Human resource data management method

Abstract

The invention relates to the technical field of human resource data management, and discloses a human resource data management method, which comprises the following steps of: 1, establishing a unified data collection platform, realizing automatic integration of multi-source human resource data, and ensuring the consistency and accuracy of the data through data cleaning and standardization processing; step 2, performing intelligent classification and tagging on the human resource data by using a machine learning algorithm, and automatically identifying employee key information; 3, constructing a real-time data monitoring system, and continuously monitoring the key human resource indexes; and 4, carrying out deep mining on the human resource data by adopting a big data analysis technology to obtain a trend of the data, and analyzing key information associated data in the trend. Compared with the prior art, the method has the advantages that the comprehensive data collection can be ensured, the data processing efficiency is improved, and the data can be deeply analyzed so as to obtain a more accurate analysis result.

Description

Technical Field

[0001] The present invention relates to the technical field of human resource data management, and in particular to a human resource data management method. Background Art

[0002] At present, with the expansion of enterprise scale and the increase of business complexity, the human resources data contained therein are numerous and large in volume. It is difficult to manage the numerous and large human resources data using traditional human resources management methods, which may lead to data omissions and insufficient analysis depth. When managing human resources data, data collection is not comprehensive, processing efficiency is low, and analysis is not in-depth, which will lead to deviations in the results of management analysis, thereby affecting managers' decision-making and other judgments. Summary of the invention

[0003] 1. Technical issues to be solved

[0004] The technical problem to be solved by the present invention is that when the traditional method is currently used to manage human resource data for enterprises with a wide variety and large amount of human resource data, data omissions may occur due to incomplete data collection, low processing efficiency, and lack of in-depth analysis, which may cause deviations in the generated results.

[0005] (II) Technical solution

[0006] In order to solve the above technical problems, the technical solution provided by the present invention is: a human resources data management method, comprising: step one: establishing a unified data collection platform to realize the automatic integration of multi-source human resources data, and ensuring the consistency and accuracy of the data through data cleaning and standardization processing; step two: using machine learning algorithms to intelligently classify and label human resources data, and automatically identify key information of employees; step three: building a real-time data monitoring system to continuously monitor key human resources indicators, the data monitoring system includes an early warning mechanism, the continuous monitoring objects are to monitor employee turnover rate K and performance fluctuation data, and the early warning mechanism is used to monitor employee turnover rate and performance fluctuation data;

[0007]

[0008] K is the monitored employee turnover rate, a is the number of employees lost in the month, a1 is the number of employees at the beginning of the month, a2 is the number of employees at the end of the month, and the employee turnover rate monitoring frequency is once at the beginning and end of each month; the performance fluctuation data is the average performance of all employees;

[0009]

[0010] P is the average performance, M n is the performance of each employee, N is the number of employees;

[0011]

[0012] K1 and K2 are the employee turnover rates for two consecutive months;

[0013]

[0014] P1 and P2 are the average performance of two consecutive months; when the value of C1 or C2 is greater than the warning threshold, the warning mechanism will sound an alarm; Step 4: Use big data analysis technology to conduct in-depth mining of human resources data, derive data trends, and analyze key information related data in the trend.

[0015] As an improvement, the data collection platform in step one collects employee files, attendance records, performance evaluations and training material data.

[0016] As an improvement, the data cleaning method in step one includes deleting duplicate data and detecting outliers.

[0017] As an improvement, the key employee information in step 2 includes employee characteristics, work performance and development potential.

[0018] As an improvement, the continuous monitoring in step three is to monitor employee turnover rate and performance fluctuation data.

[0019] As an improvement, the data monitoring system includes an early warning mechanism, which is used to monitor employee turnover rate and performance fluctuation data.

[0020] As an improvement, the key information related data of step 4 include related data of employee satisfaction and turnover rate, and related data of training effect and employee performance.

[0021] As an improvement, after analyzing the data, personalized management strategies are tailored for each employee based on the data analysis results.

[0022] As an improvement, the personalized management strategy includes career development path planning and training plan customization.

[0023] As an improvement, the real-time data monitoring system includes: a data acquisition component: acquiring data in the database online or offline; a data processing component: processing the collected data through data cleaning, data conversion and data aggregation; a data display component: displaying the processing results in a visual manner.

[0024] (III) Beneficial effects

[0025] Compared with the existing technology, the advantages of the present invention are: establishing a unified data collection platform, and using algorithms to intelligently classify and label human resource data, which can achieve comprehensive integration and efficient management of human resource data, while using a real-time data monitoring system to continuously monitor key human resource indicators and issue early warnings for abnormal data, which can help companies to promptly discover and respond to potential risks. After in-depth analysis of the data, it can help managers effectively and accurately discover data trends and make strategies based on evidence. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a human resources data management method of the present invention.

[0027] Figure 2 It is a system principle diagram of a real-time data monitoring system of a human resource data management method of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] A human resources data management method, comprising: step one: establishing a unified data collection platform to realize automatic integration of multi-source human resources data, and ensuring data consistency and accuracy through data cleaning and standardization processing, wherein the data collection platform of step one collects employee files, attendance records, performance evaluation and training material data, and the data cleaning method of step one includes deleting duplicate data and detecting outliers;

[0030] Step 2: Use machine learning algorithms to intelligently classify and label human resource data and automatically identify key employee information. The key employee information in step 2 includes employee characteristics, work performance, and development potential. Step 3: Build a real-time data monitoring system to continuously monitor key human resource indicators. The continuous monitoring in step 3 is to monitor employee turnover rate and performance fluctuation data. The data monitoring system includes an early warning mechanism, which is used to monitor employee turnover rate and performance fluctuation data. The formula calculates the monthly employee turnover rate. Calculate the average performance of enterprise employees, and then calculate the difference between the employee turnover rate and the average performance for two consecutive months, and then calculate C1 and C2, so as to issue an early warning. The real-time data monitoring system includes: data acquisition component: obtain data in the database online or offline; data processing component: process the collected data through data cleaning, data conversion and data aggregation; data display component: display the processing results in a visual way;

[0031] Step 4: Use big data analysis technology to conduct in-depth mining of human resources data, obtain data trends, and analyze key information related data in the trends. The key information related data in step 4 include the related data between employee satisfaction and turnover rate, and the related data between training effects and employee performance. After analyzing the data, a personalized management strategy is tailored for each employee based on the data analysis results. The personalized management strategy includes career development path planning and training plan customization.

[0032] In the specific implementation of the present invention, after establishing a unified data collection platform, all kinds of human resource data are automatically collected, and after the collection, duplicate data are deleted, outliers are detected and standardized for employee files, attendance records, performance evaluations and training materials data, so as to ensure the consistency and accuracy of the data. After the collection is completed, the data is classified and labeled by using algorithms, and key information such as employee characteristics, work performance and development potential is identified. Then a real-time data monitoring system is constructed to continuously monitor key human resource indicators. During the monitoring process, the early warning mechanism can issue early warnings for abnormal monitoring employee turnover rate and performance fluctuation data;

[0033] Using big data analysis technology to conduct in-depth mining of human resources data can reveal data trends, and analyze the correlation data between employee satisfaction and turnover rate, and the correlation data between training effectiveness and employee performance, thereby deriving career development path planning and training plans tailored for each employee, which can help managers make effective and accurate strategic decisions.

[0034] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0035] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0036] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A human resources data management method, characterized in that: include: Step 1: Establish a unified data collection platform to achieve automatic integration of multi-source human resources data, and ensure data consistency and accuracy through data cleaning and standardization; Step 2: Use machine learning algorithms to intelligently classify and label human resources data and automatically identify key employee information; Step 3: Build a real-time data monitoring system to continuously monitor key human resource indicators. The data monitoring system includes an early warning mechanism. The continuous monitoring objects are employee turnover rate K and performance fluctuation data. The early warning mechanism is used to monitor employee turnover rate and performance fluctuation data. K is the monitored employee turnover rate, a is the number of employees lost in the month, a1 is the number of employees at the beginning of the month, a2 is the number of employees at the end of the month, and the employee turnover rate monitoring frequency is once at the beginning and end of each month; Performance fluctuation data is the average performance of all employees; P is the average performance, M n is the performance of each employee, N is the number of employees; K1 and K2 are the employee turnover rates for two consecutive months; P1 and P2 are the average performance of two consecutive months; When the value of C1 or C2 is greater than the warning threshold, the warning mechanism will sound an alarm. Step 4: Use big data analysis technology to conduct in-depth mining of human resources data, obtain data trends, and analyze key information related data in the trends.

2. A human resources data management method according to claim 1, characterized in that: The data collection platform in step 1 collects employee files, attendance records, performance evaluations and training material data.

3. A human resources data management method according to claim 1, characterized in that: The data cleaning method in step one includes deleting duplicate data and detecting outliers.

4. A human resources data management method according to claim 1, characterized in that: The key employee information in step 2 includes employee characteristics, work performance and development potential.

5. A human resources data management method according to claim 1, characterized in that: The key information related data of step 4 include the related data of employee satisfaction and turnover rate, and the related data of training effect and employee performance.

6. A human resources data management method according to claim 1, characterized in that: After analyzing the data, personalized management strategies are tailored for each employee based on the data analysis results.

7. A human resources data management method according to claim 6, characterized in that: The personalized management strategy includes career development path planning and training plan customization.

8. A human resources data management method according to claim 1, characterized in that: The real-time data monitoring system comprises: Data collection component: obtain data from the database online or offline; Data processing component: processes the collected data through data cleaning, data conversion and data aggregation; Data display component: displays the processing results in a visual way.

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