Notes

2 Methods and Madness

1 . Wolfram Research Inc., Mathematica , Version 10.0 Champaign, IL (2014).

3 Data

1 . Parts of this example were extracted from the Fake Names Generator at http://names.igopaygo.com/ .

4 The Art of Science: Visualization

1 . Ray Kurzweil, How to Create a Mind (London: Viking Adult, 2012). 2 . “Lean Thinking,” Wikipedia, accessed April 2015. http://en.wikipedia.org/wiki

/Lean_Thinking . 3 . Generally speaking, I am not a fan of rigid standards that stifle creativity, but in

this case I make an exception. Enforcing ICOM gives your diagrams a consistently clean, readable quality.

4 . Knowledge Based Systems, Inc. http://www.idef.com/idef0.htm . 5 . “Sankey Diagram,” Wikipedia, accessed March 2015. http://en.wikipedia.org/wiki

/Sankey_diagram . 6 . “Treemapping,” Wikipedia, accessed April 2015. http://en.wikipedia.org/wiki

/Treemapping .

5 Tools of the Trade: The Technology of Problem Solving

1 . Formally, this is often referred to as an entity relationship diagram (ERD), but one need not adhere to each and every syntax rule of ERDs in order to generate a useful representation of the data structure one is creating to feed the model.

2 . Please refer to ProfitFromScience’s website at http://www.business-laboratory.com/profitfromscience , where I will periodically review and comment on software applications for problem solving.

232 ● Notes

3 . Google, Inc. https://code.google.com/p/kml-samples/ .

6 Using Your New Superpower

1 . If your code does not work this way or you find it very difficult to do forensics on your code, this is usually a signal that the code is not structured correctly. Consider revising your approach to the code to make it more amenable to forensics

7 Setting the Stage: The Making of a Great Problem-Solving Team

1 . Michael Lewis, Moneyball (New York: W. W. Norton & Company, 2004). 2 . The term “smart creative” was coined in Eric Schmidt, Jonathan Rosenberg,

How Google Works (New York: Grand Central Publishing, 2014). 3 . This is a heavily stylized example from a real case from our practice. 4 . Clayton M. Christensen, Richard Alton, Curtis Rising, and Andrew Waldeck,

“The Big Idea: The New M&A Playbook,” Harvard Business Review , March 2011. 5 . This is sometimes referred to as “sweeping the solution space,” wind-tunneling, or

even stress testing.

8 Implications for the Future

1 . “Driverless Car,” Wikipedia, accessed April 2015. http://en.wikipedia.org/wiki/Google_driverless_car .

2 . Sensors and housings are not included in that price. 3 . Stephen Wolfram, Stephen Wolfram, LLC, A .data Top-Level Domain? http://blog

.stephenwolfram.com/2012/01/a-data-top-level-internet-domain/ . 4 . “Richard Santulli,” Wikipedia, accessed May 2015. http://en.wikipedia.org/wiki

/Richard_Santulli . 5 . Mark Keough and Andrew Doman, “The CEO as Organization Designer,” The

McKinsey Quarterly , No. 2, Spring 1992. 6 . http://www.businessinsider.com/meet-the-companies-that-make-the-iphone

-2012–5# .

3D printing, 2107 Habits of Highly Effective People

(Covey), 183

access to data, 10accordion model of problem solving, 180acquiring data, 71–5ad hoc utility, 129–30agent-based models, 46–7Agile methodology, 142, 150, 156–7, 187,

190algorithms

as business currency, 210–11capacity and, 111data and, 70, 92, 213logic and, 70methods and, 37, 156

Amazon, 130, 220amplification of data, 76analysis, 28–30analysis paralysis, 183analytical thinking, 199–200analytics, transformation of business

models through, 219–20animation, 116–17antifragile, 216Apple, 136, 215Aristotle, 13

bad and missing data, 86–7balancing loop, 44, 46bench strength, 125–6bias, 157–9biology, 47brain, 24, 60, 77, 90, 101, 103–4, 112, 119,

162, 197, 204

business agents, 150

C#, 131, 136candidates, choosing, 181–2capability, problem-solving

accordion model, 180grass-roots model, 179–80rover model, 180

capacity, 111–12causal loop, 22, 43central processing units (CPUs), 138–9CEO, redefinition of role, 220–1change, fear of, 184cleansing of data, 87clustering, 91–2code optimization, 138collaboration, 219color, 103competition, 195–7computable document format (CDF),

139computer programming, 132, 147, 181–2computing

cloud computing, 208computing power, 11self-extending networks, 209small, highly intelligent devices, 206–7super computing for the masses, 208–9virtual reality, 207–8

Covey, Stephen, 183creativity, problem solving and, 185–6cross-collaboration, 155, 198–9crowdselling, 78–9CUDA, 139cultural resistance to problem solving,

190–2

Index

234 ● Index

dataacquiring, 71–5amplification, 76as code, 212–13cleansing, 87clustering, 91–2crowdselling, 78–9curation of, 93–5dealing with bad and missing, 86–7discovery of, 89–93distribution, 90–1explained, 70–1granularity, 80–6graphing, 90huge data sets, 85–6making data useful, 86–9normalizing, 88–9overview, 69–70privacy and security, 80–5regression, 93sorting, 89–90

data envelopment analysis (DEA), 56–9.data top-level domain (TLD), 214Data.gov, 75decision trees, 114–16de-identification, 140deployment, 139design

adding elements to mock-up, 102–7color and, 103dimensions and, 104–7fonts and, 103–5highlighting, 107role and function of, 126–9starting process of, 101–2tooltipping, 107see also visualization

distribution of data, 90–1document management, 140–1dumbing down, 35–6

entity relationship diagram (ERD), 88ethical dilemmas, 223Excel, 27execution, solution to, 167–8executive sponsors, 150experiments, running, 162–5

feedback loops, 43–6, 156FlexSim, 131fonts, 103–4

forecasting, 47–50, 217–18Forrester, Jay, 220fragmented sorting, 90

Gantt charts, 141geographic maps, 108GitHub, 141Google, 86–7, 133–5, 141, 206–7, 215granularity

data privacy and security, 80–5huge data sets, 85–6overview, 80

graphical processing units (GPUs), 138–9graphing data, 90grass-roots model of problem solving,

179–80greying workforce problem, 193–4

heuristics, 59–62hierarchy, 119horizontal sorting, 90human-machine interaction, 206hypothesis, 18–22

IBM, 204, 215ICOM standard, 109IDEF0, 109Internet, 11, 77, 130, 211, 213–14Internet of Things, 77

Java, 131, 136juxtaposition, 118, 185–6

Keyhole Markup Language (KML), 133–4knowledge systems, 214–15

language-processing technology, 94–5layering, 124, 126, 128, 155leadership, 180–1lean thinking, 109, 184LeanKit, 142Lewis, Michael, 179long-running models, 137–9

machine learning, 50–2management flight simulators (MFSs),

169–71Massachusetts Institute of Technology

(MIT), 147, 169Matlab, 131McGraw Hill Financial, 73

Index ● 235

mergers and acquistions, 194–5methods

agent-based models, 46–7data envelopment analysis, 56–9forecasting, 47–50heuristics, 59–62machine learning, 50–2Monte Carlo simulation, 52–6optimization, 42–3overview, 35–8process/flow modeling, 62–4simulation modeling, 38–41systems thinking/systems dynamics,

43–6Microsoft, 27, 126, 130model validation

forensics session, 160–1logic and, 159–60overview, 159unit testing, 161–2visualization and, 162

Moneyball (Lewis), 179Monte Carlo simulation, 37, 52–6

Nest, 204normalizing data, 88–9novel metrics, 219nVidia, 139

Occam’s razor, 12, 76, 101offloading humans, 218–19OpenCL, 139operational risk, 195optimization, 42–3Oracle, 75, 130

parallelization, 138Pareto filtering, 86, 89, 154people, problem solving and, 150–1People Express Airlines, 147Platt’s, 73prediction, 205privacy concerns, 211problem solving, approaches to

being a good client, 171bias and, 157–9cross-collaboration, 155development of qualitative model,

156–7getting started, 150–3identifying problem, 148–50

management flight simulator (MFS), 169–71

model validation, 159–62overview, 147–8repurposing models, 172running experiments, 162–5solution to execution, 167–8users, 166–7working with subject matter experts,

153–5problem solving, technology of

computation and, 131–2data and, 129–30, 139–40deployment, 139document management, 140–1elements of, 123–6long-running models, 137–9overview, 123project management, 141–2role and function of design, 126–9sandbox concept, 136–7unit testing, 132utilities, 140–2version control, 141visualization, 132–6workflow for, 126–36

process flow modeling, 62–4, 109–10process order, 128project management, 141–2, 150Promodel, 131

QlikView, 131qualitative model, 22–4quantitative model, 24–7

R&D of problem solving, 198–9Rackspace, 130Raspberry Pi, 206–7redesigned partnerships/alliances, 221–2reenactment, 74–5regression, 93reinforcing loop, 44–5, 203relative analysis, 118repurposing models, 172robustness, 11–12, 30, 113rover model of problem solving, 180

sandbox concept, 136–7Sankey diagrams, 110–11SAP, 75scenarios, 112–13

236 ● Index

scienceadvances in, 10–11analysis, 28–30problem-solving and, 13–17qualitative model, 22–4quantitative model, 24–7scientific method, 17–31strategy matrix, 30–1

security, 139–40sensible abstraction, 39Simul8, 131simulation modeling, 38–41Siri, 94, 203–4, 215solver teams, 150sorting data, 89–90SpotFire, 131sprints, 142storytelling, 117–19

hierarchy, 119juxtaposition, 118relative analysis, 118

strategy matrix, 30–1structured query language (SQL), 130subject matter experts (SMEs), 75–7, 126,

140, 150–1, 153–6, 159–63, 194–5, 227

surge, 216–17systems that anticipate, 205systems that learn, 205systems thinking, 43–6

Tableau, 131, 135tables, 114Taleb, Nassim Nicholas, 216team building

adding new members, 181–2capability and, 179–80commons, 189–98competition, 195–7cross-collaboration, 198–9culture, 183–7finding new methods, 199greying workforce problem, 193–4infusing analytical thinking,

199–200leadership and, 180–1mergers and acquisitions, 194–5operational risk, 195overview, 177–9picking problems, 187–9

problem solving and, 185–6, 200tradeoffs, 192–3

technology, business problems and, 10–11, 153

TED Talks, 154tool bending, 125, 143tradeoffs, 188, 192–3Transport for London (TfL), 75Trello, 142

Uber, 205, 220Unity, 135–6users, problem solving and, 166–7utilities, 140–2

variables, explained, 43version control, 141Verson One, 142Visio, 126visualization

adding design elements to mock-up, 102–7

animation, 116–17capacity, 111–12crafting effective visualizations, 100–1decision trees, 114–16geographic maps, 108overview, 99–100process flow, 109–10scenarios, 112–13sources and uses, 110special cases, 108–16starting design process, 101–2storytelling with, 117–19tables, 114validation and, 162

Watson, 204, 215whiteboard sessions, 151–2Wolfram, Stephen, 204, 214Wolfram Alpha, 204, 215Wolfram Language, 131workflow for problem solving

computation, 131–2database design and, 129–30role and function of design, 126–9unit testing, 132visualization, 132–6

XML, 133