Index

  • Page numbers followed by f and t refer to figures and tables, respectively.
  • Action:
    • for decisions to have value, 125
    • driving, 200
    • informing, compelling vs., 139–140, 202
    • transitioning from analysis to (see Synthesis)
  • Active listening, 8
  • Advanced analytics, 194
  • Agent‐based models, 191
  • Aggregating questions about problems, 25–29, 26t, 27f, 28f, 29t
  • Agile decision‐making, 17, 163, 165, 169
  • AI (see Artificial intelligence)
  • Alice in Wonderland (Carroll), 33–34
  • Allocating time, 108–110
  • Alternatives, distribution of, 193–194
  • Amazon:
    • decision reversibility assessment of, 115
    • decision types of, 153
    • leadership principles of, 172f, 173
    • PR/FAQs of, 46–48
  • Ambiguity:
    • in communication, 133
    • in decision moment, 118–121
  • Analysis paralysis, 52, 104
  • Analysis phase, 144
  • Analytical mindset, 178
  • Anscombe's Quartet, 60–62, 61f
  • Answers:
    • developing the need for, 2
    • precision answering, 3–6
    • in Socratic method, 3–4
  • Apgar score, 110–111
  • Approximation(s), 73–87
    • accuracy, 74, 83–84
    • art of guesstimating in, 76–78
    • becoming comfortable with, 82–86
    • in context, 82–85
    • first‐order, 76–77
    • guesstimation in QI framework, 86–87
    • learning, 76–78
    • power of, 74– 76
    • in practice, 78–80
    • statistical significance vs., 85–86
    • why guesstimation works, 80–82
  • Archetype personalities, 129–131 (see also Audience)
  • Artificial intelligence (AI), 185, 186, 194–196
    • AI Risk Management Framework for Winter 2022/23, 194
  • Ask step (IWIK process), 19–21 (see also Powerful questions/questioning)
  • Assessing data, 51–64
    • examining data reliability, 62–64
    • general questions in, 52
    • identifying missing data, 56–58, 57f
    • trustworthiness of data and analyses, 58–62
    • what data was collected, 52–56, 55t
  • Audience (see also Business Chemistry types)
    • billie numbers, 129–130
    • developing understanding of, 129–132
    • segmenting, 140
    • seymour, 130
    • silver surfer, 130–131
  • Authenticated data, 193
  • Automation, 186–191
  • Averages, 54–56

 

  • Back‐of‐the‐envelope estimates, 67, 75–76, 84. (see also Approximation(s))
  • “Backward Market Research” (Andreasen), 36
  • BANT model, 111–112
  • Beckhard and Harris change model, 146–150
  • Bias(es), xxvii, xxx–xxxi, 7–8 (see also “What surprised you?”)
    • amplified by narrative voice, 128
    • anchoring, xxxiv–xxxv
    • availability, xxxiii
    • avoiding, 52
    • confirmation, xxxv–xxxvi, 44, 45, 51
    • conservatism, xxxvi
    • implicit, 7–8
    • information, xxxvi
    • nonresponse, 58
    • optimism, xxxi–xxxiii
    • overconfidence, xxxi–xxxiii
    • reason for yielding to, 106
    • selective attention, xxxiv
    • for very quick decisions, 104
    • from working intuitively, xxxi–xxxiv
    • from working with data, xxxiv–xxxvi
  • Big Data, xvii, xx
    • explosion of, 200
    • managers' use of, 169
    • real value of, 177
    • thinking beyond, 202
  • Billie Numbers, 129–130 (see also Audience)
  • Blind spots, 44, 109
  • Blueprint:
    • for building insights, 176
    • of delivery, writing the, 39–41, 40t, 41f
  • Bottom line, as your top line, 93–95, 130–131, 135
  • Brainstorm:
    • Applied Imagination (Osborn), 21
    • step in IWIK process, 21–23, 22f
  • Brands, in constructing story arc, 127
  • Broader picture:
    • around data (see Context of data)
    • for decisions, 82–85
    • declaring truth without seeing the, 16
    • open‐ended questions for capturing, 10
  • Budget planning, 161–163
  • Business Chemistry types, 114, 129
  • Business Process Automation (BPA), 187
  • Business risk, 104, 116

 

  • Call centers, 187–188
  • Campbell, Joseph, 126 (see also Personas)
  • Capture step (IWIK process), 24–25
  • Case for decisions, creating, 146–150
  • Cause questions, 6 (see also Powerful questions/questioning)
  • Certainty:
    • as a myth, 100
    • resisting the urge for, 80–81
    • risk of striving for, 83
    • trade‐off between relevance and, 41
  • Change, resistance to, 146, 199
  • Chasing decisions, 143–145, 202
  • Chronological reports, 96–97
  • Classifying type of decision, 153–154
  • Coding IWIK statements, 26t, 27 (see also IWIK (“I wish I knew”))
  • Collaborative mindset, 163
  • Columbia Business School, 23
  • Committee decision‐making, 105
  • Communication:
  • Comparable context of data, 65
  • Comparisons, relevance of, 63
  • Compelling, informing vs., 139–140
  • Compelling action, 140, 202
  • Competition, context of, 65
  • Confidence:
    • in data and individual decision‐makers, 106–107
    • for synthesizing, 97–99
  • Confidence levels, 84
  • Confirmation bias, xxxv–xxxvi, 44, 45, 51
  • Conscious competence, xxiv, xxivf, xxv (see also Learning)
  • Conscious incompetence, xxiv–xxv, xxivf (see also Learning)
  • Consensus:
    • consent vs., 163–164
    • flight to safety reflected by, 201
  • Conservatism bias, xxxvi
  • Content, facts vs., 179
  • Context of data, xvii, 200 (see also Framing the problem)
    • in constructing story arc, 126
    • decision‐making without knowing, 16
    • evaluating the, 49
    • questions for determining, 64–65
  • Context of decisions, 82–85
  • Contextual analysis skills, 170
  • Contingency plans, 42
  • Convergent questions, 5 (see also Powerful questions/questioning)
  • Creative thinking, 24
  • Crisis decision‐making, 105–107, 107f, 159
  • Cross‐functional teams, 109–110
  • Culture:
    • of collaboration, 163
    • inquisitive, 6–11
    • of organization, 182
  • Customer, in constructing story arc, 127

 

 

 

  • Facts:
    • content vs., 179
    • dimensions beyond, 131–132
    • engaging listeners by use of, 136
  • Factual questions, 5 (see also Powerful questions/questioning)
  • Failed decisions, 53
  • Fear, of imperfect decisions, 144–145
  • Feasibility studies, 43
  • Feedback, 7, 139–140, 164
  • Fermi, Enrico, 76–79, 81
  • First cause/first principle, 18
  • First‐order approximations, 76–77
  • First steps, as factor for change, 146–150
  • 5‐Dot Interviewing exercise, 173–175, 173f, 174f
  • 5Vs, 185, 186
  • 5Ws and H, 1
  • Framing decisions, 30–31
  • Framing the outcome, 150–153, 152f
  • Framing the problem, 15–31
  • Future of data‐driven decision‐making, 185–197
    • automation, 186–191
    • Digital Twin, 190–192
    • probabilistic vs. deterministic thinking, 193–194
    • Trustworthy AI frameworks, 195–196

 

  • Gallery walk, 98–99
  • Governance framework, for artificial intelligence, 194
  • GPCT (Goals, Plans, Challenges, Timeline), 112
  • Growth:
    • inquisitive team culture for, 11
    • with moon‐shot mentality, 201
    • through questioning, 2
  • “Guardians,” 114 (see also Business Chemistry Types)
  • Guardrails, 108, 111–112
  • Guesstimation (see also Approximation(s))
    • in constructing story arc, 126
    • defined, 76
    • Fermi's approach for, 76–79, 81
    • in pushing through uncertainty, 119
    • in QI framework, 86–87
    • reasons for usefulness of, 80–82

 

  • Harmonizing data, 177
  • Hero's Journey (Campbell), 126 (see also Personas)
  • Heuristics, xxx
  • Hiring top talent, 167–168
    • typical tools for, 169
    • uncovering QI skill set in, 169–175, 173f, 174f
  • Historical context of data, 65
  • Hybrid automation approach, 189–190

 

  • IBM:
    • BANT model of, 111
    • cross‐functional team at, 109–110
    • and electronic election system company, 116
    • narrative given by, 135–136
    • pressure testing by, 68–70
    • storytelling with senior stakeholders at, 138–139
    • Trustworthy AI approach of, 195–196
  • Illusion, perfect decisions as, 164–165
  • Imperfect decisions, fear of, 144–145
  • Implicit biases, 7–8
  • Individual risk, 104
  • Inference, 132, 194
  • Information:
    • combination of instinct and, 101
    • interpretation of, 131–133, 135
    • optimizing discovery of, 21–23, 22f
    • for pushing through uncertainty, 118–121
    • retention of, 136
    • underlying images, 137
  • Information bias, xxxvi
  • Informing, compelling vs., 139–140
  • Inner voice, external reality of communication vs., 128–137, 134f
  • Inquisitive team culture, 6–11 (see also Powerful questions/questioning)
    • asking stream of questions in, 9–10
    • embracing silence in, 8–9
    • open‐ended questions in, 7–8
    • responding vs. reacting in, 8–9
    • steps in building, 6–7
  • Inside‐of‐the‐box thinking, outside‐of‐the‐box thinking vs., 42–45
  • Insights:
    • actionable, 99–100 (see also Synthesis)
    • aligned with trust, 144
    • blueprint for building, 176
    • derived from data, 35
    • from Digital Twins, 191
    • from what surprised you, 200
  • Instinct, combination of information and, 101
  • Institutional knowledge, 190
  • Integration, pivoting from interrogation to, 5
  • “Integrators,” 114 (see also Business Chemistry Types)
  • Interoperability:
    • of data, 177
    • with Digital Twins, 192
  • Interpretation of information, 131–133, 135
  • Interrogation (see also Data interrogation)
    • fierce interrogator, 49–71
    • pivoting to integration from, 5
    • without decision‐making, 99
  • Interrogative mindset, xviii, 49–50
  • Intuition, xix–xxxi
    • aligning trust and, 144
    • biases arising from, xxx–xxiv
    • combining quantitative analysis and, xxvi–xxxi (see also Quantitative Intuition (QI))
    • in decision‐making, xx–xxii, xxx–xxiv
    • defined, xx
    • developing/learning (see Approximation(s))
    • “gut” as well as brain involved in, xx–xxii
    • parallel thinking in, xxi
    • in pressure testing analyses, 67–68
    • as subconscious process, xx–xxi
    • teaching skills of, xxiv
    • as unconscious competence, xxv
  • Intuitive mindset, 196
  • Inventory, IWIKTM, 26–27, 26t
  • Irrational exuberance, 137
  • IWIKTM (“I wish I knew”), 15
    • analyzing IWIK information, 27, 27f
    • ask step in, 19–21
    • brainstorm step in, 21–23, 22f
    • capture step in, 24–25
    • in constructing story arc, 126, 127
    • data discovery plan in, 29t
    • deliberate step in, 25–28, 26t, 27f, 28f
    • goal of, 17, 25–26
    • moving to decision moment from, 121–123, 122f
    • process of/steps in, 18–29, 29t
    • in pushing through uncertainty, 119–122, 122f
    • scaling of, 30
    • tool for, 17–18
    • value of, 30–31
  • IWIKTM inventory, 26–27, 26t
  • IWIKTM knowledge matrix, 27, 27f
  • IWIKTM map, 28, 28f

 

 

 

  • Lang, Andrew, 202
  • The Leader's Voice (Clarke and Crossland), 136
  • Leading IWIK discussions, 24–25 (see also IWIK (“I wish I knew”))
  • Learning:
    • collaborative environment for, 5
    • conscious competence, xxivf, xxv
    • conscious incompetence, xxiv–xxv, xxivf
    • of deep analytical skills, 168
    • dimensions and stages of, xxiv–xxv, xxivf
    • to be intuitive about numbers, 73 (see also Approximation(s))
    • learning cycle, 3
    • by questioning, 1–2
  • Listening:
    • active, 8
    • effective, 8–9
  • Lower bounds, in guesstimating, 81–82

 

  • Machine learning predictive models, 176, 194
  • McKinsey & Company, 93, 168–169
  • Making bottom line your top line, 93–95
  • Managers:
    • data interrogation role of, 49–51
    • knowledge and use of Big Data by, 169
  • Map:
    • IWIK, 28, 28f
    • stakeholder, 19
    • of steps to decision moment, 165f
    • of your QI skills in organization, 182–183, 182f
  • Marketing research, backward approach to, 36
  • Market sizing, approximating, 74–76
  • Meaning, deciphering, 131–132
  • Measurement:
    • of decision moment dimensions, 108–114
    • in pressure testing decisions, 159
    • questions about metrics used in, 63
  • Metrics, questions about, 63
  • Micro‐decisions, 155–156
  • Mindset:
    • collaborative, 163
    • of data translators, 178
    • in decision moment, 104
    • in education system, 2
    • interrogative, xviii, 49–50
    • intuitive, 196
  • Missing data, 56–58, 57f, 63
  • Models, pressure testing, 66–71, 67f

 

 

  • One‐way Door decisions, 115, 118
  • Open‐ended questions (see also Powerful questions/questioning)
    • asked by children, 1–2
    • in building inquisitive team culture, 7–8
    • for capturing broader picture, 10
  • Operational context of data, 65
  • Opportunity cost, 104
  • Optimism bias, xxxiii (see also Bias)
  • Ordering questions about problems, 25–29, 26t, 27f, 28f, 29t
  • Outcome(s):
    • actionable, 42
    • framing, 150–153, 152f
    • scenarios in discussion of, 151
  • Outliers, 63, 200
  • Outside‐of‐the‐box thinking, inside‐of‐the‐box thinking vs., 42–45
  • Overconfidence, xxxi (see also Bias)
  • Overengineering, 152–153, 152f
  • Ownership of decision process, 109

 

  • Parallel thinking, xx, xxi
  • Pareto Principle, 66, 67f, 187
  • Patterns, xxiii
    • in data, Anscombe's Quartet for identifying, 60–62, 61f
    • repeatable, 186
    • revealed by IWIK process, 25–29, 26t, 27f, 28f, 29t
  • Perfect decisions, xvii, 145, 164–165
  • Performance data, 42–44
  • Performance reviews, 2
  • Personas:
    • Campbell, Joseph, 126
    • in decision‐making, 127
    • delivery of decisions and, 129–131
  • Personality types, 129–131 (see also Audience, Personas)
  • “Pioneers,” 114 (see also Business Chemistry Types)
  • Powerful questions/questioning, 1–13
    • building inquisitive team culture for, 6–11
    • importance of, 10–11
    • precision questioning/answering in, 3–6
    • training for, 3
  • Precision questioning and precision answering, 3–5
    • hiring for skills in, 169–170
    • power of, 5–6
    • process of, 4
  • Press Release/Frequently Asked Questions (PR/FAQ), 46–48
  • Pressure testing:
    • of analyses or models, 66–71, 67f
    • of decisions, 159–163
  • PR/FAQ (Press Release/Frequently Asked Questions), 46–48
  • Pricing decisions, 189–190
  • Probabilistic thinking, deterministic thinking vs., 193–194
  • Problem(s):
  • Product, in constructing story arc, 127
  • Proust Questionnaire, 12–13
  • Putting data in context, 64–65
  • Pyramid Principle, 93–95

 

  • QI (see Quantitative Intuition)
  • QI skills (see Quantitative Intuition skills)
  • QI team (see Quantitative Intuition team)
  • Qualitative guardrails, 111–112
  • Quantitative analysis:
    • avoidance of, xvii
    • combining intuition and, xxvi–xxxi
  • Quantitative IntuitionTM (QI), xvii–xxv (see also individual topics)
    • defined, xix, xixf
    • intuition in, xix–xxiv
    • precision questioning and precision answering in, 3
    • purpose of, 201
    • as set of rapid response tools, 201
    • steps of, xxiii
    • value of, xviii–xix
  • Quantitative IntuitionTM (QI) skills, 169–175, 172f–174f (see also specific skills)
    • contextual analysis, 170
    • organizational role of, 182–183, 182f
    • overlap among, 179
    • precision questioning, 169–170
    • synthesizing, 170–172
    • teaching, xxiv
  • Quantitative Intuition (QI)TM team, 167–183
  • Question(s):

 

  • Reacting, responding vs., 8–9
  • Recommendation, delivering (see Delivering decisions)
  • Recruiting top talent, 167–168 (see also Quantitative Intuition (QI) team)
  • Reducing scope of decision, 154–156, 155f
  • Relevance:
    • of comparisons, 63
    • trade‐off between certainty and, 41
  • Resistance:
    • to change, 146, 199
    • as factor for change, 146–150
  • Responding, reacting vs., 8–9
  • Retention:
    • of employees, 187–188
    • of information, 136
  • Reversibility:
    • and decision moment, 115–118
    • of Type 1 or Type 2 decisions, 153–154
  • Right sizing decision‐makers to decision moment, 156–159, 158f
  • Risk, 202
    • with artificial intelligence, 194
    • in constructing story arc, 126
    • in decisional situational quadrant, 157–159, 158f
    • in decision moment, 103–106, 103f
    • in defining the problem, 35
    • measuring, 108, 110–113
    • of striving for certainty, 83
    • with synthesis, 94–98
    • in working backward, 45–46
  • Risk appetite, 23, 112

 

  • Salesforce automation, 189–190
  • Scientific method, 3
  • Scope of decision, reducing, 154–156, 155f
  • Scoping out decisions, 37. (see also Working backward)
  • Seeking consent not consensus, 163–164
  • Segmentation studies, 38–39, 93–94
  • Sensitivity analysis, 66, 67, 191
  • Seymour Why, 130 (see also Audience, Personas)
  • Sharing E Umbrella, 20–21
  • Silver Surfer, 130–131 (see also Audience)
  • Simpson Paradox, 55, 55t
  • Simulations, 191
  • Situational risk, 104
  • Socratic method, 3–4
  • Sorting IWIK information, 26t, 27 (see also IWIKTM (“I wish I knew”))
  • Sources of data, 62–63, 177
  • Stakeholder map, 19 (see also Audience)
  • Statistical significance, approximation vs., 85–86
  • Storytelling, 126
    • deciphering meaning from, 131–132
    • in delivering data‐driven decisions, 132–136, 134f
    • elements in effectiveness of, 136–139
    • messages changed by, 132
    • with senior stakeholders, 138–139
    • story arc, 126–128
  • Strategies for decision‐making (see Decision‐making strategies)
  • Success:
    • varying definitions of, 150
    • victory conditions concept of, 151–153
  • Summary, synthesis vs., 90–96, 91f, 98
  • Supply chain optimization, 191–192
  • Surprise:
    • asking “What surprised you?,” 199–200
    • use of term as bias killer, 7, 8
  • Synthesis, 89–100
    • in constructing story arc, 126, 128
    • in crisis decision‐making, 105–107, 107f
    • defined, 89
    • difficulty of, 95–97
    • encouraging, 97–99
    • example of, 91–93, 92t
    • hiring for skills in, 170–173
    • of IWIK process information, 25–29, 26t, 27f, 28f, 29t
    • making bottom line your top line in, 93–95
    • process of, 99–100
    • summary vs., 90–96, 91f, 98
  • Systems 1 and 2 thinking, xx–xxii, xxx, 104

 

  • Tacking, 154–156, 154f
  • Talent (see Hiring top talent)
  • Teams (see also Quantitative IntuitionTM (QI) team)
    • communication with, 156
    • cross‐functional, 109–110
    • diverse intuition areas on, 67–68
    • inquisitive culture for, 6–11
  • Teamwork, in decision‐making, 105–106
  • Thinking:
    • in agile decision‐making, 17
    • analytical, 178
    • beyond Big Data, 202
    • creative, 24 (see also IWIK (“I wish I knew”))
    • geography's influence on, 128
    • inside‐ vs. outside‐of‐the‐box, 42–45
    • parallel, xx, xxi
    • probabilistic vs. deterministic, 193–194
    • quantitative, xix
    • raising power of, 201
    • Systems 1 and 2, xx–xxii, xxx, 104
  • Time:
    • allocating, 108–110
    • in constructing story arc, 126
    • in decisional situational quadrant, 157–159, 158f
    • in decision moment, 103–106, 103f
    • measuring, 108–110
    • for working backward, 46
  • Top line, bottom line as your, 93–95, 130–131, 135
  • Trust:
    • in constructing story arc, 126
    • in decision moment, 106–107, 107f
    • insight and intuition aligned with, 144
    • measuring, 108, 114
  • Trustworthiness:
    • of artificial intelligence, 194–196
    • of data and analyses, 58–62
  • Trustworthy AI frameworks, 195–196
  • Truth, 144
  • T‐shirt sizing estimates, 83–84, 108
  • Two‐way Door decisions, 115, 116, 118
  • Type 1 and Type 2 decisions, 153–154

 

  • Uncertainty:
    • in decision moment, 118–121
    • determining level of, 67–68
    • in probabilistic approach, 194
  • Unconscious competence, xxiv, xxivf, xxv (see also Learning)
  • Unconscious incompetence, xxiv, xxivf (see also Learning)
  • Upper bounds, in guesstimating, 81–82

 

  • Victory conditions, 151–153
  • Vision, as factor for change, 146–150
  • Visualizations of data, 178–180
  • Voice Lessons (Crossland), 136, 178–180

 

  • War gaming, 159–160
  • “What surprised you?,” 199–200 (see also Bias)
  • “Why” questions, 38 (see also 5Ws and H)
  • Working backward, 33–48
    • case study for, 46–48
    • in constructing story arc, 126, 127
    • creating decision tree in, 37–39
    • criticisms of, 44
    • defining the problem by, 35–42
    • to identify risk and time trade‐off, 104
    • inside‐ vs. outside‐of‐the‐box thinking in, 42–45
    • in pushing through uncertainty, 119
    • reverse engineering data and analysis map in, 42
    • risk with, 45–46
    • writing blueprint of delivery in, 39–41, 40t, 41f
  • Working Backwards (Bryar and Carr), 47
  • World War II aircraft, 56–57, 57f
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