Quantification: Enterprise Management in the big data age

Quantification: Enterprise Management in the big data age
Basic Information
Original Title: metrics: how to improve key business results
Author: (US) Martin klubeck [Translator's introduction]
Translator: Wu Haixing
Press: People's post and telecommunications Press
ISBN: 9787115299611
Mounting time:
Published on: February 1, January 2013
Start: 16
Page number: 1
Version: 1-1
Category: Computer> database storage and management

More about "Quantification: Enterprise Management in the big data age"
Introduction
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Quantification: Enterprise Management in the big data age is a successful guide for enterprises to conduct quantitative management in the big data age. Firstly, this paper briefly introduces the relationship between quantitative analysis tools and other improvement tools, then focuses on the composition of the quantitative analysis system, and comprehensively introduces quantitative analysis, a powerful tool for improving organization and performance, it aims to help readers develop and improve their quantitative analysis systems more systematically and scientifically, mine the value behind the data, and truly use the data for improving enterprise performance. Quantitative: Enterprise Management in the big data age applies to all data analysis project managers and data analysts who are committed to improving the Organization and improving performance.
Directory
Quantification: Enterprise Management in the big data age
Chapter 4 unified language: data, indicators, and information, dear! 1
1.1 story 1 of three pig
1.2 data 5
1.3 metrics 6
1.4 information 7
1.5 Quantitative Analysis 8
1.6 root problem 10
1.7 data? Quantitative analysis paradox 14
1.8 composition of the quantitative analysis system 16
1.9 Conclusion 17
1.10 small stove 17
Chapter 2 design a quantitative analysis system: How to Design 20
2.1 move toward the fundamental problem! 21
2.2 check root problems 28
2.3 construct a quantitative analysis system 29
2.4 clear information, indicators, and data requirements 32
2.5 metrics and Data Collection 34
2.6 how to collect data 35
2.6.1 priority automation 36
2.6.2 hardware and software 37
2.6.3 conduct investigation 37
2.6.4 employing 37
2.7 For example: 38
2.7.1 information 39
2.7.2 Indicator 39
2.7.3 data 40
2.8 Review 41
2.9 conclusion 41
2.10 small kitchen 42
Chapter 4 Planning quality quantitative analysis: Where to start 46
3.1 Quantitative Analysis and Development Plan 46
3.2 document: improving the development planning grade 59
3.3 review 63
3.4 Conclusion 64
Chapter 5 quantitative analysis as an indicator 65
4.1 fact is not truth 65
4.1.1 quantitative analysis may also make mistakes 68
4.1.2 accurate quantitative analysis is only an indicator 69
4.1.3 indicator: qualitative and quantitative data 71
4.2 Review 73
4.3 conclusion 74
Chapter 5 answer outline: convenient means 75
5.1 Answer outline 75
5.1.1 answer outline: Level 1 76
5.1.2 answer outline: Layer 2 76
5.1.3 answer outline: Layer 3 78
5.1.4 answer outline: Layer 4 80
5.1.5 answer outline: layer 5 and Subsequent layers 85
5.2 how to use answer outline: clear quantitative analysis type 86
5.3 small kitchen 87
5.4 Review 89
5.5 conclusion 90
Chapter 1 starting from utility 91
6.1 vision health status 92
6.2 organization Health Status 93
6.3 investment vs 94 output
6.3.1 Business Process health status 95
6.3.2 product/service health status 96
6.4 customer perspective 98
6.5 eliminate fear 100
6.6 do first 101
6.7 review 101
6.8 conclusion 102
Chapter 2 triangle crossing: essential medicine for efficiency quantification analysis 7th
7.1 history of the triangle crossing method 105
7.2 practical application of triangular crossover 106
7.2.1 indicator triangle crossover 107
7.2.2 data sources and collection methods are triangular cross-section 109
7.2.3 angle triangle crossover 111
7.3 of the population statistics are not good. 112
7.4 review 114
7.5 conclusion 115
Chapter 4 expectations: How to see the meaning of quantitative analysis results 8th
8.1 exercise caution when selecting 117 8.1.1 extension target 117
8.1.2 use metrics as the target 118
8.1.3 tactical targets and critical points 119
8.1.4 incentive scheme linked to indicators 120
8.2 Expected Value: 120
8.3 expectation discovery tour 125
8.4 Review 128
8.5 Conclusion 129
Chapter 1 Preparation and interpretation of transcript 9th
9.1 concept 131
9.2 basic work 132
9.2.1 availability 140
9.2.2 speed 142
9.2.3 precision 145
9.2.4 usage 147
9.2.5 Customer Satisfaction 149
9.3 use Expected Value: 157
9.4 review 161
9.5 conclusion 161
Chapter 2 final deliverables: Quantitative Analysis transcript 10th
10.1 delivery status 163
10.1.1 availability 163
10.1.2 speed 166
10.1.3 accuracy 167
10.2 usage 169
10.3 Customer Satisfaction 174
10.4 weight and indicator 177
10.4.1 confidentiality 178
10.4.2 178 flexibility
10.5 data volume to 179 rating
10.6 review 184
10.7 conclusion 185
Chapter 2 advanced quantitative analysis 11th
11.1 getting involved in other quadrants 187
11.1.1 as support for product/service health status 187
11.1.2 Guide Business Process Optimization 187
11.1.3 grow 187 together with the organization
11.2 benefits of quantitative analysis for minor organizations 188
11.3 repeat business processes by 189
11.4 evaluation helps encourage the use of existing processes 190
11.5 quantification analysis of other quadrants 191
11.5.1 organization Health 192
11.5.2 evaluation of organizational health status 196
11.5.3 vision health 198
11.5.4 Business Process health 201
11.6 review 205
11.7 Conclusion 206
Chapter 4 Create a Service Catalog: How to Enhance the transcript 12th
12.1 how to develop Service Catalog 209
12.1.1 service/product Health Status Service Catalog 209
12.1.2 Service Catalog of health status (efficiency) of business processes 211
12.2 small stove 213
12.3 review 214
12.4 conclusion 215
Chapter 1 construction standards and benchmarks 13th
13.1 benchmark: best suited for comparison 216
13.2 develop benchmarks responsibly 218
13.3 can be compared to 219 if there is a standard
13.3.1 219 of high-quality data
13.3.2 goal: reliable industry standard 220
13.4 Review 221
13.5 conclusion 222
Chapter 4 strength of awesome quantitative analysis: 14th
14.1 quantitative analysis: indicator or fact 224
14.2 misuse quantitative analysis: "customers hate us" 226
14.3 misuse quantitative analysis: Good, bad, and ugly 228
14.3.1 good 228
14.3.2 bad 229
14.3.3 ugly 230
14.4 art of handling unexpected situations 231
14.5 Review 233
14.6 conclusion 234
Chapter 1 do not fall into the trap of research 15th
15.1 cost of research 236
15.2 disguised research 237
15.2.1 have you been trapped in 237
15.2.2 why is it wrong? 240
15.3 review 240
15.4 conclusion 241
Chapter 2 accepting the personality of the organization 16th
16.1 problem simplification 245
16.1.1 why 245
16.1.2 what is 245
16.1.3 when 245
16.1.4 who 246
16.1.5 how to do 246
16.2 what if the boss does not agree? 246
16.2.1 not unprepared 246
16.2.2 conduct research 247
16.2.3 handling failure 247
16.2.4 successful response 248
16.2.5 is it so miserable? 248
16.3 why personalized acceptance is a healthy performance of 249
16.4 review 251
16.5 conclusion 251
Appendix tools and resources 252

Source of this book: China Interactive publishing network