A
advanced analytics, 2, 18, 19, 21, 22, 24, 104, 114, 116
AI. See artificial intelligence
algorithm, 5, 11, 12, 17–28, 30, 37–39, 45–49, 51–59, 68, 70–73, 76, 78–85, 88–90, 92, 93, 95, 96, 108, 109, 129–131
Amazon, 16
Amazon Web Services (AWS), 16
American Express, 58
artificial intelligence (AI), 1–3, 5–8, 11, 12, 15–17, 19, 21, 22, 25, 28, 37, 38, 63, 64, 75, 79, 82, 89, 91–93, 101–104, 111, 113, 114, 117, 125, 126, 128, 132
Augmentation Research Center at the Stanford Research Institute (SRI), 7
augmented intelligence, 1–9, 11–13, 15, 16, 18, 19, 21, 22, 28, 29, 47, 53, 61–66, 69–71, 73–75, 87, 88, 91, 92, 98, 101, 102, 104–106, 110, 111, 113–116, 118, 121, 125–128, 131, 132
AWS. See Amazon Web Services
B
banking, 110
bias, 8–13, 18, 35, 43, 45, 46, 48, 51, 79, 81–83, 86, 88, 96, 126, 128
big data, 16–19, 22, 25, 29, 36
black box algorithm, 51, 56, 129
Bostrum, Nicholas, 11
bot, 127
building models, 20, 31, 33, 47, 87
business application, 19, 29, 30, 56, 57
business case, 101
business process, 1–3, 8, 9, 13, 17, 20, 21, 47, 48, 51, 53, 59, 61, 62, 64–67, 69–71, 73, 76–79, 81, 85, 86, 101, 102, 106, 117, 130, 132
business process redesign, 65
C
California Consumer Privacy Act, 95, 97
call center, 108, 127
Cambridge Analytica, 95
CDO. See Chief Data Officer 137
center of excellence, 121
chess, 3–5, 127
Chief Data Officer (CDO), 98, 116
Civil Rights Act, 128
cloud, 16–18, 22, 28, 103
compliance, 88, 89, 92, 93, 97–99, 114, 117, 126
confirmation bias, 10, 13
control activities, 92
control environment, 91
control framework, 88, 89, 91–93, 97
controls, 8, 11, 12, 88, 92–94, 96, 98, 99
credit score, 132
customer expectations, 98, 103
D
dark data, 122
Dartmouth College, 5
data acquisition, 13, 29, 31, 33, 46, 66
data catalog, 115
data cleansing, 32, 36, 38, 39, 107
data culture, 116, 123
data exploration, 38, 39, 41, 42, 66
data lakes, 18
data management, 18, 28, 29, 32, 36, 106, 116
data maturity, 106
data mining, 18, 24, 108
data preparation, 29, 32, 33, 35–39, 46, 48
data quality, 41
data reliability, 114
data scientist, 20, 21, 24, 25, 30, 32, 35, 38, 46, 51, 52, 54–56, 72, 73, 76–78, 80–82, 84, 85, 87, 92, 105, 114, 129
decision tree, 46, 49–51, 59, 84, 108
deep learning, 25, 27, 28, 58, 125, 130
deep neural networks, 58, 59, 130
digital disruption, 77
digital marketing, 69, 70
discrimination, 12, 89
distributed data, 114, 131
E
EC2. See Elastic Compute Cloud
ECOA. See Equal Credit Opportunity Act
Elastic Compute Cloud (EC2), 16
Engelbart, Douglas, 6, 7
ensemble modeling, 46
Equal Credit Opportunity Act (ECOA), 89, 92
ethics, 13, 75, 82, 86, 87, 89, 90, 92, 94, 96, 98, 99, 127
Everseen Ltd., 4
explainability, 49, 56, 84, 86, 131
F
Facebook, 94, 95
feature engineering, 32, 38, 41
financial services, 102
fraud, 4, 58, 120
G
GDPR. See General Data Protection Regulation
General Data Protection Regulation (GDPR), 70, 95–98
gold standard, 52, 56, 129
governance, 8, 11–13, 70, 75, 87–90, 92, 96, 98, 106, 110, 117, 126, 127
GPU. See graphic processing unit
graphic processing unit (GPU), 16
Great Recession, 9
H
hierarchical clustering, 55, 56
human–machine collaboration, 3, 4, 6, 7, 13, 65, 70, 71, 73, 74
hybrid augmentation, 73
hybrid collaboration, 126, 132
hybrid man–machine, 4
hybrid professional, 1, 65
hybrid team, 105, 113, 122
I
IBM, 3, 39
inspectability, 49, 131
inspectable algorithm, 48, 49
insurance company, 2, 9, 89, 108
intelligent tutor, 128
Internet of Things (IoT), 28
IoT. See Internet of Things
K
Kahneman, Daniel, 10
Kasparov, Garry, 4
K-means clustering, 55, 56
k-nearest neighbors (k-NN), 53, 54
k-NN. See k-nearest neighbors
knowledge expert, 87
knowledge transfer, 30, 121
Kraljic, Peter, 68
L
legal, 13, 35, 37, 82, 88, 92, 93, 96, 97, 131
Licklider, J. C. R., 6
lift and shift, 62, 63
loss aversion, 10
M
machine–human collaboration, 65, 74
machine learning libraries, 113
machine learning (ML), 1–5, 8, 11, 12, 15–28, 30–32, 36–39, 46–49, 51, 53–55, 58, 59, 64–66, 68, 75–86, 88, 89, 92, 101–104, 106, 109, 111, 113, 114, 116, 125, 127, 128, 130–132
man–computer symbiosis, 6
masking, 80, 84
marketing, 13, 33, 34, 69, 70, 89, 91, 93–95, 130
McCarthy, John, 5
Minsky, Marvin, 6
missing values, 36, 38–42
ML. See machine learning
model development and deployment, 13, 32, 46
monitoring system, 92, 93
N
natural language processing (NLP), 2, 5, 12, 20, 25, 64, 65, 68, 71, 108, 109
neural network, 27, 28, 56–59, 130
NLP. See natural language processing
O
offline machine learning, 23
online machine learning, 24
OpenAI, 131
opt in, 70, 88, 94–96
overfit, 54
overfitting, 26, 43–46, 54, 80, 86
P
Peck, Art, 71
personal data, 70, 72, 75, 84, 94–96, 98, 99
phases of the data cycle, 31
predictive analytics, 5, 12, 18, 29, 30, 64, 68, 106, 108, 109
predictive applications, 30
predictive maintenance, 65, 69
predictive sourcing, 65, 67, 69
privacy, 70, 88, 93–95, 97, 98, 102, 114, 126
R
real estate, 9, 10, 31, 39, 46
regulation, 70, 95, 97, 126
regulatory, 92, 94, 97, 114, 127, 132
reinforcement learning (RL), 25, 27, 56, 57, 59, 130
retail, 3, 4, 69–73, 94, 95, 102, 104, 107, 122, 129
risk, 2, 9–11, 13, 35, 45, 46, 58, 68, 75–86, 88, 90, 92–94, 97–99, 102, 108, 117, 119–121, 127, 131, 132
risk assessment, 92
RL. See reinforcement learning
robotic process automation (RPA), 63, 64, 69, 71, 74
RPA. See robotic process automation
rules, 10, 22, 52, 54, 56, 63, 64, 70, 76, 80, 87, 88, 91–93, 97, 98, 108, 114, 126, 130
S
SAP Ariba, 67
security, 4, 10, 80, 84, 97, 98, 102, 106, 114, 119, 131
semi-structured data, 19
Simon, Herbert, 6
Smith, Adam, 10
SRI. See Augmentation Research Center at the Stanford Research Institute
statistics, 24, 131
Stitch Fix, 71–73
strong augmentation, 2, 61, 64, 65, 67, 69, 71, 73, 74
structured and unstructured data, 5, 61, 102, 105, 108
structured data, 18, 19, 36, 104
subject matter experts, 3, 20, 21, 35, 38, 52, 59, 76, 85, 102, 104, 113, 117, 118
supervised algorithms, 26, 52, 59
supervised learning, 11, 25–27, 31, 52, 53, 58
support vector machines (SVM), 53, 54
SVM. See support vector machines
symbiotic relationship, 7
T
Target, 94, 95
The Gap, Inc., 71
third-party data, 80, 88, 92, 93, 114, 115, 121, 122
training data, 12, 23, 26, 31, 34, 35, 43, 45, 46, 52, 54, 129
transparency, 88, 94–96, 98, 131
transportation, 110
Tversky, Amos, 10
U
Uber, 94
underfitting, 43–45, 80, 86
unlabeled data, 26, 52, 129
unstructured data, 5, 8, 18–20, 27, 32, 61, 102, 104, 105, 107, 108, 119, 121, 122, 129
unsupervised algorithms, 55, 56
unsupervised learning, 25–27, 55
unsupervised machine learning, 55
V
visibility, 126
W
weak augmentation, 2, 61–64, 70, 73, 74, 102
Y
YouTube, 90
Z
Zestimate, 39
Zillow, 39–42