Index

active management, 41–42

ADKAR change model, 110, 117, 153, See also change management

agile

shifting to, 76

agile methods

value of, 61

AI washing, 84

ambassador, 136

ambassadorship, 38

analysis paralysis, 63

artificial intelligence (AI), 81

backlog items, 144, 145

bad data, 21

Bergh, Christopher, 69, 145

break/fix issues, 68

broken trust, 133–35

budget, 45

Burns, Kevin, 61

business leader, 53

change management, 100, 107–11

ADKAR model of, 117

and communication, 111–13

and the ADKAR model, 153

change model

ADKAR, 110

change plan, 114

change, disruptive, 51

changes

in the process, 71

classic test theory, 132

clean data, 16, 25

communication, 23, 49, 116–18

and change management, 111–13

importance of, 62, 150–53

communication plan, 118, 153

competition, 10

Costner, Kevin, 151

culture, 101–3

organizational, 101

customer relationship management (CRM), 114

data

definition of, 81–83

error and, 120

protection of, 38, 40

radical democratization of, 27

regulatory protection of, 40

same, 10

Data Ambassador role, 52–53

data availability, 10

data catalog, 89–91

purchase requirements, 92–94

data creation, 10

data culture, 99, 101–3, 104–5

data environment

testing of, 131–32

data governance, 147–48

and data defintion, 81–83

and technology connection, 80–81

and trust, 26

disrupting, 115

history of, 17–22

importance of communication and, 150–53

issues with, 23, 59–60

people input and, 35–36

scope of, 44

the ‘why’ of, 28

data governance efforts

and leadership of, 48–51

cost of, 45

disruption of, 100

data governance function

old-fashioned vs modern, 35–36

data governance leader, 44, 45, 48–51

Data Governance Operations (DGOps), 60, See DGOps

data governance principles, 142

data governance processes, 132

data literacy programs, 24

data profiling work, 64

data quality, 86–87

and context, 125

areas of, 124–28

dashboard, 96

definitions of, 87

importance of, 121–24

machine learning (ML) and, 88–89

data quality tests, 129–30

and data governance, 132

data Sherpa, 37

data stewards, 19, 30, 36

and active management, 41

and data management, 41

Data Stewardship, 19

data warehouse, 121

tested, 131

data-driven culture

changing to, 115

data-driven definition, 104

Data-Driven Healthcare, 104

DataKitchen, 69, 128, 145, 148

DataOps, 69–72

DataOps Cookbook, 76, 128

DataOps frameworks, 59

DataOps Manifesto, 69

DataOps principles

and backlog, 145

DevOps frameworks, 59

DGOps, 148–50

tenets of, 148–50, 154

DGOps mindset

shifting to, 76

disruptive change, 51, 100, 102

electronic health record (EHR), 114

empathy map, 108–9, 108

error, possibility of, 119

Executive Demand for Data-Driven Decisions, 11

executive sponsors, 22–23, 44, 55

Field of Dreams (film), 151

fit-for-purpose changes, 124

fit-for-purpose tests, 125, 131

functional tests, 129

GDPR, 40

good data quality, 86–87

Good Data Quality (GDQ), 86–87

Healthcare Business Intelligence, (Madsen), 14

Hero Mentality, 99, 109

HIPAA, 40

How Tests, 130

Hussman, David, 57

Imhoff, Claudia, 18

InfoSec team, 40

in-service, 138

in-service activity, 136–39

integration tests, 129

job descriptions

updating of, 52–53

Johnson, Steve, 86, 132

key organizational metrics, 62

leaders, 43

line of business (LOB)

and data governance, 143

machine learning (ML), 81

Madsen, Lauren

Healthcare Business Intelligence,, 14

McKinsey, 104

metrics

key organizational, 62

metrics, 45–46

successful, 113–15

working definitions and, 68

metrics, success, 142

middle managers, 111–12

Milanesi, Lou, 119

minimally valuable product (MVP), 145

ML frameworks, 87

Modern Data Governance (MDG), 142

Nason, Rick, 116

Olson, Dan, 108, 110

Olson, Jack, 123

Organization alignment, 53–55

organizational culture, 99, 101

organizational metrics

defining, 62

people

importance of, 135–45

need for, 46

prioritization, 73–75

process, the, 57–58

visibility of, 69

quality assurance tests, 128

Quality Control (QC) team, 64–65

RACI models, 39

Raden, Neil, 84

re-framing data governance, 135

regression tests, 129

resilient processes, 77

return on investment (ROI), 20, 39

risk assessment, 47–48

risk assessment templates, 47

risk logs, 47

scope, for data governance, 141

Sinek, Simon, “Start with Why, 27, 134

SOX, 40

sponsors, 43

sponsorship

of data governance efforts, 48

staffing needs, 46

Start with Why (Sinek), 27, 134

stewardship, definition of, 37

success metrics, 30, 45–46, 142

measuring, 113–15

supervised learning, 88

technology, 153–54

and data governance, 80–81

and data quality, 95–96

as a tool, 98

purpose of, 80

test, how to, 128–30

testing process, 131

testing schedule, 131

Trifacta, 89

trust, and data governance, 26

trust, broken, 133–35

Underwood, Jen, 92

unit tests, 128

unsupervised learning, 88

visibility, importance of, 62

Weidner, Robert, 146

What Tests, 129

work

personal nature of, 105–7

workflow, the, 143

working definitions, 66

and metrics, 68