ANALYTICS

THE AGILE WAY

BY PHIL SIMON

Contents

Figure P.1 5

Figure P.2 6

Preface Review and Discussion Questions 7

Table I.1 8

Introduction Review and Discussion Questions 9

Figure 1.1 10

Figure 1.2 11

Chapter 1 Review and Discussion Questions 12

Table 2.1 13

Table 2.2 14

Figure 2.1 15

Figure 2.2 16

Figure 2.3 17

Figure 2.4 18

Figure 2.5 19

Figure 2.6 20

Figure 2.7 21

Figure 2.8 22

Figure 2.9 23

Chapter 2 Review and Discussion Questions 24

Figure 3.1 25

Table 3.1 26

Table 3.2 27

Table 3.3 28

Chapter 3 Review and Discussion Questions 29

Figure 4.1 30

Chapter 4 Review and Discussion Questions 31

Figure 5.1 32

Figure 5.2 33

Figure 5.3 34

Figure 5.4 35

Figure 5.5 36

Figure 5.6 37

Figure 5.7 38

Table 5.1 39

Table 5.2 40

Table 5.3 41

Figure 5.8 42

Figure 5.9 43

Table 5.4 44

Figure 5.10 45

Figure 5.11 46

Chapter 5 Review and Discussion Questions 47

Figure 6.1 48

Table 6.1 49

Table 6.2 50

Figure 6.2 51

Table 6.3 52

Chapter 6 Review and Discussion Questions 53

Table 7.1 54

Table 7.2 55

Table 7.3 56

Figure 7.1 57

Figure 7.2 58

Figure 7.3 59

Figure 7.4 60

Figure 7.5 61

Figure 7.6 62

Figure 7.7 63

Figure 7.8 64

Chapter 7 Review and Discussion Questions 65

Figure 8.1 66

Chapter 8 Review and Discussion Questions 67

Figure 9.1 68

Figure 9.2 69

Chapter 9 Review and Discussion Questions 70

Table 10.1 71

Figure 10.1 72

Figure 10.2 73

Figure 10.3 74

Chapter 10 Review and Discussion Questions 75

Figure 11.1 76

Figure 11.2 77

Figure 11.3 78

Figure 11.4 79

Figure 11.5 80

Chapter 11 Review and Discussion Questions 81

Table 12.1 82

Chapter 12 Review and Discussion Questions 83

Chapter 13 Review and Discussion Questions 84

Figure 14.1 85

Chapter 14 Review and Discussion Questions 86

Chapter 15 Review and Discussion Questions 87

fpref xx 31 May 2017 9:31 AM fpref xxi 31 May 2017 9:31 AM

Figure P.1 Foursquare Interest over Time, March 1, 2009, to March 29, 2017Source: Google Trends.

100

75

50

25

March 1, 2009 August 1, 2011 January 1, 2014 June 1, 2016

5

10%

7.5%

E. coliOutbreak Employees

Sick5%

2.5%

Jan.

2015

Jan.

2016

July

2015

Frequent Chipotle Goers

Infrequent Chipotle Visitors

Figure P.2 Chipotle Share of Restaurant Foot Traffic (Week over Week)Source: Foursquare Medium feed.

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PREFACE review and disCussion Questions

■ Foot traffic has always mattered. What’s different about it today?

■ Why would hedge funds and even individual investors be interested indata related to “digital” foot traffic? Can you think of any other uses forthis data?

■ Why has Foursquare struggled to meet its financial goals? What is it trying to doto finally meet them? Do you think that the company will succeed?

7

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table I.1 Project Plan for Launch of Generic BI Application

Phase Description Start Date End Date

1 Evaluate proposals from software vendors, check references, and perform general due diligence.

2/1/02 5/31/02

2 Select winning bid. Negotiate terms and sign contract. 6/1/02 7/31/02

3 Extract data from legacy systems, clean up errors, and deduplicate records.

8/1/02 8/31/02

4 Implement and customize software, typically with help of expensive consultants.

9/1/02 10/31/02

5 Train users on new application. 11/1/02 2/28/03

6 Load purified data into BI application and address errors. 3/1/03 3/31/03

7 Launch application and squash bugs. 4/1/03 4/30/03

8 Engage vendor in on-site or remote application support. 5/1/03 6/30/03

Source: Phil Simon.

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INTRODUCTION rEviEw anD DiSCuSSion QuEStionS

■ What are Waterfall or phase-gate projects?

■ Do they lend themselves to successful outcomes? Why or why not?

■ What are a few examples of automated decision making? Is this currently theexception or the rule? Do you expect that to change?

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$1,000,000.00

$100,000.00

$10,000.00

$1,000.00

$100.00

$10.00

$1.00

$0.10

$0.01January

1980January

2010

$/GB

figure 1.1 Data-Storage Costs over timeSource: Data from Matthew Komorowski (see www.mkomo.com/cost-per-gigabyte) Figure from Phil Simon.

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figure 1.2 the World’s Most Valuable Companies by Market Cap as of July 29, 2016, at 10:50 a.m. etSource: Data from Google Finance. Figure from Phil Simon.

Apple 567.75

Alphabet

Microsoft

Amazon

Facebook

546.49

445.14

366.95

364.26

0 100 200

Value (billions)

300 400 500

11

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Chapter 1 review and disCussion Questions 

■ What do you think of Steve Jobs’s stance regarding the New York Times?

■ What types of things could Apple do with this type of customer information?What types of apps could it recommend?

■ What types of data does Uber capture? How can it analyze that data in waysthat traditional taxi and transportation companies cannot?

■ What types of experiments can Uber run?

■ How else would you use this data?

■ How could you change the Uber app to collect even more information?

■ Now imagine that you are Airbnb CEO Brian Chesky. What kinds ofquestions could you ask and answer of your company’s data?

■ How would you use that information?

■ Could bookstores and traditional publishers have embraced newtechnologies and data sources more quickly? What specifically could eachhave done?

■ Do you think that any of these moves ultimately would have made adifference, or are disruption and marginalization inevitable?

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Table 2.1 Sample of Structured Data from Fictional employee table*

Employee First Name Last Name Salary

7777 Mark Kelly 100,000

7778 Steve Hogarth 110,000

7779 Pete Trawavas 99,000

7780 Steven Rothery 103,455

7781 Ian Mosley 105,000

Source: Phil Simon.

*The astute reader will recognize these names and how underpaid these men are in this example.

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Table 2.2 Affiliate payments from GMC

Check Date Check Number Check Amount

1/31/13 1234 $28.12

2/28/13 1254 $30.07

3/31/13 1275 $31.04

Source: Phil Simon.

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Figure 2.1 tweet about Data ScientistsSource: @josh_wills, Twitter, May 3, 2012.

15

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Figure 2.2 Write and rant Facebook postSource: Facebook post on March 2, 2017.

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Figure 2.3 Initial results of Cover poll for Analytics: The Agile Way Cover VoteSource: Data generated via Google Forms. Figure from Phil Simon.

Which cover do you like the most?

1

2

3

4

5

7

0 1 2 3 4

17

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Figure 2.4 Data Roundtable Front page as of December 16, 2016Source: SAS/Phil Simon.

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Figure 2.5 results of Web Scraping via import.ioSource: import.io/Phil Simon.

author_name

1 ebruaryF 29, 2016

February 23, 2016

February 18, 2016

2463

2966

2210

2

3

How big of a deal is big data quality?

Scaling a vision for data quality

Who’s in charge of data quality?

Jim Harris

Dylan Jones

Phil Simon

date title page_views

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Figure 2.6 page Views by Author on Data RoundtableSource: Data generated from Data roundtable, scraped via import.io, and analyzed with Google Sheets. Figure from Phil Simon.

6,000

4,500

3,000

1,500

3,047

2,087

3,418

2,438

1,756

4,956

3,6963,311

0Phil Simon David Loshin Jim Harris Dylan Jones Joyce Norris-

MontanariMatthewMagne

DanielTeachey

CarolNewcomb

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firstName,lastName

Emilio,Koyama

Skyler,White

Hank,Schrader

Figure 2.7 Simple CSV example

Source: Phil Simon.

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{"employees":[

{ "firstName":"Emilio", "lastName":"Koyama" },

{ "firstName":"Skyler", "lastName":"White" },

{ "firstName":"Hank", "lastName":"Schrader" }

]}

Figure 2.8 Simple JSON example

Source: Phil Simon.

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<employees>

<employee>

<firstName>Emilio</firstName> <lastName>Koyama</lastName>

</employee>

<employee>

<firstName>Skyler</firstName> <lastName>White</lastName>

</employee>

<employee>

<firstName>Hank</firstName> <lastName>Schrader</lastName>

</employee>

</employees>

Figure 2.9 Simple XML example

Source: Phil Simon.

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ChAptEr 2 rEviEw AND DiSCuSSioN QuEStioNS

■ The term Big Data consists of four more specific types of data. What arethey? Provide an example of each.

■ Can you think of a way to turn an unstructured dataset into a morestructured one?

■ What tools would you use to do this?

■ Are there any limitations to what you’re capturing?

■ We know that employees shouldn’t indiscriminately share confidentialdata with competitors and the public at large. Think back to the GMCexample. What kinds of information are companies inadvertently lettingweb sleuths know?

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Figure 3.1 Google trends: Analytics versus Key performance Indicators (March 7, 2007, to March 7, 2017)Source: Data from Google Trends. Figure from Phil Simon.

25

Reporting Analysis

Provides data Provides answers

Provides what is asked for Provides what is needed

Is typically standardized Is typically customized

Does not involve a person Involves a person

Is fairly inflexible Is extremely flexible

Source: Modified from Taming the Big Data Tidal Wave by Bill Franks

Table 3.1 reporting versus Analytics

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Table 3.2 Lleyton hewitt’s 2001 performance versus top 50 and top 10 players

Hewitt’s 2001 Season Vs. Top 50 Vs. Top 10

Return rating 160.2 (8th) 178.8 (1st)

Return games won 30.7% (5th) 37% (1st)

First-serve return points won 33.4% (9th) 35% (1st)

Break points converted 43.4% (7th) 51.4% (1st)

Tie-breaks won 58.3% (16th) 100% (T-1st)

Second-serve return points won 53.7% (9th) 55.4% (3rd)

Source: Data from ATP World Tour. Table from Phil Simon.

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Table 3.3 traditional Analytics versus event-Stream processing

Traditional Analytics via Data at Rest Analytics via Event-Stream Processing

1. Receives and stores data, typically from a single source.

1. Stores queries and analysis.

2. Prepares data. 2. Receives data, typically from multiple sources.

3. Processes and analyzes the data. 3. Processes the data.

4. Gets results and actively shares as needed. 4. Immediately and automatically pushes results.

Source: Adapted from SAS.*

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CHAPTER 3 REViEw AnD DiSCuSSion QuESTionS 

■ What are the three main types of analytics?

■ Is reporting the same as analytics? What differences exist between the two?

■ What does the best analytics do?

■ What is data in motion? How does it differ from data at rest?

■ What is the point of event-stream processing (ESP)?

■ What are different steps involved in deriving traditional andESP analytics?

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figure 4.1 Agile Methods: Before and AfterSource: the Next Wave of technologies.

Transparency

54.5

43.5

32.5

21.5

1

00.5

Communications—Internal

Communications—External

Quality

Planning

Team Performance

Teamwork

Development Process

Empowerment

Issue Resolution

Change Management

Work/Life Balance

Sustainable Pace

Leadership

Satisfaction

Personal Involvement

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Chapter 4 review and disCussion Questions

■ Consider Waterfall and Agile methods. When it comes to launching andimplementing technology projects, which are generally better and why?

■ Are there any circumstances in which the Waterfall method makes sense? Ifso, what are they? Why do they make sense? If not, why not?

■ Why are guidelines generally better than rules?

■ What are the four main guidelines from the Agile Manifesto?

■ What are some of the specific tenets of Agile methods? How do they differfrom older methods?

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figure 5.1 Simple Visual of Scrum team MakeupSource: Phil Simon.

Product Owner

Team MemberScrum Master

32

Product Backlog

SprintBacklog

figure 5.2 relationship between Product and Sprint BacklogsSource: Phil Simon.

33

figure 5.3 Schedule for a One-Week SprintSource: Phil Simon.

34

Nevada Arizona

figure 5.4 Simple representation of Grass at author’s Former and Current homeSource: Phil Simon.

35

figure 5.5 two Very Different BuildingsSource: Phil Simon.

36

figure 5.6 two Very Similar BuildingsSource: Phil Simon.

37

2113

5

3 21 1

8

figure 5.7 Fibonacci Sequence Source: By —Own work, CC BY-SA 4.0.*

38

table 5.1 estimates for User Story Points

Team Member Estimate

Dinesh 8

Gilfoyle 5

Nelson 21

Source: Phil Simon.

39

table 5.2 Planning Poker, round 1

Team Member Estimate

Richard 5

Monica 8

Russ 21

Source: Phil Simon.

40

table 5.3 Planning Poker, round 2

Team Member Estimate

Richard 5

Monica 5

Russ 21

Source: Phil Simon.

41

< Smaller Bigger > Too Big

figure 5.8 team estimation Game, round 1Source: Figure from Phil Simon.

42

53 128

figure 5.9 team estimation Game, round 2Source: Figure from Phil Simon.

43

table 5.4 Sample Points for User Story and Sprint Velocity

User Story Points

1 5

2 8

3 5

4 3

Sprint Velocity 23

Source: Phil Simon.

44

300

225

150

75

0Sprint 1

Sto

ry P

oin

ts

Sprint 2 Sprint 3 Sprint 4 Sprint 5

figure 5.10 Generic Burn-Down ChartSource: Figure from Phil Simon.

45

To Do DoneIn Progress Tested

figure 5.11 Sample Kanban Board for analytics ProjectSource: Phil Simon.

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ChaPTEr 5 rEViEw and diSCUSSion QUESTionS 

■ What are the three roles on Scrum teams? What does each do?

■ What are user stories? Why are they important? What are their three parts?

■ Which is more accurate: relative or absolute estimates? Why?

■ What is a burn-down chart? What should its general direction be?

■ Should sprint velocities increase or decrease over time? Why?

■ Why are Kanban boards helpful?

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Figure 6.1 A Simple Six-Step Framework for Agile AnalyticsSource: Model adapted from Alt-Simmons’ book Agile by Design. Figure created by Phil Simon.

PerformBusinessDiscovery

Evaluateand

Improve

Scoreand

Deploy

ModelData

PrepareData

PerformData

Discovery

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table 6.1 expected Client Data

Customer_ID PurchDate PurchAmt ProductCode

1234 1/1/08 12.99 ABC

1234 1/19/08 14.99 DEF

1234 1/21/08 72.99 XYZ

Source: Phil Simon.

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table 6.2 Actual Client Data

Customer_ID Purch Date1

Purch Amt1

Product Code1

Purch Date2

Purch Amt2

Product Code2

1234 1/1/08 12.99 ABC 1/19/08 14.99 DEF

1235 1/1/12 72.99 XYZ 1/19/08 14.99 DEF

1236 1/1/08 12.99 ABC 1/19/08 72.99 XYZ

Source: Phil Simon.

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Figure 6.2 player extra ValueSource: Data from Basketball-reference.com.

Scott PadgettTim DuncanBobby Jackson–25.00%

0.00%

25.00%

50.00%

75.00%

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table 6.3 regular Forecasters versus Superforecasters

Regular Forecasters Superforecasters

Myopic and provincial. They tend to start with an inside view and rarely look outside. These folks generally can’t get away from their own predispositions and attachments.

Ignorant in a good way. They tend to start with an outside view and slowly adopt an inside view. That is, they look heavily at external data sources, news articles, and crowd indicators, especially as starting points.

Lazy. They doubt that there is interesting data lying around.

Stubborn. They believe strongly that there is interesting data lying around, even if they can’t quickly find it.

Tend to rely on informed hunches and make the data conform to those hunches.

Engage in active, open-minded thinking. They go wherever the data takes them, even if it contradicts their preexisting beliefs.

Tend to believe in fate. Tend to reject fate and understand that someone has to win. Examples here include lotteries, markets, poker tournaments, and so on.

Source: Principles from Superforecasting: The Art and Science of Prediction by Philip Tetlock and Dan Gardner. Table from Phil Simon.

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CHAPTER 6 REvIEW AnD DISCuSSIon QuESTIonS 

■ What are the six steps in the framework for Agile analytics?

■ Why is it essential to complete every step in the framework?

■ Why is business discovery so essential?

■ Do models need to be complicated to be effective? Why or why not?

■ Are experts particularly adept at making accurate predictions? Why orwhy not?

■ What are the personality characteristics that make for better forecasting?

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Table 7.1 team Composition and roles

Team Member Role

Zachary Scott Product Owner

Sasha Yotter Scrum Master

Alex Luo Team Member

Saki Iida Team Member

Nathan Wall Team Member

Source: Phil Simon.

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Table 7.2 User Stories for tutoring project

User Role User StoryT-Shirt

SizePriority or

Release Version Notes

Tutor As a tutor, I want to make sure that my workload is fair and I am not overwhelmed.

M 3 Alleviate any bottlenecks.

Student As a student, I want to make sure that the UTC can tutor in the subject I need.

L 4

Coordinator As a tutoring center coordinator, I want to make sure that the UTC is being properly staffed.

L 3

Coordinator As a tutoring center coordinator, I want to know when the UTC is busy and why.

S 1

Coordinator As a tutoring center coordinator, I want to know any tutoring inefficiencies as soon as possible.

L 4

Coordinator As a tutoring center coordinator, I want to know the makeup of the tutees.

L 2

Coordinator As a tutoring center coordinator, I want to meet the language needs of international students.

M 2

Coordinator As a tutoring center coordinator, I want to foster a relationship with the economics tutoring center.

S 3

School Administrator

As a school administrator, I want to make sure student information is being handled in compliance with FERPA.*

M 1 Anonymize the data.

Source: Analytics team.

55

* Family Educational Rights and Privacy Act (FERPA) is a United States federal law that

governs the access of educational information and records.

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Table 7.3 Fibonacci Number Conversion table

T-Shirt Size Fibonacci Number

Small 3

Medium 5

Large 8

Source: Analytics team.

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figure 7.1 heat Map for UtC Accounting tutees Source: Analytics team.

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figure 7.2 heat Map for UtC Math tuteesSource: Analytics team.

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figure 7.3 heat Map for UtC Accounting tutorsSource: Analytics team.

59

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figure 7.4 heat Map for UtC Accounting StrsSource: Analytics team.

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figure 7.5 tutee ethnicities and Courses requested Source: Analytics team.

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figure 7.6 hispanic tutees’ popular Subjects and Visit hoursSource: Analytics team.

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figure 7.7 representation of New tutor ScheduleSource: Analytics team.

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figure 7.8 Accounting Str, thursday: Old versus NewSource: Analytics team.

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ChaPTeR 7 ReView aNd diSCUSSioN QUeSTioNS 

■ What problem was the analytics team attempting to solve?

■ In what ways did the team benefit from gathering user stories at thebeginning of the project?

■ Did the team need to overcome data-quality issues? Why was this important?

■ What did the team release in its first iteration?

■ How did the team use feedback to improve its model in each iteration?

■ Was the project ultimately successful? Why or why not?

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figure 8.1 tweet from @SusanWojcickiSource: Twitter.

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Chapter 8 review and disCussion Questions 

■ Why have most companies relied on GPA when making hiring decisions?Why might that not correlate with future performance?

■ How does Google treat HR? How is it different than its contemporaries?

■ Why did Google increase employee leave? What did the numbers ultimatelysay about the move?

■ Which of PiLab’s findings did you find most interesting? Why?

■ What other issues should PiLab investigate? Why?

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8

6

4

2

0

6.2

Rutgers Cornell Columbia Harvard Yale Ivy League

1.6 1.8

1.2 1.4 1.5

Average

Figure 9.1 Average MBA Years of tenure with Beneke: rutgers versus Ivy League SchoolsSource: Phil Simon.

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Figure 9.2 percentage of MBAs promoted within three Years at Beneke: rutgers versus Ivy League SchoolsSource: Phil Simon.

0.00%

25.00%

50.00%

75.00%

100.00%

78.26%

25.00%

0.00% 0.00%

16.67%13.33%

Rutgers Cornell Columbia Harvard Yale Ivy LeagueAverage

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Chapter 9 review and disCussion Questions 

■ Describe the general approach to analytics in HR at Beneke Pharmaceuticals.Did its employees embrace analytics?

■ When it comes to making recruiting decisions, how does Beneke differ fromGoogle?

■ Which company’s approach is better? Why?

■ How do analytics threaten established professionals?

■ What are you going to do when—not if—someone doesn’t want to listen tothe data and analytics that you’ve prepared?

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Table 10.1 Generic Nurse payroll Data

Employee_ID Date Pay_Code Time_In Time_Out Hours Rate

1234 1/1/08 REG 04:00 08:30 4.00 $20.00

1234 1/1/08 REG 09:30 13:30 6:00 $20.00

1234 1/1/08 SHIFT1 04:00 08:30 4.50 $2.00

1234 1/1/08 ON-CALL 12:00 04:00 4.00 $5.00

1234 1/1/08 OVT 11:30 13:30 2.00 $30.00

Source: Phil Simon.

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Figure 10.1 Front end for ISZM Microsoft Access Application, Version 1.0Source: Phil Simon.

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Figure 10.2 Front end for ISZM Microsoft Access Application, Version 1.1Source: Phil Simon.

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Figure 10.3 Front end for ISZM Microsoft Access Application, Version 1.2Source: Phil Simon.

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CHaPTER 10 REvIEw anD DIsCussIOn QuEsTIOns 

■ Why was perfoming business discovery so critical at ISZM?

■ What was the state of the data at ISZM? How did it get this way?

■ Why was collecting its data so problematic? What was the biggest issue?

■ What were the benefits of involving Terri and her team throughout theprocess?

■ Did the presence of a separate team help or hinder ISZM? Why or why not?

■ Why did the Badger consultants fail? What should they have donedifferently?

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‡ See http://fus.in/1N4GrOF.

Figure 11.1 Screenshot from racially Charged UserSource: Fusion.‡

casing the house

we just had a young AA woman with long dreds very slender and light (should havebeen in school) knock aggressively on our door and ask for “keith”. first time while wewere home.she disappeared immediately after. checked all cross streets.

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Figure 11.2 Screenshot from Original Nextdoor AppSource: Nextdoor.

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Figure 11.3 Screenshot from Nextdoor’s redesigned AppSource: Nextdoor.

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Figure 11.4 Screenshot from Nextdoor’s redesigned AppSource: Nextdoor.

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Figure 11.5 Screenshot from Nextdoor’s redesigned AppSource: Nextdoor.

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Chapter 11 review and disCussion Questions

■ How did Nextdoor’s first version of its app enable racial profiling?

■ Was the fix simple? Why or why not?

■ What do Nextdoor’s changes mean in terms of the data that it now collects?

■ Do you think that Nextdoor is finished tinkering with its app? Why or whynot?

■ What has the company learned from the experience? Will these lessonsinform its future design decisions? Why or why not?

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Table 12.1 Comparison between two Companies

Advantage

Area IAC RDC

Existing familiarity with Scrum ✓

Length of learning curve ✓

Respond to crises ✓

Identify to nascent trends ✓

Data quality ✓

Time needed for data-preparation effort ✓

Time needed to identify and integrate new data sources ✓

Realistic expectations re: analytics ✓

Time needed to discern between real and vanity metrics ✓

Invite ideas for improvement ✓

Source: Phil Simon.

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ChApteR 12 RevIew AnD DIsCussIon QuestIons 

■ Judging from IAC, what do you think are the most important benefits of Agilemethods for analytics endeavors?

■ How has IAC created a virtuous cycle?

■ Now look at RDC. Why is it destined for failure?

■ Which of the two companies is better equipped to capitalize on theopportunities that Agile analytics can yield? Why?

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Chapter 13 review and disCussion Questions 

■ What types of people issues can thwart organizations’ analytics efforts?

■ What types of data issues can do the same?

■ What are some of the limitations of Agile analytics?

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figure 14.1 example of a pandora recommendation: “the King of Sunset town” by MarillionSource: Pandora app.

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Chapter 14 review and disCussion Questions 

■ Go back to 1999 when Pandora was just an ambitious idea. Did its foundersinsist on mapping each and every song before launching its products? Whatabout each gene of each song? Why or why not?

■ Why does Pandora ask new users to enter their birth year, zip code, andgender? What can the company do with this information? What otherinformation would help it better customize its music recommendations?

■ Is Pandora finished mapping each song’s genes? That is, does the companyhave the capability to add additional “genes” in the future? Why or why not?

■ Do you think that Pandora’s recommendation algorithm has changed overtime? How so?

■ How is technology changing design and data?

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Chapter 15 review and disCussion Questions 

■ What happened at Target? Why did the public react so strongly?

■ Is every type of data or analytics fair game? Where do you draw the line?

■ Why has there yet been no similar backlash with the Amazon Echo?

■ Will technology, data, and privacy continue to collide? Why?

■ With respect to building trust, what can organizations learn from Amazon?

■ What is data governance? Why is it fundamentally harder to pull off todaycompared to even 15 years ago?

■ What is data exhaust? Can you think of other ways in which it might bevaluable?

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