(A) awareness-raising and scientific management
The scientific management of data can only be realized and valued at the strategic level. Data is the basis of major software applications. All enterprise data are finally collected and stored in the database of the computer system. Staff get the required data from the background database through the information interaction system, and get the results after being processed by the middle-level information system. All surveys and analyses need true, comprehensive, accurate and consistent data. Some problems in enterprise informatization construction are mainly not because there is no good system, but because the existing system has not been well applied. Therefore, the accuracy, completeness and scientificity of the data will directly determine the correctness of the results. It will also affect the effectiveness of information application. At the same time, only scientific management can ensure the accuracy and integrity of data.
(two) improve the functional departments and improve the management system.
Because the data management function should be implemented by a special department, it is necessary to set up a special data management leading group and data management (processing) department, and entrust the data management department with the responsibility of data supervision, which will centrally manage the monitoring data and cooperate with all relevant responsible departments. Each unit has also set up corresponding data processing posts. Then, the "Measures for Data Management" and the "Interim Measures for the Investigation of Data Management Responsibility" were promulgated, which clarified the scope of responsibility, working procedures, monitoring content, assessment, rewards and punishments, etc. Establish data notification, training and other systems, formulate relevant measures such as information collection, review, entry, analysis and comparison, information transmission, and gradually standardize data supervision and application.
(3) Strictly input data and strengthen source control.
The first is to improve the quality of personnel. Train data entry personnel in software operation, data entry, responsibilities and other knowledge, clarify their responsibilities, and clarify the job responsibilities and quality standards of data management personnel at all levels and positions; Define the procedures for submitting, handling and feedback problems in the integrated management software, and the data management department is responsible for receiving, researching and solving problems and giving feedback, so as to avoid multi-head submission and multi-head instruction and provide personnel quality guarantee for data management.
The second is to strengthen the information system, improve the error correction function of the system itself, and reduce or avoid data entry errors.
The third is to create a reasonable and efficient workflow. According to the actual situation, formulate the workflow, clarify responsibilities, avoid duplication, facilitate management, refine posts, scientifically connect posts, organize efficient workflow, reduce data redundancy, and maximize the efficiency of collection and management.
The fourth is to act according to principles. Strictly control the collection, review, approval, entry and modification of data according to the principle of "three non-records", that is, non-standard, unsafe and unaudited. Ensure that the system data is complete and accurate, and the system runs with high quality and high efficiency.
The fifth is to inform the assessment. Establish a notification system. For example, adhere to "January 1st bulletin, January 1st appraisal, January 1st assessment and January 1st investigation". Complete the data quality collected and managed by each unit on time, and publish the data bulletin on the document processing system and website. At the monthly executive meeting, the competent leader will report the data quality of last month, analyze the crux and propose corrective measures. Establish daily assessment ledger, monthly assessment, and post the scores of each unit; At the same time, in accordance with the accountability measures, investigate the responsibilities of relevant units and personnel. The annual data quality assessment ranked the last few, in the target management assessment points. Formulate data assessment indicators. The data quality assessment is lower than that of average index, and the target management assessment is one vote.
(d) Thought should be taken seriously and all staff should participate.
Strengthening data management and comprehensively promoting the application process of enterprise informatization construction cannot be separated from the attention and support of leaders at all levels. Only the attention of leaders is the key to data management and in-depth analysis, and information construction can really develop. At the same time, all staff should manage the data of their respective work links, do not create junk data and erroneous data, solve problems in time when found, trace back to the source, and strive to eliminate erroneous data and junk data to ensure the correctness and integrity of the data.
(5) cooperation should be in place.
In data processing, information technology is the means of realization. The advanced application of information technology determines the quality level of system software, while the standardization of business determines the breadth and depth of informatization promotion. The application of data processing not only involves the selection and application of information technology, but also involves the standardization and unification of enterprise business processes, which directly affects the effectiveness of enterprise system information construction. Therefore, every enterprise management data processing and its specific application can not be separated from the close cooperation and collaborative work of the information department and the business department. Technical departments and business departments need good cooperation, mutual support and cooperation to deepen and improve the application of data processing.
(6) The mechanism should be sound.
On the basis of the established mechanism, it is necessary to further improve the management mode of data analysis and application, and establish departmental responsibility system, including project management system and information release system; Establish an enterprise business support system suitable for data processing applications; Establish information technology support, security and operation and maintenance guarantee system, including information security emergency plan and operation and maintenance post responsibility system. To ensure the healthy and orderly development of data analysis and application.