DAMA-DMBOK, also known as the "Data bible" is now available as a summary for easy reading. Many subjects such as data governance, data quality, modeling, ETL etc. are covered.
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DAMA - Data Management Book of Knowledge 2 DMBOK2 [full book - second edition 2017]
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DAMA-DMBOK (2nd edition)
Source:
Title: DAMA-DMBOK (Data Management Body of Knowledge)
2nd Edition (2017)
Authors: DAMA International
Publisher: Technics Publications
ISBN: 9781634622349
Summarized by: Robin Kras, July 2022
,Contents
1. Data Management ..................................................................................................................... 4
1.1. Essential concepts .............................................................................................................. 4
1.2. Data Management Frameworks ......................................................................................... 6
2. Data Handling Ethics ................................................................................................................ 9
3. Data Governance ..................................................................................................................... 11
3.1. Essential concepts ............................................................................................................ 11
3.2. Data Governance Activities ............................................................................................. 14
3.3. Tools and Techniques ...................................................................................................... 15
4. Data Architecture .................................................................................................................... 16
4.1. Essential Concepts ........................................................................................................... 16
4.2. Activities .......................................................................................................................... 17
4.3. Tools & techniques .......................................................................................................... 18
4.4. Implementation Guidelines & Data Architecture Governance ........................................ 18
5. Data Modeling and Design ..................................................................................................... 19
5.1. What is a data model? ...................................................................................................... 19
5.2. Data model components .................................................................................................. 19
5.3. Data modeling schemes ................................................................................................... 21
5.4. Data Model Levels of Details .......................................................................................... 22
5.5. Activities .......................................................................................................................... 22
5.6. Tools and best practices ................................................................................................... 23
6. Data Storage and Operations .................................................................................................. 24
6.1. Essential Concepts ........................................................................................................... 24
6.2. Activities .......................................................................................................................... 26
7. Data Security ........................................................................................................................... 27
7.1. Essential concepts ............................................................................................................ 28
7.2. Activities .......................................................................................................................... 31
7.3. Tools & Techniques......................................................................................................... 32
7.4. Implementation guidelines .............................................................................................. 33
8. Data Integration and Interoperability ...................................................................................... 34
8.1. Essential Concepts ........................................................................................................... 34
8.2. Data Integration Activities............................................................................................... 37
8.3. Tools ................................................................................................................................ 39
9. Document and Content Management ...................................................................................... 39
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, 9.1. Essential concepts ............................................................................................................ 40
9.2. Activities .......................................................................................................................... 41
9.3. Tools ................................................................................................................................ 42
10. Reference and Master Data ................................................................................................. 42
10.1. Essential Concepts ....................................................................................................... 43
10.2. Reference and Master data activities ........................................................................... 44
11. Data Warehousing and Business Intelligence ..................................................................... 45
11.1. Essential Data Warehousing and Business Intelligence Concepts............................... 45
11.2. Data Warehouse Activities........................................................................................... 47
12. Metadata Management ........................................................................................................ 47
12.1. Essential Metadata concepts ........................................................................................ 47
12.2. Metadata Management activities ................................................................................. 48
13. Data Quality ........................................................................................................................ 48
13.1. Data Quality Business Drivers ..................................................................................... 49
13.2. Data Quality Essential Concepts .................................................................................. 49
13.3. Data Quality Activities ................................................................................................ 52
13.4. Data Quality Techniques .............................................................................................. 54
14. Big Data and Data Science .................................................................................................. 55
14.1. Essential Concepts ....................................................................................................... 56
14.2. Big Data Activities ....................................................................................................... 58
15. Data Management Maturity Assessment............................................................................. 59
15.1. Essential Concepts ....................................................................................................... 59
15.2. Activities ...................................................................................................................... 60
16. Data Management Organization and Role Expectations ..................................................... 60
16.1. Understand existing organization and cultural norms.................................................. 60
16.2. Data Management Organizational Constructs ............................................................. 61
17. Data Management and Organizational Change Management ............................................. 63
17.1. Laws of Change ............................................................................................................... 63
17.2. Not Managing a Change: Managing a Transition ........................................................ 64
17.3. Eight Errors of Change Management........................................................................... 66
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, 1. Data Management
Data Management is the development of plans that deliver, protect and enhance the
value of data and information. Data represents facts about the world, having the wrong
information, or missing information, might lead to lost opportunities in business. A single
fact can be represented in multiple ways, this brings the need for Data Architecture,
modeling, governance, stewardship, metadata and data quality management. These
practices can add value because businesses no longer have to act on an erroneous gut
feeling, but on insights from facts instead. As data holds or produces value it can be
called an asset.
1.1. Essential concepts
Data management principles
Important data management principles are:
• Data is an asset with unique properties. For example, data is not consumed
when it is used as it is with physical assets.
• The value of data can and should be expressed in economic terms. Having
insight in the costs of low-quality data and the benefits of high-quality data aid in
setting strategic goals for the organization.
• Managing data means managing the quality of data.
• It takes Metadata to manage data. The data used to manage and use data is
called Metadata. Metadata is needed to interpret the data.
• It takes planning to manage data. Planning is required from an architectural
and process perspective to reduce complexity.
• Data management is cross-functional; it requires a range of skills and
expertise. Both technical and non-technical (business) skills are needed to
manage data.
• Data management requires an enterprise perspective. Data in one part of an
organization influences data of other parts of the organization.
• Data management is lifecycle management. Different type of data have
different lifecycle characteristics that need to be managed.
• Managing data includes managing the risks associated with data. Data can
be lost, stolen, misused or wrong.
• Data management requirements must drive Information Technology
decisions. Data is deeply intertwined with IT. Managing data requires an
approach that ensures IT serves, rather than drives, an organization’s strategic
data needs.
• Effective data management requires leadership commitment. Data
management requires coordination, collaboration, and commitment.
Data management challenges
An organization will meet many challenges when following these principles. Some
challenges are:
• Data differs from other assets. Data is intangible, it can’t be touched such as
physical assets. Due to its nature, it can be easily copied or transported, but is not
easy to reproduce if it is lost or destroyed. It can be stolen without being gone. It
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