Index
- Aberdeen Information, 75–76
- AdventHealth Celebration, 132
- agility, 80, 105–106
- Amazon, 7, 118
- analysts, 31–41
- analytics
- for artificial intelligence and machine learning, 38–39
- credible, 154–155
- for decision making, 38
- essential elements for, 3–10
- importance of people in, xvii
- internal customers for, 63
- predictive, 21–29
- skills for, 31–41
- speed and excellence in, 35–36
- strategic versus tactical, xii–xvi
- use of employee data in, 127–135
- analytics labs, 61–70
- artificial intelligence (AI), xiii, 13
- analytics for, 38–39
- decision making driven by, 118–120, 137–150
- workflows with, 137–138, 144–146
- assumptions, making, 45–46
- automation, 146
- AXA, 131–132
- bag-of-words model, 17
- behavior, determining the “why” for, 43–51
- Berinato, Scott, 85–111
- bias, 133, 139–140
- cognitive, 139–140, 143–144, 145
- Bloom, Nicholas, 73–83
- Bluecore, 118–125
- Bowne-Anderson, Hugo, 11–19, 86
- Brandt, Tobias, 61–71
- Brinton, Willard, 87, 110
- business models, 5–6
- Cantrell, Susan, 127–135
- career development, 131–132
- cause analysis, 43–48
- Chart Wizard, 90–91
- click and viz, 90–91, 92
- Clickclickclick.click, 134
- Clinton, Hillary, 12
- cloud computing, 14–15, 73–83
- adoption across industries, 76–77
- adoption rates of, 75–76, 78
- in small, young firms, 77–81
- Cloudera, 17
- cognitive biases, 139–140, 143–144, 145
- collaboration, 88–89, 107, 155–156
- colocation, 107–108
- Colson, Eric, 104–105, 108, 137–150
- communication, 66, 146
- about data, 11–12, 86–111
- batch-and-blast, 117, 120
- via email and social media, 118–121
- how it fails, 92–93
- persuasive, 85–111
- skills for, 94–99
- storytelling in, 36–37
- targeted, 116, 118–120
- winning over customers with, 115–126
- confounding factors, 143
- consultants, 27, 29
- Correll, Michael, 96
- culture, xi–xii
- clashes, overcoming, 101–105
- data-driven, xiv, xv
- information sharing in, 146
- for succeeding with data, 6–7
- customers
- internal, 63, 66, 70
- segmentation of, 122–124
- targeted communication to, 115–126
- data, xiv
- analysts, 31–41
- cognitive biases and, 143–144
- communicating about, 11–12, 86–111
- conclusions based on, 36–37
- decision making supported by, 141–145
- employee, using responsibly, 127–135
- essential elements for, 3–10
- ethics and, 13
- forms of, 15–18
- generation, collection, and storage of, 13–15
- image, 16, 19
- monetization of, 5–6, 10
- preparation, 91–106
- presenting to nonexperts, 86–111
- quality, 4–5, 8–9, 156
- reduction, 142–143
- repurposing for the public good, 151–158
- skills, prioritizing needed, 53–59
- storytelling and, 36–37
- structured, 105–106
- tabular, 15–16, 19
- training, for machine learning, 26
- unstructured, 17–18, 19
- visualization, 87–88, 90–91
- what it’s good and not good for, 43–51
- wide versus deep analysis of, 34–35
- “why” questions and, 43–51
- data analysis, 95–96
- data-driven cultures, xiv, xv
- data-driven decision making, 138
- data-driven workflows, 141–145
- data science
- defining objectives for, 22–24
- getting value from, 86–87
- hierarchy of needs in, 13
- persuasion and, 85–111
- skills needed for, 94–99
- storytelling and, 36–37
- talent for, xiv–xv, 21, 31–41, 100–105
- data scientists, xiv–xv, 6
- alternatives to, 31–41
- communicating with executives, 85–111
- demand for, 21, 85
- as domain experts, 67–68
- expectations for, 88, 91
- forms of data used by, 15–18
- data selfie (browser extension), 14
- data wrangling, 95
- dataviz. See visualization
- Davenport, Thomas H., xi–xvii, 24
- decision making, xi–xii
- AI-driven, 118–120, 137–150
- analysts’ role in, 37–38
- analytics for, 38
- data literacy for, 12
- data-supported, 141–145
- human judgment in, 138–140
- leveraging AI and humans in, 146–148
- defense, 8, 10
- deployment, of projects, 25–26
- design skills, 97
- digital determinism, 131
- domain experts, 66, 67–68, 71
- e-commerce, 79
- empathy, 104–105
- employee data, 127–135
- employees. See talent
- empowerment, 106, 108
- ethics, 13
- employee data and, 127–135
- execution, 65–66, 70
- executives, support by, 67, 71
- experimentation, 80
- General Data Protection Regulation (GDPR), 15
- Glaeser, Edward L., 151–158
- Glassdoor, 153
- Goby, Niklas, 61–71
- Google, xiii, 7
- governance, 130–131
- Graphic Methods for Presenting Facts (Brinton), 87
- heuristics, 139–140
- hiring, xiii
- for communication skills, 100–101
- data specialists, xiii, 39–40, 88
- reducing bias in, 133
- holiday campaigns, 120–121
- hypotheses, 37
- image data, 16, 19
- implementation, of analytics, xiii–xiv
- innovation, 67
- internet of things, 14, 141
- JPMorgan Chase, 130–131
- last-mile problem, 87–89, 100–101
- leadership, 7
- communication between data scientists and, 92–93
- employee data use and, 130–131
- predictive analytics and, 23–24, 27
- for projects, 107
- liberal arts, 96
- Lifetime Values (LTV), 16
- LinkedIn, 153
- Littlewood, Chris, 53–59
- Luca, Michael, 151–158
- machine learning, 13
- analytics for, 38–39
- consultants in, 27, 29
- data for training, 16, 26
- excellence in, 33–34
- planning deployment of, 25–26
- predictive analytics and, 23
- specialists, 33–35, 38–39, 41
- Marketing, Interrupted (Sutton), 116
- meetings, 103–104
- mentoring, 104–105
- Microsoft Excel, 55, 90–91
- model building, 13
- Mohamood, Fayez, 118
- Murray, Ian, 115–126
- Murray, Shep, 115–126
- objectives, 22–24, 25–26, 28
- Olson, Randal, 100–101
- operational agility, 80
- organizational capabilities, 6–7, 10
- organizational structure, 6, 7
- outside authorities, 67, 71
- paired analysis techniques, 107–108
- partnerships, 155–156
- people analytics, 127–135
- performance
- cloud computing and, 81
- data privacy and, 133
- employee, tracking, 132–133
- impact of data on, 4
- machine learning specialists and, 33–34
- personal computers (PCs), 79
- perspective, 154
- Pierri, Nicola, 73–83
- pilot projects, 27, 29
- Pivothead, 75
- Practical Charting Techniques (Spear), 89–90
- predictive analytics, 21–29
- definition of, 22–23
- planning deployment of, 25–26
- software selection versus team skills for, 24
- Predictive Analytics World, 24
- predictive audiences, 119
- predictive model, 12, 16–17, 23, 121–122
- presentations, 89–111
- privacy, 8
- cloud services and, 15
- employee data and, 127–135
- performance and, 133
- probabilistic models, 12
- problem solving
- identifying high-impact problems for, 64–65, 70
- “what” versus “why” in, 43–51
- project management, 95, 105–106
- public good, 151–158
- qualitative approach, 46–47
- randomized controlled trials, 143
- regulations, 8, 10, 15, 129
- Redman, Thomas C., 3–10
- repurposing data, 151–158
- reuse, 108–109
- revenue per email (RPE), 119, 120, 121
- rigor, 33, 48
- risk management, 8, 10
- Rogati, Monica, 13
- Sage-Gavin, Eva, 127–135
- scale, 7–8, 34, 80
- Schlumberger’s Center for Reliability & Efficiency, 129
- sentiment analysis, 17
- Shapiro, Joel, 43–51
- Shook, Ellyn, 127–135
- Siegel, Eric, 21–29
- silos, 4–5, 7
- skills, xi–xii, xiv–xv
- of analysts, 31–41
- communication, 11–12, 88–111
- employee data and, 131–133
- for predictive analytics, 24, 28
- prioritizing needed, 53–59
- for strategic analytics, xiii
- for success with data, 6–7
- for teams, 94–99
- wide versus deep, 34–35
- small companies, 73–83
- social media analytics, 47
- software selection, 24
- Spear, Mary Eleanor, 89–90
- speed, in data analysis, 35–36, 39
- stakeholders, 106
- statisticians, 33, 34–35, 38, 41, 92
- statistics, 13
- Stitch Fix, 104, 108
- storytelling, 36–37, 97, 105. See also communication
- strategic analytics, xi–xvii
- assessment of readiness for, xvi
- definition of, xii
- leveraging, xiv–xv
- structured data, 105–106
- subject expertise, 96–97
- survival heuristics, 139–140
- Sutton, Dave, 115–126
- Tableau, 96
- tabular data, 15–16, 19
- talent, xiv–xv, 6–7, 8
- dangers of underappreciating, 39–40
- dashboards, 102
- employee data use and, 127–135
- exposing teams to, 101–105
- hiring portfolios of, 100–101
- prioritizing data skills for, 53–59
- strategic analytics and, xiii
- structuring projects around, 105–106
- supporting, 107
- See also data scientists
- teams, 89–90, 91, 94
- colocating, 107–108
- defining talents for, 94–99
- empowering, 108
- skills for, 24, 28
- technology
- as democratizing force, 73–83
- in presenting data, 90–91
- prioritizing data skills versus, 57
- for success with data, 7–8, 10
- Telstra, 130
- templates, creating, 108–109
- testing, 48, 49, 143
- text classification, 17
- time-utility analysis, 55–59
- transparency, 155
- Trump, Donald, 12
- trust, 105, 128, 133
- trust dividend, 128
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