Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.1 KiB
Average record size in memory84.3 B

Variable types

Text4
Categorical3
Boolean1
Numeric2

Dataset

Description해당 파일 데이터는 신용보증기금의 보증상담결제관계에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092839/fileData.do

Alerts

상담결재역할관계코드 has constant value ""Constant
업무구분코드 has constant value ""Constant
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
상담결재ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:48:32.617503
Analysis finished2023-12-12 16:48:33.917496
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상담ID
Text

Distinct398
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:48:34.133673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique330 ?
Unique (%)66.0%

Sample

1st row9dnSZ3cc0s
2nd row9dnSUvJ26O
3rd row9dnSYCLyKU
4th row9dnSXlyKu4
5th row9dnSZScO74
ValueCountFrequency (%)
9dnohyj3cm 6
 
1.2%
9dnm78ccdc 6
 
1.2%
9dnosti3rd 5
 
1.0%
9dnnkt0kja 5
 
1.0%
9dnm56czxt 5
 
1.0%
9dnsfcuqml 4
 
0.8%
9dnsndc4e1 4
 
0.8%
9dnlicbmnz 4
 
0.8%
9dnspd02tb 3
 
0.6%
9dnskrtuij 3
 
0.6%
Other values (388) 455
91.0%
2023-12-13T01:48:34.557740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 558
 
11.2%
d 552
 
11.0%
n 523
 
10.5%
O 221
 
4.4%
S 175
 
3.5%
M 123
 
2.5%
N 123
 
2.5%
m 79
 
1.6%
C 71
 
1.4%
c 69
 
1.4%
Other values (52) 2506
50.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2260
45.2%
Uppercase Letter 1754
35.1%
Decimal Number 986
19.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 552
24.4%
n 523
23.1%
m 79
 
3.5%
c 69
 
3.1%
i 60
 
2.7%
f 58
 
2.6%
k 56
 
2.5%
r 54
 
2.4%
l 53
 
2.3%
g 52
 
2.3%
Other values (16) 704
31.2%
Uppercase Letter
ValueCountFrequency (%)
O 221
 
12.6%
S 175
 
10.0%
M 123
 
7.0%
N 123
 
7.0%
C 71
 
4.0%
Z 69
 
3.9%
Q 67
 
3.8%
K 62
 
3.5%
J 58
 
3.3%
Y 56
 
3.2%
Other values (16) 729
41.6%
Decimal Number
ValueCountFrequency (%)
9 558
56.6%
0 59
 
6.0%
5 52
 
5.3%
4 51
 
5.2%
3 49
 
5.0%
7 48
 
4.9%
6 45
 
4.6%
8 45
 
4.6%
1 43
 
4.4%
2 36
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4014
80.3%
Common 986
 
19.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 552
 
13.8%
n 523
 
13.0%
O 221
 
5.5%
S 175
 
4.4%
M 123
 
3.1%
N 123
 
3.1%
m 79
 
2.0%
C 71
 
1.8%
c 69
 
1.7%
Z 69
 
1.7%
Other values (42) 2009
50.0%
Common
ValueCountFrequency (%)
9 558
56.6%
0 59
 
6.0%
5 52
 
5.3%
4 51
 
5.2%
3 49
 
5.0%
7 48
 
4.9%
6 45
 
4.6%
8 45
 
4.6%
1 43
 
4.4%
2 36
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 558
 
11.2%
d 552
 
11.0%
n 523
 
10.5%
O 221
 
4.4%
S 175
 
3.5%
M 123
 
2.5%
N 123
 
2.5%
m 79
 
1.6%
C 71
 
1.4%
c 69
 
1.4%
Other values (52) 2506
50.1%

상담결재역할관계코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 500
100.0%

Length

2023-12-13T01:48:34.721857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:48:34.843901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

상담결재ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:48:35.075362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st row9dnS0fGzdh
2nd row9dnSXbVc5C
3rd row9dnSYVpIIr
4th row9dnSYhqdjo
5th row9dnS0ala9v
ValueCountFrequency (%)
9dns0fgzdh 1
 
0.2%
9dnomwgoji 1
 
0.2%
9dnof6etoi 1
 
0.2%
9dnogqqrhy 1
 
0.2%
9dnolrsy13 1
 
0.2%
9dnokvyaau 1
 
0.2%
9dnomg2zte 1
 
0.2%
9dnomjejiw 1
 
0.2%
9dnomndewn 1
 
0.2%
9dnomsxzsi 1
 
0.2%
Other values (490) 490
98.0%
2023-12-13T01:48:35.443764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 564
 
11.3%
d 551
 
11.0%
9 543
 
10.9%
O 267
 
5.3%
S 207
 
4.1%
N 118
 
2.4%
M 100
 
2.0%
X 68
 
1.4%
6 64
 
1.3%
t 62
 
1.2%
Other values (52) 2456
49.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2318
46.4%
Uppercase Letter 1726
34.5%
Decimal Number 956
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 564
24.3%
d 551
23.8%
t 62
 
2.7%
f 61
 
2.6%
o 61
 
2.6%
y 60
 
2.6%
q 58
 
2.5%
g 57
 
2.5%
m 56
 
2.4%
j 54
 
2.3%
Other values (16) 734
31.7%
Uppercase Letter
ValueCountFrequency (%)
O 267
 
15.5%
S 207
 
12.0%
N 118
 
6.8%
M 100
 
5.8%
X 68
 
3.9%
Z 57
 
3.3%
K 55
 
3.2%
C 55
 
3.2%
I 54
 
3.1%
E 52
 
3.0%
Other values (16) 693
40.2%
Decimal Number
ValueCountFrequency (%)
9 543
56.8%
6 64
 
6.7%
3 56
 
5.9%
8 53
 
5.5%
4 50
 
5.2%
7 44
 
4.6%
2 43
 
4.5%
0 36
 
3.8%
5 34
 
3.6%
1 33
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4044
80.9%
Common 956
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 564
 
13.9%
d 551
 
13.6%
O 267
 
6.6%
S 207
 
5.1%
N 118
 
2.9%
M 100
 
2.5%
X 68
 
1.7%
t 62
 
1.5%
f 61
 
1.5%
o 61
 
1.5%
Other values (42) 1985
49.1%
Common
ValueCountFrequency (%)
9 543
56.8%
6 64
 
6.7%
3 56
 
5.9%
8 53
 
5.5%
4 50
 
5.2%
7 44
 
4.6%
2 43
 
4.5%
0 36
 
3.8%
5 34
 
3.6%
1 33
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 564
 
11.3%
d 551
 
11.0%
9 543
 
10.9%
O 267
 
5.3%
S 207
 
4.1%
N 118
 
2.4%
M 100
 
2.0%
X 68
 
1.4%
6 64
 
1.3%
t 62
 
1.2%
Other values (52) 2456
49.1%

업무구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowG
5th rowG

Common Values

ValueCountFrequency (%)
G 500
100.0%

Length

2023-12-13T01:48:35.592511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:48:35.693572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

삭제여부
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
378 
True
122 
ValueCountFrequency (%)
False 378
75.6%
True 122
 
24.4%
2023-12-13T01:48:35.784276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
440 
1
60 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 440
88.0%
1 60
 
12.0%

Length

2023-12-13T01:48:35.950450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:48:36.099944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 440
88.0%
1 60
 
12.0%

처리시각
Text

Distinct492
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:48:36.528977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique484 ?
Unique (%)96.8%

Sample

1st row30:37.0
2nd row30:25.7
3rd row30:15.7
4th row29:49.4
5th row29:18.0
ValueCountFrequency (%)
30:28.4 2
 
0.4%
46:27.0 2
 
0.4%
15:04.2 2
 
0.4%
43:41.6 2
 
0.4%
12:41.4 2
 
0.4%
37:17.5 2
 
0.4%
36:44.3 2
 
0.4%
12:44.4 2
 
0.4%
18:39.1 1
 
0.2%
21:49.8 1
 
0.2%
Other values (482) 482
96.4%
2023-12-13T01:48:37.113716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 342
9.8%
4 340
9.7%
0 321
9.2%
1 306
8.7%
3 304
8.7%
5 301
8.6%
9 161
 
4.6%
8 154
 
4.4%
Other values (2) 271
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 342
13.7%
4 340
13.6%
0 321
12.8%
1 306
12.2%
3 304
12.2%
5 301
12.0%
9 161
6.4%
8 154
6.2%
7 146
5.8%
6 125
 
5.0%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 342
9.8%
4 340
9.7%
0 321
9.2%
1 306
8.7%
3 304
8.7%
5 301
8.6%
9 161
 
4.6%
8 154
 
4.4%
Other values (2) 271
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 342
9.8%
4 340
9.7%
0 321
9.2%
1 306
8.7%
3 304
8.7%
5 301
8.6%
9 161
 
4.6%
8 154
 
4.4%
Other values (2) 271
7.7%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4380.452
Minimum2689
Maximum6200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T01:48:37.306457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2689
5-th percentile3197.95
Q13734
median4110
Q35046
95-th percentile6102.35
Maximum6200
Range3511
Interquartile range (IQR)1312

Descriptive statistics

Standard deviation874.65622
Coefficient of variation (CV)0.1996726
Kurtosis-0.55751867
Mean4380.452
Median Absolute Deviation (MAD)485
Skewness0.55647845
Sum2190226
Variance765023.51
MonotonicityNot monotonic
2023-12-13T01:48:37.441815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3745 7
 
1.4%
2710 7
 
1.4%
4055 7
 
1.4%
5214 6
 
1.2%
4851 6
 
1.2%
4929 6
 
1.2%
5195 6
 
1.2%
3734 5
 
1.0%
6200 5
 
1.0%
4238 5
 
1.0%
Other values (246) 440
88.0%
ValueCountFrequency (%)
2689 1
 
0.2%
2710 7
1.4%
2962 4
0.8%
3055 2
 
0.4%
3060 2
 
0.4%
3078 1
 
0.2%
3082 1
 
0.2%
3083 2
 
0.4%
3173 1
 
0.2%
3178 4
0.8%
ValueCountFrequency (%)
6200 5
1.0%
6199 1
 
0.2%
6194 4
0.8%
6184 4
0.8%
6175 1
 
0.2%
6174 1
 
0.2%
6165 1
 
0.2%
6163 1
 
0.2%
6157 1
 
0.2%
6150 1
 
0.2%

최초처리시각
Text

Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:48:37.844454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)99.6%

Sample

1st row30:37.0
2nd row43:53.0
3rd row10:21.3
4th row00:30.3
5th row29:18.0
ValueCountFrequency (%)
29:02.4 2
 
0.4%
17:15.9 1
 
0.2%
17:10.8 1
 
0.2%
44:12.7 1
 
0.2%
07:19.0 1
 
0.2%
53:03.3 1
 
0.2%
13:30.7 1
 
0.2%
14:09.4 1
 
0.2%
15:02.0 1
 
0.2%
16:20.8 1
 
0.2%
Other values (489) 489
97.8%
2023-12-13T01:48:38.407132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 337
9.6%
4 324
9.3%
3 322
9.2%
0 313
8.9%
5 302
8.6%
1 300
8.6%
6 173
 
4.9%
7 149
 
4.3%
Other values (2) 280
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 337
13.5%
4 324
13.0%
3 322
12.9%
0 313
12.5%
5 302
12.1%
1 300
12.0%
6 173
6.9%
7 149
6.0%
8 147
5.9%
9 133
 
5.3%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 337
9.6%
4 324
9.3%
3 322
9.2%
0 313
8.9%
5 302
8.6%
1 300
8.6%
6 173
 
4.9%
7 149
 
4.3%
Other values (2) 280
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 337
9.6%
4 324
9.3%
3 322
9.2%
0 313
8.9%
5 302
8.6%
1 300
8.6%
6 173
 
4.9%
7 149
 
4.3%
Other values (2) 280
8.0%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct255
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4381.836
Minimum2689
Maximum6200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T01:48:38.572663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2689
5-th percentile3197.95
Q13734
median4110
Q35046
95-th percentile6102.35
Maximum6200
Range3511
Interquartile range (IQR)1312

Descriptive statistics

Standard deviation876.68904
Coefficient of variation (CV)0.20007345
Kurtosis-0.56231255
Mean4381.836
Median Absolute Deviation (MAD)485
Skewness0.55832006
Sum2190918
Variance768583.67
MonotonicityNot monotonic
2023-12-13T01:48:38.715214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4055 7
 
1.4%
2710 7
 
1.4%
3745 7
 
1.4%
4851 6
 
1.2%
5195 6
 
1.2%
5214 6
 
1.2%
4929 6
 
1.2%
6200 5
 
1.0%
4021 5
 
1.0%
5046 5
 
1.0%
Other values (245) 440
88.0%
ValueCountFrequency (%)
2689 1
 
0.2%
2710 7
1.4%
2962 4
0.8%
3055 2
 
0.4%
3060 2
 
0.4%
3078 1
 
0.2%
3082 1
 
0.2%
3083 2
 
0.4%
3173 1
 
0.2%
3178 4
0.8%
ValueCountFrequency (%)
6200 5
1.0%
6199 1
 
0.2%
6194 4
0.8%
6184 4
0.8%
6175 1
 
0.2%
6174 1
 
0.2%
6165 1
 
0.2%
6163 1
 
0.2%
6157 1
 
0.2%
6150 1
 
0.2%

Interactions

2023-12-13T01:48:33.138382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:32.942728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:33.236863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:33.040820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:48:38.813925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
삭제여부최종수정수처리직원번호최초처리직원번호
삭제여부1.0000.3060.5600.565
최종수정수0.3061.0000.1270.132
처리직원번호0.5600.1271.0001.000
최초처리직원번호0.5650.1321.0001.000
2023-12-13T01:48:38.902475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수삭제여부
최종수정수1.0000.198
삭제여부0.1981.000
2023-12-13T01:48:38.991537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리직원번호최초처리직원번호삭제여부최종수정수
처리직원번호1.0001.0000.4290.098
최초처리직원번호1.0001.0000.4330.102
삭제여부0.4290.4331.0000.198
최종수정수0.0980.1020.1981.000

Missing values

2023-12-13T01:48:33.357080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:48:33.499146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

상담ID상담결재역할관계코드상담결재ID업무구분코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnSZ3cc0s19dnS0fGzdhGN130:37.0421630:37.04216
19dnSUvJ26O19dnSXbVc5CGN230:25.7423243:53.04232
29dnSYCLyKU19dnSYVpIIrGN230:15.7401910:21.34019
39dnSXlyKu419dnSYhqdjoGN229:49.4336800:30.33368
49dnSZScO7419dnS0ala9vGN129:18.0405029:18.04050
59dnJBK3co819dnSZ3TxqUGN127:42.7513627:42.75136
69dnlkd3D0b19dnSZ3MZq3GN127:41.2405927:41.24059
79dnSZByqfV19dnSZ2QqMBGN127:27.2450727:27.24507
89dnSZxQJ9319dnSZSWLpjGN125:01.0335325:01.03353
99dnMY4JHSs19dnSZQ2tMDGN124:32.8513624:32.85136
상담ID상담결재역할관계코드상담결재ID업무구분코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dnM56CZxt19dnM6Riw6NGY232:07.2504631:28.15046
4919dnM4mQsfJ19dnM4wzorZGN231:16.7456355:49.64563
4929dnLcV231o19dnM4A4JdbGN231:12.1396356:56.23963
4939dnM56CZxt19dnM6OPtrdGY231:11.7504630:51.65046
4949dnM56CZxt19dnM6G8tgRGY229:23.7504628:58.05046
4959dnM56CZxt19dnM6CdRSVGY228:17.2504627:45.45046
4969dnM5rMvTW19dnM6w8CIxGY227:54.1404126:30.34041
4979dnM3mpNZW19dnM3AbBGPGN226:56.3541441:26.95414
4989dnM25ceZA19dnM3OY5HgGN226:04.6402345:05.64023
4999dnMSf7X6H19dnM3Ry6VWGN226:01.7402345:43.74023