Index
‘Note: Page numbers followed by “f” indicate figures.’
ABA, See American Banker’s Association (ABA)
ABC, See Audit and balance controls (ABC)
ACA, See Affordable Care Act (ACA)
Activity-level sequence construction, 182
capability progression view, 182f
Addressing concerns, 207–209
Affordable Care Act (ACA), 6
Aggregating data, 13
American Banker’s Association (ABA), 6
AML, See Antimoney laundering (AML)
AML/BSA, See Antimoney Laundering/Bank Secrecy Act (AML/BSA)
Analytic Competitiveness, 33
Analytics, 13–14
Analytics Leadership Focus, See Analytic Competitiveness
Application development, 21
Artificial intelligence, 13–14
Audit and compliance, 211–213
Bank Secrecy Act (1970), 4–5
“Benefits case approach”, 221
Best practices, 150–155
business term definition recommendations, 152–155
See also Metadata
business glossary for bigdata analytics, 157
chief data officer integrating, 235–236
digital healthcare, 235
governing bigdata in large and small ways, 237
bigdata and analytic delivery example, 241–242
cloud-based information technology, 240–241
hospital workflow model, 238–239
personalized online sales and delivery, 239–240
personal bigdata, 236–237
preventing litigation, 234–235
BRD, See Business requirements document (BRD)
“Burning platform” projects, 35–36
Business
data governance, 32
groups, 223–224
intelligence, 12–13
platform vendors, 163
management and executives, 20
term definition, 148–151
recommendations, 152–155
See also Data governance
best practices, 150–155
business term definition recommendations, 152–155
for bigdata analytics, 157
business terms definitions and term names, 148–150
critical deliverable of data governance team, 141–144
glossary taxonomies and hierarchies creation, 155–156
Playbook framework, 138f
standards, 148
structure creation, 145–148
understanding context, 139f
user identification, 144–145
Business intelligence platform, 163
Business requirements document (BRD), 216
Cancer Biomedical Informatics Grid project (caBIG project), 6–7
CAO, See Chief Analytics Officer (CAO)
Capability
Capability Maturity Model Integration (CMMI), 16
Catalysts for rapid playbook deployment, 243
CCR, See Counterparty Risk Management (CCR)
CDD, See Customer Due Diligence (CDD)
CDEs, See Critical data elements (CDEs)
CDO, See Chief Data Officer (CDO)
Center for Medicare Services (CMS), 7
Chief Analytics Officer (CAO), 35
Chief Data Officer (CDO), 33–35
Mission Map, 38
Chief Digital Officers, 33
CIO, 40–43
CISO/CPO, See Information Security and Privacy (CISO/CPO)
Client-facing reports, 247–248
Closed-loop analytics, 241
Clouds, 14–16
CMS, See Center for Medicare Services (CMS)
Comprehensive Capital Analysis and Review (CCAR), 31
Conditions, controls and capabilities assessment
capability measurement, 58–59
controls reporting—ABC control levels, 57–58
data controls, 56–57
demand management, 52
execution of Playbook activities, 47–48
measures, 48
capabilities, 50–51
methods, 53–55
Playbook assessment process, 49–50
process, 45
reporting and visualization, 61–63
risk and exposure approach, 59–61
solid demand management, 52
Consolidation, 69
Continuous performance improvement, 210–211
Continuous performance projects (CPI), 210–211
group, 220
Controls
conscious management, 23
control-focused efforts, 33
controls-conscious management, 191
reporting, 57–58
Controls Focus, See Regulatory and Compliance
COO, See Chief Operating Officer (COO)
Coordinate data relationships
corrective controls for master data, 117f–118f
defining external data quality rules & thresholds, 104f–105f
enhancing master data standards, 123f–124f
establishing and applying hierarchy standards, 125f–126f
executing standards-based master data improvements, 124f–125f
identifying external data
identifying hierarchy integration opportunities, 135f–136f
identifying master data control points, 103f–104f
list master subjects, objects & hierarchies, 89f–90f
score external data providers, 105f–106f
Core governance
Corporate governance, 32
Corporate politics, 221
business groups, 223–224
data stewards, 226–227
IT department, 222–223
lack of senior management buy-in, 224–225
Playbook, 225
wrong people running data governance, 227–229
Cost-effective stewardship, 30
Costs, 161
Counterparty Risk Management (CCR), 31
Cover graphic model, 183
Critical data, 26–27
Crowdsourcing approach, 236
Current Conditions area, 50
Customer Due Diligence (CDD), 5
and analytics
assets, 137
capabilities measurement, 50–51
catalog, 175
chiefs of, 33–35
assessment, 56–57
critical data, 26–27
exposure, 191–194
governance specialist, 163
leadership, 33
lineage, 143
management platform, 163
master, 26
mining, 13–14
officer’s power, 33–43
reference, 26
risk, 191–194
scientists, 14–16
services, 47–48
communication, 190–191
as strategy, 246–247
Data and Reporting Improvement, 33
Data control assessment, 56–57
bigdata, NoSQL, data scientists, clouds and social media, 14–16
business intelligence, 12–13
corporate politics, 221
business groups, 223–224
data stewards, 226–227
IT department, 222–223
lack of senior management buy-in, 224–225
Playbook, 225
wrong people running data governance, 227–229
customizing and maintaining Playbook, 229
customization and maintenance process, 231–232
types of customizations, 230
update deployment, 232
update frequency, 232
data and analytics governance, 1–8
data governance team, glossary and, 141–144
business term architecture lineage, 144f
extending reach, 210
audit and compliance, 211–213
cashing check, 220–221
continuous performance improvement, 210–211
enterprise architecture, 217–218
IT projects, 215–217
labeling for success, 218–220
“metrics” projects, 213–215
industry, 1–2
information and data management, 6–8
iterations, 209–210
jobs, 244
management, 163
math, statistics, data mining, artificial intelligence, analytics, 13–14
maturity model, 16–18
operational model, 203–205
operations, 199
data governance operational model, 203–205
finance and accounting, 202–203
report creation, 200–201
service center, 201–202
operations process, 210
planning and acquiring budget, 205
Playbook
programs, 243
software, 161
vendors, 174
specialist vendors, 163
categories and sections, 165–169
leaders, 174–176
method for technology selection, 163–165
process after scoring, 173–174
process for evaluation scoring, 169–173
tool selection process, 164
tools, 162
traditional approach to, 27–30
Data Management Maturity Model (DMM), 16
Data marts (DM), 11
dashboard, 163
Data Relationship Manager (DRM), 60
Data Transformation Focus, See Data and Reporting Improvement
Data-quality, 163
Data-quality vendors, 163
Demand management, 52
Demand Management Models, 35–38
Demand pipeline approach, 185
“Desk-level procedure”, 204
Digital healthcare, 235
DM, See Data marts (DM), Data modeling (DM)
DMM, See Data Management Maturity Model (DMM)
Documentation, 200
DQ, See Data quality (DQ)
DRM, See Data Relationship Manager (DRM)
DW, See Data warehousing (DW)
Elastic computing, 241
Enterprise
application implementation, 245–246
ERP, See Enterprise resource planning (ERP)
ETL tools, See Extract, transform, and load tools (ETL tools)
Eventual consistency, 15
Executive call to action, 23–25
See also Data governance
chiefs of data, governance and analytics, 33–35
cost/benefit issue, 25
data leadership
Demand Management Models, 35–38
modeling effective communication, 38–43
scope and focus area definition, 26–27
sustainable approach, 32–33
traditional approach to data governance, 27–30
Executive experience, 40–43
Executive messaging and involvement, 23–24
Facebook, 2
Fair Trade Commission (FTC), 2
Finance and accounting, 202–203
Financial Crimes Enforcement Network (FinCEN), 5
Financial Planning and Analysis (FP&A), 31
Financial services, 4–6
chief data officer integrating bigdata for, 235–236
FinCEN, See Financial Crimes Enforcement Network (FinCEN)
Formal request for information (RFI), 164
Fortune 100 global manufacturing firm, 40–43
FP&A, See Financial Planning and Analysis (FP&A)
FTC, See Fair Trade Commission (FTC)
GDP, See Gross Domestic Product (GDP)
Global Head of Data, 33
Glossary taxonomies and hierarchies, 155–156
Google, 2
Govern programs & services
adjust periodic steward, 136
define & catalog data elements, 83f–84f
define strategic goals, 136f
engage or establish data services, 81f–82f
establish governance program operating model, 79f–80f
identify authorities, experts, owners & stewards, 78f–79f
identify capability & risk reduction measures, 80f–81f
initial scope & sponsorship, 76f–78f
Governance
Governance and Regulatory Compliance (GRC), 31
Gross Domestic Product (GDP), 4
Health information, 6
Healthcare, 6–8
Healthcare Insurance Portability and Accountability act (HIPAA), 6
Holden Security, 1–2
Industry, 1–2
classification, 162
financial services, 4–6
healthcare, 6–8
key hierarchies and master data, 4f
manufacturing, 2–4
model category, 156f
Industry affiliation, 5
Information management, 6–8
Information Security and Privacy (CISO/CPO), 31
Integration, 69
Internal audit function, 211
Internal Revenue Services (IRS), 5
International Standards Organization (ISO), 5
“Internet of Things” approach, 241
IRS, See Internal Revenue Services (IRS)
IT
IT Asset Management (ITAM), 31
“Jobs-centric” approach, 211
“Khan Academy” approach, 30
Know Your Customer (KYC), 5
Labeling for success, 218–220
Land-based vehicles, 240
Large initiative review and alignment, 39–40
Learning Management Systems (LMS), 30
Manage quality & risk
analyze data
apply leading analytic model designs, 134f–135f
coordinate control & testing standards with risk & audit, 96f–97f
create analytic testing & improvement plan, 114f–115f
design rules-based profiling controls, 98f–99f
establish data quality reporting thresholds and targets, 99f–100f
evaluate data risk & exposure, 106f–107f
execute rules-based data profiling, reporting & alerting, 100f–101f
identify corrective control candidates, 116f–117f
identify physical data with systems and interfaces, 90f–91f
identify reporting & analytics impacted by data issues, 102f–103f
identify systems of record and authority, 91f–92f
implement corrective controls, 118f–119f
simplify or rationalize data interfaces, 126f–128f
specify data quality improvements for analytics, 115f–116f
track benefits, 119f–120f
Manufacturing, 2–4
Marcus, Neiman, 1
Master data, 26
Math, 13–14
Maturity levels with defined blocks of data, 183f–184f
Maturity model, 16–18
MDM, See Master data management (MDM)
Mean sea level (MSL), 151
Modeling effective communication, 38–43
MSL, See Mean sea level (MSL)
North American Industry Classification System (NAICS), 162
NoSQL, 14–16
OCC, See Office of the Comptroller (OCC)
OFAC, See Watch List Monitoring (OFAC)
Office of the Comptroller (OCC), 5
Ontology, 155–156
Operational metrics, 204
Operational model, 204
Oracle Corporation, 60
Organization functions, 9
Organizational control, 161
Performance management, 247
Personal bigdata, 236–237
Personal healthcare information (PHI), 6
assessment process, 49–50
customization and maintenance, 229
govern programs & services, 69
Playbook-based model, 248–249
steward data & analytics, 70
Playbook deployment, 177
business and IT groups, 255
catalysts for, 243
change as source of urgency, 244
data as strategy, 246–247
ERP application implementation, 245–246
integrated sales and revenue reporting, 244–245
performance management, 247
sales force automation implementation, 246
transformation program, 245
contextual decision factors, 254f
crises as source of urgency, 247
AML/BSA and fraud, 248–249
building data warehouse, 249
client-facing reports, 247–248
implementation program failure, 248
restatement financial reporting, 249
four-step process, 185f–186f
function-specific organizations, 255
identifying highest value target areas, 250–252
mapping deployment priorities, 185
patterns, 187–189
planning process, 186
rapid deployment process, 251f
schedules, 185
sense of urgency, 243–250
smart scoping, 252–255
urgency, playbooks, and existing data governance programs, 249–250
websites with data governance, 256
PMO, See Program Management Office (PMO)
POC, See Proof-of-concept (POC)
Program failure, implementation, 248
Program Management Office (PMO), 35–36
Project and program management, 20–21
Project-level data governance, 215–217
Proof points, 199
Proof-of-concept (POC), 174
Quality-control process, 62–63
Ramping, 205–206
RDM, See Reference data management (RDM)
Reference data, 26
Reference data, 162
Reference data management (RDM), 11
Regulatory and Compliance, 33
Relational database management systems (RDBS), 14–15
Report creation, 200–201
Request for proposal (RFP), 164
Restatement financial reporting, 249
RFI, See Formal request for information (RFI)
RFP, See Request for proposal (RFP)
Risk and exposure approach, 59–61
Risk and quality management, 70
Risk management, 21
Risk-adjusted maturity model, 193
Sales and marketing, 9
Sales force automation implementation, 246
Security, 241
Senior management buy-in, lack of, 224–225
Service center, 201–202
“Seven Streams of Data Resource Management” approach, 236
Smart scoping, 252–255
SMEs, See Subject matter experts (SMEs)
Social media, 14–16
Software Engineering Institute Capabilities Maturity Model (CMM SEI), 65
Software evaluation, 164
Software selection process, 174
steps for, 164–165
Solid demand management, 52
Statistics, 13–14
Steward data & analytics, 70
analytics map generation, 108f–109f
apply data and analytic standards, 122f–123f
catalog analytic models, 87f–88f
conduct discovery data profiling, 85f–86f
create data or technical service improvement plan, 113f–114f
define & catalog business terms, 82f–83f
define & publish controls operating model, 111f–112f
enhance data and analytic standards, 121f–122f
establish analytics model controls, 107f–108f
identify & register risks to & from critical data, 95f–96f
identify analytic model portfolios, 88f–89f
identify redundant & overlapping analytic models and portfolios, 133f–134f
implement monitoring process, 112f–113f
incrementally reduce redundancy, 131f–132f
prioritize & flag critical data elements, 84f–85f
provide impact analysis, 132f–133f
set quality rules & targets, 86f–87f
update risk and audit partners, 109f–110f
Subject matter experts (SMEs), 213
Taxonomy, 155–156
Technology repository, 140–141
Telehealth 2.0, 235
Term names creation, 148–150
Tiered service, 202
Top-down approach, 66
Transformation program, 245
Transformational and Analytic Leadership-based efforts, 33
Update deployment, 232
Update frequency, 232
USA Patriot Act (2001), 4–5
Users identification of business glossary, 144–145
“Vague concepts”, 207
Vendor categories, 163
Vendor market dynamics, 161
Watch List Monitoring (OFAC), 31
Workflow, 162
Workstreams, 70
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