CN104933531A - Data analysis method and data analysis device - Google Patents
Data analysis method and data analysis device Download PDFInfo
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- CN104933531A CN104933531A CN201510374627.8A CN201510374627A CN104933531A CN 104933531 A CN104933531 A CN 104933531A CN 201510374627 A CN201510374627 A CN 201510374627A CN 104933531 A CN104933531 A CN 104933531A
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Abstract
The invention discloses a data analysis method and a data analysis device, which relate to a data processing technology and are used for reducing the manpower resource management cost. The data analysis method provided by the invention comprises the following steps of: obtaining each sampling datum, and obtaining the sub PES (Performance Evaluation System) value of each sampling datum; obtaining target sampling data with the sub PES value being smaller than the preset value according to the sub PES value of each sampling datum; and determining target sampling data to be processed in the target sampling data. The data analysis method and the data analysis device are mainly used in a salary calculation technology and can reduce the manpower resource management cost.
Description
Technical field
The present invention relates to data processing technique, particularly a kind of data analysing method and device.
Background technology
Cost of human resources is the key factor affecting each company or enterprise development.But existing human resource management scheme is all the demand by analysis company or corporate client mostly, thus the service of manpower resource base or traditional consulting scheme are proposed.But the problem that the discovery employee that this scheme can not be real thinks, thus the manpower management problem that effectively cannot solve company or enterprise, thus make human resource management cost compare high.
Summary of the invention
The invention provides a kind of data analysing method and device, in order to reduce the cost of human resource management.
A kind of data analysing method provided by the invention, comprising:
Obtain each sampled data, and obtain the filial duties position evaluating system PES value of each sampled data;
The destination sample data of preset value are less than according to the sub-PES value of sub-PES value acquisition of each sampled data;
Pending destination sample data are determined in described destination sample data
Wherein, each sampled data of described acquisition, and the sub-PES value obtaining each sampled data comprises:
Obtain each sampled data;
Obtain the first scoring rank number that each sampled data is corresponding respectively, the second scoring rank number and the 3rd scoring rank number;
The the first scoring rank number utilizing described each sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain the sub-PES value of each sampled data;
Wherein, the sub-PES value of described each sampled data=C-A/A+B+C, A represents that the 3rd scoring rank number that each sampled data is corresponding, B represent the second scoring rank number that each sampled data is corresponding; C represents the first scoring rank number that each sampled data is corresponding.
Wherein, describedly in described destination sample data, determine that pending destination sample data comprise:
In described destination sample data, determine that the 3rd scoring rank number meets the destination sample data of preset requirement, described 3rd scoring rank number is met the destination sample data of preset requirement as pending destination sample data.
Wherein, described method also comprises: obtain ultimate PES value, comprising:
Obtain the first scoring rank number that ultimate sampled data is corresponding, the second scoring rank number and the 3rd scoring rank number;
The the first scoring rank number utilizing described ultimate sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain ultimate PES value;
Wherein, described ultimate PES value=C1-A1/A1+B1+C1, A1 represents that the 3rd scoring rank number that ultimate sampled data is corresponding, B1 represent the second scoring rank number that ultimate sampled data is corresponding; C1 represents the first scoring rank number that ultimate sampled data is corresponding.
A kind of data analysis set-up provided by the invention, comprising:
First acquiring unit, for obtaining each sampled data, and obtains the filial duties position evaluating system PES value of each sampled data;
Second acquisition unit, for obtaining the destination sample data that sub-PES value is less than preset value according to the sub-PES value of each sampled data;
Choose unit, for determining pending destination sample data in described destination sample data.
Wherein, described first acquiring unit comprises:
First data acquisition module, for obtaining each sampled data;
First number acquisition module, for obtaining the first scoring rank number corresponding to each sampled data respectively, the second scoring rank number and the 3rd scoring rank number;
First computing module, for the first scoring rank number utilizing described each sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain the sub-PES value of each sampled data;
Wherein, the sub-PES value of described each sampled data=C-A/A+B+C, A represents that the 3rd scoring rank number that each sampled data is corresponding, B represent the second scoring rank number that each sampled data is corresponding; C represents the first scoring rank number that each sampled data is corresponding.
Wherein, described choose unit specifically for: in described destination sample data, determine that the 3rd scoring rank number meets the destination sample data of preset requirement, described 3rd scoring rank number met the destination sample data of preset requirement as pending destination sample data.
Wherein, described device also comprises: the 3rd acquiring unit; Described 3rd acquiring unit comprises:
Second number acquisition module, for obtaining the first scoring rank number corresponding to ultimate sampled data, the second scoring rank number and the 3rd scoring rank number;
Second computing unit, for the first scoring rank number utilizing described ultimate sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain ultimate PES value;
Wherein, described ultimate PES value=C1-A1/A1+B1+C1, A1 represents that the 3rd scoring rank number that ultimate sampled data is corresponding, B1 represent the second scoring rank number that ultimate sampled data is corresponding; C1 represents the first scoring rank number that ultimate sampled data is corresponding.
Compared with prior art, the invention has the beneficial effects as follows:
In embodiments of the present invention, obtain the sub-PES value of each sampled data, and the destination sample data of preset value are less than according to the sub-PES value of sub-PES value acquisition of each sampled data, then from destination sample data, pending destination sample data are determined, thus make user clearly can to understand each sampled data to the impact of human resource management cost to determine rational processing policy, reduce the cost of human resource management.
Below by drawings and Examples, technical scheme of the present invention is described in further detail.
Accompanying drawing explanation
Accompanying drawing is used to provide a further understanding of the present invention, and forms a part for instructions, together with embodiments of the present invention for explaining the present invention, is not construed as limiting the invention.In the accompanying drawings:
Fig. 1 is the process flow diagram of the data analysing method of the embodiment of the present invention one;
Fig. 2 is the structural drawing of the data analysis set-up of the embodiment of the present invention two.
Fig. 3 is the schematic diagram of the data analysis set-up of the embodiment of the present invention two.
Embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described, should be appreciated that preferred embodiment described herein is only for instruction and explanation of the present invention, is not intended to limit the present invention.
As shown in Figure 1, the data analysing method of the embodiment of the present invention one, comprising:
Step 11, obtain each sampled data, and obtain sub-PES (posts evaluating system, the Performance Evaluation System) value of each sampled data.
In embodiments of the present invention, sampled data mainly refers to and investigates a matter with relevant various of human resources, is divided into underlying issue, ultimate problem and secondary issues.These problems can store in a database.Such as, underlying issue can be the problems such as " age " " hiring date ", can be arranged as required by user.Ultimate problem can be " if from 0 to 10 marking, the possibility that you recommend your friend or relatives to carry out our company's work has much? ", generally set by system default.Secondary issues can be " corporate culture whether you admit our company ", " your working pressure is large? " etc. problem, can be arranged as required by user.In this step, described sampled data refers to secondary issues, and so sub-PES value refers to PES value corresponding to each secondary issues.
Concrete, in this step, first obtain each sampled data, then obtain the first scoring rank number that each sampled data is corresponding respectively, the second scoring rank number and the 3rd scoring rank number.Finally, the first scoring rank number utilizing described each sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain the sub-PES value of each sampled data.Wherein, the sub-PES value of described each sampled data=C-A/A+B+C, A represents that the 3rd scoring rank number that each sampled data is corresponding, B represent the second scoring rank number that each sampled data is corresponding; C represents the first scoring rank number that each sampled data is corresponding.
Wherein, to mark, full marks 10 are divided into example, and the first scoring rank number can be the headcount of scoring for 0-6, and the second scoring rank number can be the headcount of scoring for 7-8, and the 3rd scoring rank number can be the headcount of scoring for 9-10.If the higher degree of expressing one's approval of mark is higher, the first employee corresponding to scoring rank can think passive employee, and employee corresponding to the second scoring rank can think passive employee, and employee corresponding to the 3rd scoring rank can think positive employee.
Such as, for " whether you admit the corporate culture of our company " this secondary issues, the first scoring rank number of its correspondence is 10, and the second scoring rank number is 20,3rd scoring rank number is 30, so sub-PES value=10-30/10+20+30=-0.33 of this problem.
Step 12, to obtain the destination sample data that sub-PES value is less than preset value according to the sub-PES value of each sampled data.
For the sub-PES value that each secondary issues obtained in step 11 is corresponding, first can sort according to ascending order to it, then, choose the destination sample data that preset value is less than certain PES value.Such as, the sampled data that 5 PES values can be selected minimum as in this destination sample data, and is labeled as Q1 them, Q2, Q3, Q4, Q5 respectively.
Step 13, in described destination sample data, determine pending destination sample data.
In this step, in described destination sample data, mainly determine that the 3rd scoring rank number meets the destination sample data of preset requirement, described 3rd scoring rank number is met the destination sample data of preset requirement as pending destination sample data.Wherein, concrete which destination sample data of selecting can be determined according to embody rule situation as pending destination sample data.Such as, can using destination sample data maximum for the 3rd scoring rank number as pending destination sample data, or can be that the destination sample data of front two are as pending destination sample data using the 3rd scoring rank number.
For each destination sample data obtained in step 12, mainly determine which target data is the sampled data had the greatest impact to ultimate PES in this step.For above-mentioned examples cited, mainly determine that the number of the passive employee which sampled data is corresponding is maximum in this step, so this sampled data is just maximum on the impact of ultimate PES, namely it can be used as pending destination sample data, thus determines the processing policy to this sampled data.For the example in step 12, the passive number of the passive number of the passive number of Q1 to be the passive number of 5, Q2 be 3, Q3 to be the passive number of 10, Q4 be 15, Q5 is 12, so can using Q4 as in this pending destination sample data.
By describing above and can finding out, in embodiments of the present invention, obtain the sub-PES value of each sampled data, and the destination sample data of preset value are less than according to the sub-PES value of sub-PES value acquisition of each sampled data, then from destination sample data, pending destination sample data are determined, thus make user clearly can to understand each sampled data to the impact of human resource management cost to determine rational processing policy, reduce the cost of human resource management.
In addition, be the human resource management situation making user better can determine enterprise, further reduce human resource management cost, on the basis of the embodiment shown in Fig. 1, the method for the embodiment of the present invention also can comprise:
Step 14, obtain ultimate PES value.
Concrete, the ultimate PES value of its correspondence can be determined for ultimate problem.In this step, obtain the first scoring rank number that ultimate sampled data is corresponding, second scoring rank number and the 3rd scoring rank number, then the first scoring rank number utilizing described ultimate sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain ultimate PES value.Wherein, described ultimate PES value=C1-A1/A1+B1+C1, A1 represents that the 3rd scoring rank number that ultimate sampled data is corresponding, B1 represent the second scoring rank number that ultimate sampled data is corresponding; C1 represents the first scoring rank number that ultimate sampled data is corresponding.
Wherein, to mark, full marks 10 are divided into example, and the first scoring rank number can be the headcount of scoring for 0-6, and the second scoring rank number can be the headcount of scoring for 7-8, and the 3rd scoring rank number can be the headcount of scoring for 9-10.Equally, if the higher degree of expressing one's approval of mark is higher, the first employee corresponding to scoring rank can think passive employee, and employee corresponding to the second scoring rank can think passive employee, and employee corresponding to the 3rd scoring rank can think positive employee.
Such as, for above-mentioned ultimate problem, the first scoring rank number of its correspondence is 20, and the second scoring rank number is 10, and the 3rd scoring rank number is 30, so ultimate PES value=30-10/10+20+30=0.33 of this problem.
As shown in Figure 2, the data analysis set-up of the embodiment of the present invention two, comprising:
First acquiring unit 21, for obtaining each sampled data, and obtains the filial duties position evaluating system PES value of each sampled data; Second acquisition unit 22, for obtaining the destination sample data that sub-PES value is less than preset value according to the sub-PES value of each sampled data; Choose unit 23, for determining pending destination sample data in described destination sample data.
Wherein, described first acquiring unit 21 comprises: the first data acquisition module, for obtaining each sampled data; First number acquisition module, for obtaining the first scoring rank number corresponding to each sampled data respectively, the second scoring rank number and the 3rd scoring rank number; First computing module, for the first scoring rank number utilizing described each sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain the sub-PES value of each sampled data; Wherein, the sub-PES value of described each sampled data=C-A/A+B+C, A represents that the 3rd scoring rank number that each sampled data is corresponding, B represent the second scoring rank number that each sampled data is corresponding; C represents the first scoring rank number that each sampled data is corresponding.
Wherein, described choose unit concrete 23 for: in described destination sample data, determine that the 3rd scoring rank number meets the destination sample data of preset requirement, described 3rd scoring rank number met the destination sample data of preset requirement as pending destination sample data.
As shown in Figure 3, be the human resource management situation making user better can determine enterprise, further reduce human resource management cost, described device also comprises: the 3rd acquiring unit 24.Wherein, described 3rd acquiring unit 24 comprises: the second number acquisition module, for obtaining the first scoring rank number corresponding to ultimate sampled data, the second scoring rank number and the 3rd scoring rank number; Second computing unit, for the first scoring rank number utilizing described ultimate sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain ultimate PES value; Wherein, described ultimate PES value=C1-A1/A1 ten B1+C1, A1 represent that the 3rd scoring rank number that ultimate sampled data is corresponding, B1 represent the second scoring rank number that ultimate sampled data is corresponding; C1 represents the first scoring rank number that ultimate sampled data is corresponding.
Described in the embodiment of the present invention, the principle of work of device can refer to the description of preceding method embodiment.
By describing above and can finding out, in embodiments of the present invention, obtain the sub-PES value of each sampled data, and the destination sample data of preset value are less than according to the sub-PES value of sub-PES value acquisition of each sampled data, then from destination sample data, pending destination sample data are determined, thus make user clearly can to understand each sampled data to the impact of human resource management cost to determine rational processing policy, reduce the cost of human resource management.
Obviously, those skilled in the art can carry out various change and modification to the present invention and not depart from the spirit and scope of the present invention.Like this, if these amendments of the present invention and modification belong within the scope of the claims in the present invention and equivalent technologies thereof, then the present invention is also intended to comprise these change and modification.
Claims (8)
1. a data analysing method, is characterized in that, comprising:
Obtain each sampled data, and obtain the filial duties position evaluating system PES value of each sampled data;
The destination sample data of preset value are less than according to the sub-PES value of sub-PES value acquisition of each sampled data;
Pending destination sample data are determined in described destination sample data.
2. method according to claim 1, is characterized in that, each sampled data of described acquisition, and the sub-PES value obtaining each sampled data comprises:
Obtain each sampled data;
Obtain the first scoring rank number that each sampled data is corresponding respectively, the second scoring rank number and the 3rd scoring rank number;
The the first scoring rank number utilizing described each sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain the sub-PES value of each sampled data;
Wherein, the sub-PES value of described each sampled data=C-A/A+B+C, A represents that the 3rd scoring rank number that each sampled data is corresponding, B represent the second scoring rank number that each sampled data is corresponding; C represents the first scoring rank number that each sampled data is corresponding.
3. method according to claim 1, is characterized in that, describedly in described destination sample data, determines that pending destination sample data comprise:
In described destination sample data, determine that the 3rd scoring rank number meets the destination sample data of preset requirement, described 3rd scoring rank number is met the destination sample data of preset requirement as pending destination sample data.
4., according to the arbitrary described method of claim 1-3, described method also comprises: obtain ultimate PES value, comprising:
Obtain the first scoring rank number that ultimate sampled data is corresponding, the second scoring rank number and the 3rd scoring rank number;
The the first scoring rank number utilizing described ultimate sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain ultimate PES value;
Wherein, described ultimate PES value=C1-A1/A1+B1+C1, A1 represents that the 3rd scoring rank number that ultimate sampled data is corresponding, B1 represent the second scoring rank number that ultimate sampled data is corresponding; C1 represents the first scoring rank number that ultimate sampled data is corresponding.
5. a data analysis set-up, is characterized in that, comprising:
First acquiring unit, for obtaining each sampled data, and obtains the filial duties position evaluating system PES value of each sampled data;
Second acquisition unit, for obtaining the destination sample data that sub-PES value is less than preset value according to the sub-PES value of each sampled data;
Choose unit, for determining pending destination sample data in described destination sample data.
6. device according to claim 5, is characterized in that, described first acquiring unit comprises:
First data acquisition module, for obtaining each sampled data;
First number acquisition module, for obtaining the first scoring rank number corresponding to each sampled data respectively, the second scoring rank number and the 3rd scoring rank number;
First computing module, for the first scoring rank number utilizing described each sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain the sub-PES value of each sampled data;
Wherein, the sub-PES value of described each sampled data=C-A/A+B+C, A represents that the 3rd scoring rank number that each sampled data is corresponding, B represent the second scoring rank number that each sampled data is corresponding; C represents the first scoring rank number that each sampled data is corresponding.
7. device according to claim 5, it is characterized in that, described choose unit specifically for: in described destination sample data, determine that the 3rd scoring rank number meets the destination sample data of preset requirement, described 3rd scoring rank number met the destination sample data of preset requirement as pending destination sample data.
8., according to the arbitrary described device of claim 5-7, it is characterized in that, described device also comprises: the 3rd acquiring unit; Described 3rd acquiring unit comprises:
Second number acquisition module, for obtaining the first scoring rank number corresponding to ultimate sampled data, the second scoring rank number and the 3rd scoring rank number;
Second computing unit, for the first scoring rank number utilizing described ultimate sampled data corresponding, the second scoring rank number and the 3rd scoring rank number obtain ultimate PES value;
Wherein, described ultimate PES value=C1-A1/A1+B1+C1, A1 represents that the 3rd scoring rank number that ultimate sampled data is corresponding, B1 represent the second scoring rank number that ultimate sampled data is corresponding; C1 represents the first scoring rank number that ultimate sampled data is corresponding.
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| CN106056283A (en) * | 2016-05-31 | 2016-10-26 | 浪潮软件集团有限公司 | Performance assessment method and device |
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