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E-raamat: Measuring the Data Universe: Data Integration Using Statistical Data and Metadata Exchange

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-May-2018
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319769899
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Measuring the Data Universe: Data Integration Using Statistical Data and Metadata ExchangeSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-May-2018
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319769899
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This richly illustrated book provides an easy-to-read introduction to the challenges of organizing and integrating modern data worlds, explaining the contribution of public statistics and the ISO standard SDMX (Statistical Data and Metadata Exchange). As such, it is a must for data experts as well those aspiring to become one.Today, exponentially growing data worlds are increasingly determining our professional and private lives. The rapid increase in the amount of globally available data, fueled by search engines and social networks but also by new technical possibilities such as Big Data, offers great opportunities. But whatever the undertaking - driving the block chain revolution or making smart phones even smarter - success will be determined by how well it is possible to integrate, i.e. to collect, link and evaluate, the required data. One crucial factor in this is the introduction of a cross-domain order system in combination with a standardization of the data structure.U

sing everyday examples, the authors show how the concepts of statistics provide the basis for the universal and standardized presentation of any kind of information. They also introduce the international statistics standard SDMX, describing the profound changes it has made possible and the related order system for the international statistics community.

0 About the Authors.- 0 About This Book.- Part 1: Creating Comprehensive Data Worlds using Standardisation.- 1 Where We Stand, Where We Want to Be, and How to Get There.- 2 What Does Reality Look Like .- 3 What Can We Expect From Big Data .- 4 Why is Data Integration so Hard .- 5 Basic Thoughts about Standardisation.- 6 Standardisation and Research.- 7 Introducing Standards Successfully.- 8 Statistics Driving Successful Data Integration.- 9 Contribution of the Statistics Standard SDMX.- 10 Conclusion and Outlook.- Part 2: The Statistics Standard SDMX.- 11 History of SDMX.- 12 The Main Elements of SDMX.- 13 Working With SDMX.- 14 SDMX as a key success factor for data integration.- Glossary

Arvustused

This book offers data professionals an introduction to data standardizationspecifically the Statistical Data and Metadata Exchange (SDMX) standard. This book would be of most interest to standardization organizations and their staff. Summing Up: Recommended. Professional. (H. Levkowitz, Choice, Vol. 56 (7), March, 2019)

Part I Creating Comprehensive Data Worlds Using Standardisation
1 Where We Stand, Where We Want to Be, and How to Get There
3(8)
1.1 Exploding Data Worlds
3(1)
1.2 Gated Communities: The Data Silos
4(1)
1.3 Data Linkage Is the Key
5(2)
1.4 Data Linkage Succeeds with an Order System
7(1)
1.5 The Order System Named SDMX (Statistical Data and Metadata Exchange)
8(3)
2 What Does Reality Look Like?
11(4)
2.1 Yawning Data Gaps Despite "Collectomania"
11(1)
2.2 The Data Universe Lacks Order
12(1)
2.3 Using Information Technology Not Possible Without Content-Related Expertise
13(2)
Reference
14(1)
3 What Can We Expect From Big Data?
15(8)
3.1 The Big Data Hype
16(1)
3.2 A Technical Approach to Big Data
16(2)
3.3 What Big Data Is Not Able to Offer
18(1)
3.4 Ethical Concerns
19(2)
3.5 SDMX and Big Data: Complementary, Not Contradictory
21(2)
Reference
22(1)
4 Why Is Data Integration So Hard?
23(12)
4.1 What Is Data Integration?
24(2)
4.2 Innovation Speed of IT
26(1)
4.3 Competition of IT Manufacturers, Products and Ideas
27(1)
4.4 IT Projects Instead of Business Projects
28(1)
4.5 Individualistic Mentality
29(1)
4.6 Silo Thinking Rather Than Interdisciplinary Thinking
30(1)
4.7 Privacy and Data Protection
30(1)
4.8 Lack of Direct Incentives for Data Providers
31(1)
4.9 Insufficient IT Standards for Data
32(3)
References
33(2)
5 Basic Thoughts About Standardisation
35(6)
5.1 Standards Do Not Fall from the Sky
35(1)
5.2 A Standard Is Never the Local Optimum, but Probably the Global Optimum
36(1)
5.3 Standards Are Accepted When They Are Usable
36(1)
5.4 Standards Promote Decentralised Work
37(1)
5.5 Standards for the Realisation of New Approaches---Current Example: Blockchain
38(3)
6 Standardisation and Research
41(6)
6.1 Limited Interest in Standardisation
41(1)
6.2 Influence of Data Availability on Research
42(1)
6.3 Role of Research Data Centres
42(5)
Reference
45(2)
7 Introducing Standards Successfully
47(6)
7.1 The Correct Sequence: Start With the Content
48(1)
7.2 Creating Structure and Order
48(1)
7.3 Use Classification Systems and Global Identifiers
49(1)
7.4 Use Technology Wisely
50(1)
7.5 Choose Small Steps for a Step-by-Step Approach
50(1)
7.6 Treat Stakeholders Right
51(2)
8 Statistics Driving Successful Data Integration
53(6)
8.1 The Cross-Domain Nature of Statistics
53(1)
8.2 Statistical Concepts for the Construction of a Data World
54(2)
8.3 Data Exchange and Data Sharing in Statistics
56(3)
References
57(2)
9 Contribution of the Statistics Standard SDMX
59(10)
9.1 What Is SDMX?
59(1)
9.2 Introduction to SDMX
60(1)
9.3 How to Design an SDMX Structure: A Simple Example
61(3)
9.4 Data-Driven Systems in Statistical Data Exchange Thanks to SDMX
64(1)
9.5 Professional Example of Practical Relevance
64(5)
References
67(2)
10 Conclusion and Outlook
69(4)
Reference
70(3)
Part II The Statistics Standard SDMX
11 History of SDMX
73(12)
11.1 The Idea, Its Origin and Its Propagation
73(2)
11.2 The Way to the Global Standard: The SDMX Initiative
75(4)
11.3 Further Development by the Bodies of the SDMX Initiative
79(2)
11.4 The Potential in the Use of SDMX as an Information Model
81(1)
11.5 The Future: Further Possibilities of Use and Stronger Industrialisation
82(3)
References
83(2)
12 The Main Elements of SDMX
85(18)
12.1 Elementary Building Blocks
86(1)
12.2 Defining a Data Structure Definition
87(2)
12.3 Creating a Data Set According to a Data Structure Definition
89(2)
12.4 Data Sets Are Exchanged Between Parties
91(3)
12.5 The Greater Perspective: Management of Information, Topic Areas, Stakeholders and Processes
94(2)
12.6 The SDMX-Based Data Warehouse
96(1)
12.7 Applicability of SDMX for Micro Data
97(1)
12.8 SDMX and Neighbouring Standards
98(5)
References
101(2)
13 Working with SDMX
103(4)
Reference
105(2)
14 SDMX as a Key Success Factor for Data Integration
107(4)
Reference
109(2)
About the Authors 111(2)
Glossary 113(2)
Index 115
Reinhold Stahl, mathematician, has worked at the Statistics Directorate of the German Federal Bank since 1985. He was responsible for the creation of the Federal Banks statistical information system in its current form, before becoming Director of General Statistics in 2014. He has been actively involved in the international success story of the SDMX standard presented in this book since its beginnings and has introduced this standard into the statistics of the German Federal Bank. The opportunities opened up by the standardization have made him a passionate advocate of this approach.

Dr. Patricia Staab, mathematician, started working at the Statistics Directorate of the German Federal Bank in 2000, and immediately took part in creating the internal statistical information system based on the SDMX standard. Since then, she has been appointed Head of the German Federal Banks Statistical Information Management division. Both the standard and the information system based on it have developed substantially since the beginning, but the effects the standardization could deliver at that time left a lasting impression on her.
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