Index

Access needs, 126

Advanced analytics, 122

Aiken, Peter

Monetizing Data Management, 122

Analytics progression, 119

Architects

data, 77

Architecture, 72–73

Architecture framework, 73

Asset, data as an, 10–11, 19

Attribute, 88

Barriers to Managing Information as a Business Asset, 166

Belmont Principles, 42

Big data, 108–12, 117

principles of, 110

strategy, 110–11

Big data architecture, 112

Big data capability roadmap, 111

Big data storage, 108–12

Billings, Juanita

Monetizing Data Management, 122

Business alignment, 66

Business growth, 129

Business Intelligence (BI), 97, 98, 114–15

Business Metadata, 140

Canada Bill 198, 41

Canadian privacy law, 46–47

Capability maturity assessment, 38

Cardinality, 86, 88

Change management, 178

Chief Data Officers (CDO), 68–69

Classic Control Chart, 120

CMS (content management systems), 108

Competitive advantage

elements of, 9

Content management systems (CMS), 108

Contractual and non-disclosure agreements, 126

Current state assessment, 175

Data, 10

and risks factors, 21–22

as an asset, 20, 34, 35

benefits of high quality, 23

characteristic of, 113

handling, 41

impact of low quality, 23

monetary value of, 20, 24

value of, 11, 34

Data architects, 77

Data architecture, 15, 75–77, 93

and data managment, 82–83

Data architecture artifacts, 77–83

Data asset valuation, 24

Data classification, 132

Data flow design, 79–81

Data flow diagram, 81

Data governance, 14, 49, 53–54, 54–57, 56

and data lifecycle, 56

and data-related decisions, 65–67

and leadership commitment, 69–70

and regulatory compliance, 57

Guiding principles for, 58

sustainable, 67–68

Data governance model, 59–63

Data Governance Operating Model, 63

Data governance organization components, 60

Data governance organization parts, 60

Data governance organizations, 59, 60

Data governance programs, 58–59, 67–68

Data handling

ethical, 49–51

Improvement strategies and, 51

risks and, 49

Data handling center, 42–44

Data handling ethics, 41

Data integration & interoperability (DII), 15, 95–98

Data interoperability, 95

Data lakes, 109

Data lifecycle, 25–27, 27, 130, 157

phases of, 30

principles of, 26

Data lifecycle management, 71

Data management, 151

and business requirements of, 35

and data governance, 54

and data lifecycle, 25–27

and data quality, 158–60

and data technology, 29

and enterprise perspective, 31–32

and ethics, 42–44

and lifecycle management, 36

and Metadata, 142

and organizational change, 178–81

and organizational maturity, 169

and skills requirement, 36–37

and skills requirements for, 30

footprint of, 31

goals of, 16

governance activities, 12–13

initiatives and, 66

lifecycle activities, 13

maturity model of, 34

quality and, 22–24

vs technology management, 11–12

Data management activities, 12–13

Data management framework, 17

Data management knowledge areas, 14–16

Data management maturity assessment, 65

Data Management Maturity Assessment (DMMA), 39, 174

Data management practices, 178

Data management principles, 33, 37–39

maturity of, 39

Data modeling, 83–90, 93

and domain, 89

design, 15

goals of, 84–85

Data models, 83–90, 85

building blocks of, 85–90

Data monetization, 122–24

Data privacy regulations, 22

Data producer, 90

Data protection, 125–36

Data quality, 66, 90, 151

and leadership commitments, 164–66

and organizatonal responsibility, 167

and regulations, 160

definition of, 152–53

Data quality dimension, 153–55

Data quality improvement lifecycle, 160–64

Data quality issues, 164

Data quality management, 16, 155–58, 157

Data Quality Measurement

maturity levels of, 177

Data quality program governance, 158

Data quality program team, 156

Data quality team, 162

Data regulations, 130

Data risks, 57

Data science, 108, 116–17, 121

and predictive analytics, 117

Data science models, 117

Data security, 15, 130

and enterprise data management, 130–32

and metadata, 132–33

goals of, 126–28

planning for, 134–36

Data security activities

goals of, 127

Data security architecture, 133–34

Data security principles, 128–29

Data security requirements, 127

Data security risks, 129

Data steward, 63–64

Data stewardship, 63–65, 158

activities of, 64–65

Data storage and operations, 15

Data storage and operations function, 94–95

Data technology

and data mangement, 29

Data visualization, 119–22

Data warehouses, 98–102, 109, 111

and Metadata repository, 147–48

Data warehousing, 97

and business intelligence, 15

Data, as an asset, 19

Database administration, 94

Database administrators (DBAs), 94–95

Dimensions

and data quality, 153–55

DMMA (Data Management Maturity Assessment), 39

Document management, 107

Document and content management (DCM), 15, 106–8

Domain, 89

Electronic data interchange (EDI), 134

ELT – loading, and transforming, 109

Enterprise architecture, 72–73, 82–83, 133

Enterprise data architecture

descriptions, 77

Enterprise data governance operating framework, 62

Enterprise Data Model (EDM), 78–79

Enterprise data standards, 31

Entity, 86

Entity categories, 87

Ethical data handling, 51

competitive business advantage and, 48

culture of, 49–51, 50–51

Ethical data management, 42

Ethical Risk Model, 51

Ethics, 41–42

and data management, 42–44

Federal Trade Commission (FTC), 47

Financial assets, 20

Foundational activities, 13

FTC, 47

General Data Protection Regulation (GDPR), 44–45, 160

Governance activities, 12–13

Government regulations, 126

Health Information Protection and Portability Act (U.S.), 41

High quality data, benefits of, 23

Home Energy Report, 120

Infonomics (Laney), 123

Information consumer, 90

Information technology management

and data management, 35

Inmon, Bill, 99

Inmon’s Corporate Information Factory, 100

ISO’s Metadata Registry Standard, 143

Kimball, Ralph, 99

Kimball’s Data Warehouse Chess Pieces, 101

Knowledge, 9

Knowledge areas

in data management, 14–16

Laney, Douglas

Infonomics, 123

Leader’s Data Manifesto, The, 157

Lifecycle activities, 13

Master Data

usage of, 114

Master Data Management (MDM), 57, 104–6

Metadata, 28, 35, 84, 106, 113, 142

and data security, 132–33

and interoperability, 143

benefits of, 138–40

data risks and, 142–43

definition of, 137

managed environment for, 147

quality of, 148–49

types of, 140–42

Metadata architecture, 146–48, 147

Centralized, 147

Distributed, 147

Metadata environment, 147

Metadata governance, 149, 150

Metadata leveraging, 138–39

Metadata lifecycle, 143, 146

Metadata management, 16, 123, 104–6

Metadata management system, 146

Metadata Registry Standard, 143

Metadata repository

and data warehouses, 147–48

Metadata requirements, 145–46

Metadata strategy, 144–45

Model, 83

Monetizing Data Management (Aiken & Billings), 122

Operational data store (ODS), 111

Operational Metadata, 141

Organization change

assessment of current state, 171–75

Organizational Change Management (OCM) process, 50

Personal data, 45

Personal Information Protection and Electronic Documents Act (PIPEDA), 41, 46, 160

Physical assets, 20

PIPEDA. See Personal Information Protection and Electronic Documents Act

Political governance, 59

Poor quality data

impact of, 23

Portability, 45

Predictive analytics, 117–19

Privacy regulations

and ethical principles, 44–48

Proprietary data, 126

Public policy and law, 44–48

Quality data, 139

Records, 106–8

Records management, 107

Reference and Master Data Management, 15

Reference Data Management (RDM), 103

Regulations

data quality, 160

Regulatory compliance

assessment, 66

data governance and, 57

Relational Data Model, 88

Relational Model with Attributes, 89

Relationship, 86–88

Risk model, 51

Risk reduction and data security, 129

Sarbanes-Oxley, 21, 42, 160

SDLC (systems development lifecycle), 25

Shewhart/Deming cycle, 161

Solvency II (EU), 22, 160

Stakeholders, 126

Steward, data, 63–64

Systems development lifecycle (SDLC), 25

Technical Metadata, 141, 143

Technology management, 11

Unethical data

handling, 41

Visualization, 119–22

Warehouses

data, 98

Zachman Framework, 73–75, 74

Zachman, John A., 74