even external units to flexibly create applications. Third parties such as internal technical departments, business departments, and even external suppliers can also create applications based on the data center. The data center helps companies build an industry ecological sharing platform to benefit internal and external personnel and upstream and downstream corporate customers.
The third way of data application in Taiwan is that the technical department can continuously build application capabilities and accumulate data assets and value assets. The data center enables business, technology, and company assets to be connected, merged, and shared. The formation of data assets enables the technical department to form continuous application development capabilities. On the contrary, the data generated by the new application provides fertile soil for the birth of the new application, and the two sides form a closed loop.
The construction content of the
data center The data center has different application content in the SaaS layer, DaaS layer, PaaS layer, and IaaS layer.
SaaS layer: From a technical point of view, the SaaS layer is the services and functions that business users or technical users can directly use, including data analysis tools, data mining tools, visualization tools, cleaning tools, modeling tools, and other different data tools; it also includes different Hierarchical data applications, such as large-screen visualization applications, decision analysis systems, user portrait systems, precision recommendation systems, AI applications, etc.
DaaS layer: Users can obtain data direct services through the DaaS layer. The most common services include customer data acquisition, raw data management services, raw data and IT data interconnection services, data quality monitoring services, data relationship analysis services, etc.
PaaS layer: This layer has deeper capabilities and mainly provides services to the company's internal data governance team, data team, and technical team. Based on the PaaS layer, a better DaaS layer and SaaS layer can be constructed. This layer should be distinguished from basic tools and capabilities such as data governance, cleaning, modeling, model management, model sharing, application development, and application publishing. There are many PaaS scheduling capabilities at this layer, including synchronization systems and scheduling systems.
IaaS layer: Mainly solve basic capabilities such as data computing resources and storage resources, including computing resources such as big data cluster computing, distributed computing, and databases. This layer can store churn calculations, data scheduling systems, and computing resources. This layer has the most basic capabilities of data, including data security. This layer can help users obtain capabilities from IT, generate DT applications, and at the same time return to IT to support IT development, and to the IT business environment to improve corporate business capabilities. (Note: Due to limited space, various technical modules in the data center will be supplemented later) The
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