Big data leads to changes in enterprise management

Big data brings a new round of information revolution, and at the same time, sets off a management revolution, in the management of the management level to bring many changes to the enterprise.

At present, the domestic big data has basically had the soil for development: the enterprise data has qualitatively improved from the quantity and diversity, and the value of data has been highly recognized. This paper tries to take the large state-owned enterprises (central enterprises) as the research object, to explore the impact of big data on the enterprise management change and the enterprise's response to the strategy, hope to benefit the enterprise big data management and utilization.

Big data triggers changes in enterprise management

From a theoretical point of view, the reason why it is said that big data triggers changes in enterprise management is that there are two closely related factors behind it.

One is that the nature of big data is highly compatible with the core factors of management. It is generally believed that one of the most central factors of management is information collection and transfer, while the connotation and essence of big data lies in the association of information within the big data, mining, so as to discover new knowledge and create new value. The two have a high degree of compatibility in this feature, and it can even be said that big data is another tool for enterprise management. Because for any enterprise, information is wealth, from the enterprise strategy, the use of big data, give full play to its potential to assist decision-making, can better serve the enterprise development strategy.

The second is the transformation of big data from resources to assets. In the era of big data, data penetrates in all industries and gradually becomes an enterprise strategic asset. The scale and quality of owned data directly determines the core competitiveness of the enterprise as well as market insights, and also affects the strategic adjustment of the enterprise, data means a huge return on investment.

Central enterprises big data management opportunities and challenges co-exist

The development of big data has different impacts on state-owned enterprises in different industries, development stages and sizes. Large central enterprises, in particular, have a relatively high starting point in utilizing big data and benefit more. For central enterprises, what does big data mean for their operation and management?

First, the opportunity. One is reflected in the information technology construction investment. Large central enterprises have the strength to invest in enterprise information technology, the application of more advanced technology to ensure the effective management and utilization of enterprise data. In addition, the management of state-owned enterprises has strong continuity and is generally more stable. Second, it is reflected in the top-level design. Large central enterprises have advantages in the top-level design of big data management, and can carry out systematic planning for enterprise data management. Third, it is reflected in the policy advantage and talent team.

Second, the challenges. First, the information system is very urgent. Generally large state-owned enterprises have a huge amount of data, from the level of information mining, which requires a reasonable technology with. In addition, from the organizational structure, big data on the close cooperation between the information technology department and the business sector has put forward higher requirements. Second, attention is paid to information security prevention. Third, the talent pool is insufficient, and the attraction and training level of relevant data mining and analyzing talents need to be improved.

Central enterprises to carry out the exploration and prospects of big data management

How to carry out big data management? For domestic central enterprises, there should be a path of big data management in line with their own development characteristics, in the construction of information technology, to create a "data-based enterprise".

First, do a good job of screening and evaluating big data assets. For domestic central enterprises, this is divided into two stages: before and after. Ex ante is to pay attention to the impact of big data on the enterprise from the ideological point of view, the data as the core resources of the enterprise to view. The aftermath is to screen big data from resources to assets within the enterprise, and assess what kind of big data can be an asset.

Second, intensively carry out top-level design and systematic planning. Large central enterprises have many subordinate units, different enterprise management structures and relatively complex situations. In order to give full play to the advantages of the system, it is necessary to carry out a unified scientific design of datatization, avoiding duplication of construction, each in its own way, incompatible with each other, and giving full play to the role of information technology in data analysis.

Third, strengthen data management and emphasize data security. In terms of data management, central enterprises can combine the existing enterprise information construction, enterprise data management to the depth. Data management is related to the core competitiveness of enterprises and strategic objectives, must have a strategic height. Data collection and management should "cast a wide net" and utilize the synergistic effect of various departments. Not only should we pay attention to comprehensive data and key data, but also to basic data, in-depth utilization and mining of data. At the same time, special attention should be paid to data security, from the technical and institutional levels to ensure data security.

Fourth, optimize internal operation mode and strengthen external cooperation. Central enterprises should establish a customer-oriented value service orientation, reformulate and optimize enterprise systems and processes in response to demand, increase data collection, management and analysis links, and design business models and internal operation models that adapt to market competition. To strengthen cooperation with external parties. Communicate and cooperate with external enterprises, research institutes, industry associations and other organizations to achieve complementary data technologies, resources and platforms. At the same time, it is necessary to strengthen the data management cooperation of relevant enterprises in the upstream and downstream industrial chain, and to carry out mutual assistance in data collection, analysis, and **** enjoyment.

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