The Agile Way
Wiley (Verlag)
  • erschienen am 21. Juni 2017
  • |
  • 304 Seiten
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-1-119-42419-2 (ISBN)
For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all.
This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends--and act upon them until it's too late.
But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating?
What if there were a better way to do analytics?
Fortunately, you're in luck...
Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon.
Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you.
Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors.
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PHIL SIMON is a frequent keynote speaker and recognized technology authority. He is the award-winning author of eight management books. He consults organizations on analytics, communications, strategy, data, and technology. His contributions have been featured in the Harvard Business Review, the New York Times, and on Fox News, and many other sites. He teaches analytics, system design, and business intelligence at Arizona State University's W. P. Carey School of Business.
@philsimon #agileanalytics
  • Intro
  • Praise for Analytics: The Agile Way
  • Analytics
  • Wiley & SAS Business Series
  • Other Books by Phil Simon
  • Contents
  • Preface: The Power of Dynamic Data
  • Figures and Tables
  • Introduction: It Didn't Used to Be This Way
  • A Little History Lesson
  • Analytics and the Need for Speed
  • How Fast Is Fast Enough?
  • Automation: Still the Exception That Proves the Rule
  • Book Scope, Approach, and Style
  • Breadth over Depth
  • Methodology: Guidelines > Rules
  • Technical Sophistication
  • Vendor Agnosticism
  • Intended Audience
  • Plan of Attack
  • Next
  • Notes
  • Part ONE Background and Trends
  • Chapter 1: Signs of the Times: Why Data and Analytics Are Dominating Our World
  • The Moneyball Effect
  • Digitization and the Great Unbundling
  • Amazon Web Services and Cloud Computing
  • Not Your Father's Data Storage
  • How? Hadoop and the Growth of NoSQL
  • How Much? Kryder's Law
  • Moore's Law
  • The Smartphone Revolution
  • The Democratization of Data
  • The Primacy of Privacy
  • The Internet of Things
  • The Rise of the Data-Savvy Employee
  • The Burgeoning Importance of Data Analytics
  • A Watershed Moment
  • Common Ground
  • The Data Business Is Alive and Well and Flourishing
  • Not Just the Big Five
  • Data-Related Challenges
  • Companies Left Behind
  • The Growth of Analytics Programs
  • Next
  • Notes
  • Chapter 2: The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It
  • Types of Data
  • Structured
  • Semistructured
  • Unstructured
  • Metadata
  • Getting the Data
  • Generating Data
  • Buying Data
  • Data in Motion
  • Next
  • Notes
  • Chapter 3: The Fundamentals of Analytics: Peeling Back the Onion
  • Defining Analytics
  • Reporting ? Analytics
  • Types of Analytics
  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Streaming Data Revisited
  • A Final Word on Analytics
  • Next
  • Notes
  • Part TWO Agile Methods and Analytics
  • Chapter 4: A Better Way to Work: The Benefits and Core Values of Agile Development
  • The Case against Traditional Analytics Projects
  • Understandable but Pernicious
  • A Different Mind-Set at Netflix
  • Proving the Superiority of Agile Methods
  • The Case for Guidelines over Rules
  • Scarcity and Trade-Offs on Agile Projects
  • The Specific Tenets of Agile Analytics
  • Next
  • Notes
  • Chapter 5: Introducing Scrum: Looking at One of Today's Most Popular Agile Methods
  • A Very Brief History
  • Scrum Teams
  • Product Owner
  • Scrum Master
  • Team Member
  • User Stories
  • Epics: Too Broad
  • Too Narrow/Detailed
  • Just Right
  • The Spike: A Special User Story
  • Backlogs
  • Sprints and Meetings
  • Sprint Planning
  • Daily Stand-Up
  • Story Time
  • Demo
  • Sprint Retrospective
  • Releases
  • Estimation Techniques
  • On Lawns and Relative Estimates
  • Fibonacci Numbers
  • T-Shirt Sizes
  • When Teams Disagree
  • Other Scrum Artifacts, Tools, and Concepts
  • Velocities
  • Burn-Down Charts
  • Definition of Done and Acceptance Criteria
  • Kanban Boards
  • Next
  • Chapter 6: A Framework for Agile Analytics: A Simple Model for Gathering Insights
  • Perform Business Discovery
  • Perform Data Discovery
  • Prepare the Data
  • Model the Data*
  • The Power of a Simple Model
  • Forecasting and the Human Factor
  • Understanding Superforecasters
  • Score and Deploy
  • Evaluate and Improve
  • Next
  • Notes
  • Part THREE: Analytics in Action
  • Chapter 7: University Tutoring Center: An In-Depth Case Study on Agile Analytics
  • The UTC and Project Background
  • Project Goals and Kickoff
  • User Stories
  • Business and Data Discovery
  • Iteration One
  • Iteration Two
  • Analytics Results in a Fundamental Change
  • Moving Beyond Simple Tutor Utilization
  • Meeting International Students' Needs
  • Iteration Three
  • Iteration Four
  • Results
  • Lessons
  • Next
  • Chapter 8: People Analyticsat Google/Alphabet Not Your Father's HR Department
  • The Value of Business Experiments
  • PiLab's Adventures in Analytics
  • Communication
  • A Better Approach to Hiring
  • Eliminating GPA as a Criterion for Hiring
  • Using Analytics to Streamline the Hiring Process
  • Staffing
  • The Value of Perks
  • Innovation on the Lunch Line
  • Family Leave
  • Results and Lessons
  • Next
  • Notes
  • Chapter 9: The Anti-Google: Beneke Pharmaceuticals
  • Project Background
  • Business and Data Discovery
  • The Friction Begins
  • Astonishing Results
  • Developing Options
  • The Grand Finale
  • Results and Lessons
  • Next
  • Chapter 10: Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem
  • Paying Nurses
  • Enter the Consultant
  • User Stories
  • Agile: The Better Way
  • Results
  • Lessons
  • Next
  • Chapter 11: Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster
  • Unintended but Familiar Consequences
  • Evaluating the Problem
  • Redesigning the App
  • Agile Methods in Action
  • Results and Lessons
  • Next
  • Notes
  • Part Four Making the Most Out of Agile Analytics
  • Chapter 12: The Benefits of Agile Analytics The Upsides of Small Batches
  • Life at IAC
  • Data and Data Quality
  • Insightful, Robust, and Dynamic Models
  • A Smarter, Realistic, and Skeptical Workforce
  • Summary
  • Life at RDC
  • Project Management
  • Frustrated Employees
  • Data Quality, Internal Politics, and the Blame Game
  • Summary
  • Comparing the Two
  • Next
  • Chapter 13: No Free Lunch The Impediments to-and Limitations of-Agile Analytics
  • People Issues
  • Resistance to Analytics
  • Stakeholder Availability
  • Irritating Customers, Users, and Employees with Frequent Changes
  • Data Issues
  • Data Quality
  • Overfitting and Spurious Correlations
  • Certain Problems May Call for a More Traditional Approach to Analytics
  • The Limitations of Agile Analytics
  • Acting Prematurely
  • Even Agile Analytics Can't Do Everything
  • Agile Analytics Won't Overcome a Fundamentally Bad Idea
  • Next
  • Chapter 14: The Importance of Designing for Data: Lessons from the Upstarts
  • The Genes of Music
  • From Theory to Practice
  • The Tension between Data and Design
  • All Design Is Not Created Equal
  • Data and Design Can-Nay, Should-Coexist
  • Next
  • Notes
  • Part FIVE Conclusions and Next Steps
  • Chapter 15: What Now?: A Look Forward
  • A Tale of Two Retailers
  • Test for Echo
  • Squaring the Circle
  • The Blurry Futures of Data, Analytics, and Related Issues
  • Data Governance
  • Data Exhaust
  • It's Complicated: How Ethics, Privacy, and Trust Collide
  • Final Thoughts and Next Steps
  • Notes
  • Afterword
  • Acknowledgments
  • Selected Bibliography
  • About the Author
  • Index
  • EULA

The Power of Dynamic Data

The most valuable commodity I know of is information.

-Michael Douglas as Gordon Gekko, Wall Street

On August 7, 2015, the mood at Chipotle headquarters in Denver, Colorado, was jovial. The stock (NYSE: CMG) of the chain of "fast casual" Mexican restaurants had just reached an all-time high of $749.12. Sure, the company faced its fair share of challenges (including an alarmingly high number of lawsuits), but today was a day to celebrate.

Fast-forward six months. As so often is the case these days, things had changed very quickly.

A series of food-borne illnesses came to light at the end of 2015-and not just a few mild stomachaches caused by a batch of bad salsa. The true culprit: E. coli. As the Centers for Disease Control and Prevention (CDC) announced on December 2, 2015, "52 people from nine states have been sickened, 20 have been hospitalized, and there are no deaths."1

By April 16, 2016, Chipotle's stock was in free fall, dropping 40 percent from its high to $444. Things continued to spiral downward for the chain. The stock hit $370 on December 9 of that year. In August 2016, nearly 10,000 employees sued the company for unpaid wages. In September, a 16-year-old girl won a $7.65 million lawsuit against the company for sexual harassment. One of the victim's attorneys described the situation as "a brothel that just served food."2 Damning words to be sure.

Sensing opportunity, activist investor Bill Ackman started gobbling up Chipotle equities. His hedge fund, Pershing Square Capital Management (PSCM), purchased large quantities of options trades, "normal" stock buys, and equity swaps. Rumor had it that Ackman wasn't just looking to make a buck; he wanted seats on the Chipotle board and a significant say in the company's long-term and daily management. And PSCM wasn't the only hedge fund betting long on CMG in 2016. Plenty of others were taking notice.3

Ackman is an interesting cat and a mixed blessing to the Chipotles of the world.* Over the years, he has earned a reputation as a thorn in the side of many distraught companies and their boards of directors. Still, Chipotle executives knew that his hedge fund was keeping their portfolios healthy. No doubt that CMG would have fallen further if PSCM and other funds weren't buying so aggressively.

Why were hedge funds buying Chipotle's shares on the cheap in 2016? You don't need to be Warren Buffett to see what was happening. The heads of these funds believed in the long-term value of the stock. Chipotle would eventually recover, they reasoned, so why not make a few bucks? In a way, Ackman and his ilk are no different from Homer Simpson. The patriarch of the iconic cartoon family once summarized his remarkably facile investment philosophy in the following seven words, "Buy low. Sell high. That's my motto."

This begs the natural question: On what basis do these folks make their multibillion-dollar bets?

At a high level, sharks such as Ackman operate via a combination of instinct and analysis. With regard to the latter, hedge funds have always coveted highly quantitative employees-aka quants. As Scott Patterson writes in The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It, their complex and proprietary models factor in dozens or even hundreds of variables in attempting to predict stock prices and place large wagers.

New and unexpected data sources could be worth a fortune.


Although Facebook beat it by five years, Foursquare still arrived relatively early at the social-media party. Launched in March 2009 as a "local search-and-discovery service mobile app," it didn't take long for the company to approach unicorn status. Cofounder and CEO Dennis Crowley became a bona fide rock star. Millions of people used the app to check in to restaurants and bars. Of course, none of this would have been possible as late as 2006. By 2009, though, the smartphone revolution was in full swing. Foursquare could piggyback on the ubiquity of iPhones and Droids.

Crowley and Foursquare allowed anyone to download and use the app for free. Millions of people did. Oodles of active users, however, do not a business model make. There's a world of difference between a user and a customer.

At some point, like all enterprises, Foursquare needed to make money. By growing its user base, Foursquare hoped to expand its customer base: local businesses that could create highly targeted ads that its millions of users would see and, it was hoped, act on.

Foursquare was about to take location-based advertising into the smartphone age. No longer would a city pub owner or restaurateur need to pay someone to interrupt passersby on the street and hand out cards that advertised two-for-one drink specials. Via Foursquare, eateries could reach potential customers in a way never before possible.

At least that was the theory.

Foursquare's promise has always exceeded its financial results. For all of its users and hype, the company has never reported earning a profit.4 At different points in 2012, both Marissa Mayer's acquisition-happy Yahoo! and Facebook reportedly flirted with acquiring Foursquare. In the end, though, the parties never consummated a deal.5 Yahoo! remained a mess, and Facebook didn't really need Foursquare. Its network was enormous, and it wasn't as if the idea of a check-in had never occurred to Mark Zuckerberg. (In August 2010, the social network launched Places, a feature "not unlike" Foursquare.6 ) Determined to remain relevant, Crowley and his troops soldiered on.

Version 2.0: Two Apps Are Better Than One

On May 15, 2014, Foursquare launched a spin-off app called Swarm. The new app allowed users to broadcast their locations to their friends on social networks such as Facebook and Twitter. The main Foursquare app would still exist, but with a new focus. It would attempt to wean market share from Yelp. Writing for The Verge, Ben Popper and Ellis Hamburger explained the two apps' different purposes:

Swarm will be a social heat map, helping users find friends nearby and check in to share their location. The new Foursquare will ditch the check-in and focus solely on exploration and discovery, finally positioning itself as a true Yelp-killer in the battle to provide great local search.7

Splitting Swarm from the Foursquare app has not turned out to be a panacea. Over the past few years, many industry analysts have doubted its long-term financial solvency. Foursquare has lost its status as an it company. In Figure P.1, Google Trends shows just how far the company has fallen.

Figure P.1 Foursquare Interest over Time, March 1, 2009, to March 29, 2017

Source: Google Trends.

Something had to give.

On January 14, 2016, COO Jeff Glueck replaced Crowley as CEO. On the same day that that long-rumored change of leadership took place, Foursquare bit the bullet and announced a new investor lifeline at a fraction of its prior valuation. Yes, the company and its employees had to endure the ignominy of the dreaded down round. As Mike Isaac wrote for the New York Times:

Foursquare said it had raised $45 million in a new round of venture funding, as it tries to bolster its location data-based advertising and developer businesses. The financing pegs Foursquare's valuation at roughly half of the approximately $650 million that it was valued at in its last round in 2013, according to three people with knowledge of the deal's terms, who spoke on the condition of anonymity.8 [Emphasis mine.]

Despite Foursquare's well-documented struggles, the app still sports a reported 50 million monthly active users.9 As Bloomberg TV's Cory Johnson is fond of saying, "That ain't nothin'." Was it possible that Foursquare's next and ultimately best business model was staring its management in the face?

Version 3.0: A Data-Induced "Pivot"

On April 12, 2016, Glueck penned a fascinating post on Medium10 that qualified as bragging or at least posturing. The Foursquare CEO revealed how his company collated user check-in data and other variables to accurately predict Chipotle's first-quarter sales. (The number dropped nearly 30 percent compared to the fourth quarter of 2015.)

As anyone who has studied retail knows, foot traffic isn't a terribly innovative concept these days. Brick-and-mortar retailers have known for many decades that it can serve as a valuable proxy for sales and revenue. All else being equal, there's a direct relationship between the former and the latter. Still, Glueck's lengthy data- and graph-laden article illustrated the power of "digital" foot traffic. Figure P.2 shows one of the...

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