As the backbone of national economic development, state-owned enterprises play a pillar role in economic and social development. Steadily promoting the reform of state-owned enterprises and cultivating world-class enterprises with international competitiveness are the important goals of state-owned enterprises at this stage.
Under the background of the rapid development of science and technology and economic globalization, how can state-owned enterprises maintain and increase the value of state-owned capital under the pressure of increasingly fierce competition? How to improve the competitiveness of state-owned economy? How to build a world-class enterprise? It has become a consensus to vigorously promote the digital transformation of enterprises. More and more enterprises are actively seeking transformation paths and models that suit their own characteristics, and accelerate the in-depth development of the integration of digital intelligence and the real economy.
Digging deep into the value of data to control operations in real time
Data has become a strategic asset of an enterprise, and it is also the basic element of an enterprise's digital transformation. The focus of digital transformation is to provide efficient technical support for data integration. The digital transformation of state-owned enterprises should focus on the security and reliability of data, stimulate the vitality of data elements, improve the quality of data circulation, integration and sharing, dig deep into the value of data assets, and release the data-driven "multiplier effect".
At present, although most state-owned enterprises are able to control their operations in real time, the complex hierarchical structure and diversified operations lead to differences in management and business data. Many heterogeneous systems established by various business sectors at different times lack source codes and data dictionaries. , the data is relatively scattered, and the challenges of data hierarchical authorization brought about by the centralization of the management and control system, and many other problems.
The new technology has been able to achieve the integrated deployment of applications, hierarchical and sub-authorization of data, layer-by-layer management of management and business data, and the formation of a collaborative model for internal and external business data supervision, control, and early warning. The direction of key exploration in the process of digital and intelligent transformation.
In the process of data governance, it is necessary to focus on building platform capabilities, and at the same time accumulate capabilities in the informatization stage, empower state-owned enterprises to develop agile applications with a powerful digital intelligence platform base, and quickly build digital application scenarios.
Give full play to the role of the platform base and move towards data-driven
Under the two-wheel drive of policy and technology, state-owned enterprises have consolidated the cornerstone of digital transformation by building a digital intelligence platform base.
The digital and intelligent base can meet the daily management needs of enterprises, achieve a "screen" without obstacles, a "network" to catch all omissions, eliminate information islands, realize interconnection, and enable enterprises to move towards the goal of digital management and intelligent decision-making step forward.
The data center collects, calculates, stores, and processes massive data, while unifying standards and interfaces, centrally storing standard data, forming a big data asset layer, and then providing efficient services for businesses. The data middle platform can improve the data reuse rate, accumulate business models, and drive business growth through data. Therefore, building a data middle platform has become the path choice for many enterprises to activate the potential of data.
Enterprises should understand that the data center is not only the construction of a technical platform, but more importantly, the integration of data, technology, and scenarios, so as to truly reflect the value of data.
The idea of building a data center can be summarized into three points: continuous operation, scene integration, and data empowerment.
The data center is a project that starts with the end in mind and operates more than construction. In the demand research stage, the actual business scenario of the enterprise should be used as a development test case to plan the blueprint of the data center, complete the planning of the data governance system and operation system, and promote the functions of the data center. Deployment, system construction, business scenario development, and service release; discover new data service needs through landing operations, evaluate data value, and expand the application scenarios of the data center, forming an iteration of "research planning-system implementation-mid-stage operation" Closed-loop management.
The construction of the data middle platform is not to abandon the original IT system of the enterprise and completely rebuild it, but to realize the "data empowerment business".