Monetizing Your Data

A Guide to Turning Data into Profit-Driving Strategies and Solutions
 
 
Standards Information Network (Verlag)
  • erschienen am 23. Februar 2017
  • |
  • 368 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-35626-4 (ISBN)
 
Transforming data into revenue generating strategies and actions
Organizations are swamped with data--collected from web traffic, point of sale systems, enterprise resource planning systems, and more, but what to do with it? Monetizing your Data provides a framework and path for business managers to convert ever-increasing volumes of data into revenue generating actions through three disciplines: decision architecture, data science, and guided analytics. There are large gaps between understanding a business problem and knowing which data is relevant to the problem and how to leverage that data to drive significant financial performance. Using a proven methodology developed in the field through delivering meaningful solutions to Fortune 500 companies, this book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. Beginning with an explanation of the analytical cycle, this book guides you through the process of developing value generating strategies that can translate into big returns. The companion website, www.monetizingyourdata.com, provides templates, checklists, and examples to help you apply the methodology in your environment, and the expert author team provides authoritative guidance every step of the way.
This book shows you how to use your data to:
* Monetize your data to drive revenue and cut costs
* Connect your data to decisions that drive action and deliver value
* Develop analytic tools to guide managers up and down the ladder to better decisions
Turning data into action is key; data can be a valuable competitive advantage, but only if you understand how to organize it, structure it, and uncover the actionable information hidden within it through decision architecture and guided analytics. From multinational corporations to single-owner small businesses, companies of every size and structure stand to benefit from these tools, methods, and techniques; Monetizing your Data walks you through the translation and transformation to help you leverage your data into value creating strategies.
weitere Ausgaben werden ermittelt
ANDREW ROMAN WELLS is the CEO of Aspirent, a management consulting firm focused on analytics. He has extensive experience building analytical solutions for a wide range of companies, from Fortune 500s to small non-profits. Mr. Wells focuses on helping organizations utilize their data to make impactful decisions that drive revenue through monetization strategies. He has been building analytical solutions for over 25 years and is excited to share these practical methods, tools, and techniques with a wider audience. Mr. Wells earned a Bachelor's degree in Business Administration with a focus on Finance and Management Information Systems from the University of Georgia.
KATHY WILLIAMS CHIANG is an established business analytics practitioner with expertise in guided analytics, analytic data mart development, and business planning. Prior to her current position as vice president of business insights at Wunderman Data Management, Ms. Chiang consulted with Aspirent on numerous analytic projects for several multinational clients including IHG and Coca Cola, among others. She has also worked for multinational corporations including Telecommunications Systems of Trinidad and Tobago, Acuity Brands Lighting, BellSouth International, and Portman Overseas. Ms. Chiang is experienced in designing and developing analytic tools and management dashboards that inform, matter, and drive action. She is highly skilled in data exploration, analysis, visualization, and presentation, and has developed solutions in the telecom, hospitality, and consumer products industries covering customer experience, marketing campaigns, revenue management, and web analytics. Ms. Chiang, a native of New Orleans, holds a Bachelor of Science degree in Chemistry, summa cum laude with University honors (4.0), from Louisiana State University, as well as an MBA from Tulane University. She is a member of Phi Beta Kappa and Mensa.
1 - Cover???????????????????????? [Seite 1]
2 - Title Page?????????????????????????????????? [Seite 5]
3 - Copyright???????????????????????????????? [Seite 6]
4 - Contents?????????????????????????????? [Seite 9]
5 - Preface???????????????????????????? [Seite 15]
6 - Acknowledgments???????????????????????????????????????????? [Seite 19]
7 - About the Authors???????????????????????????????????????????????? [Seite 21]
8 - Section I: Introduction [Seite 25]
8.1 - Chapter 1: Introduction???????????????????????????????????????????????????????????? [Seite 27]
8.1.1 - Decisions???????????????????????????????? [Seite 28]
8.1.2 - Analytical Journey?????????????????????????????????????????????????? [Seite 31]
8.1.3 - Solving the Problem???????????????????????????????????????????????????? [Seite 32]
8.1.4 - The Survey Says.?????????????????????????????????????????????? [Seite 33]
8.1.5 - How to Use This Book?????????????????????????????????????????????????????? [Seite 36]
8.1.6 - Let's Start???????????????????????????????????? [Seite 39]
8.2 - Chapter 2: Analytical Cycle: Driving Quality Decisions?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 40]
8.2.1 - Analytical Cycle Overview???????????????????????????????????????????????????????????????? [Seite 41]
8.2.2 - Hierarchy of Information User???????????????????????????????????????????????????????????????????????? [Seite 52]
8.2.3 - Next Steps?????????????????????????????????? [Seite 54]
8.3 - Chapter 3: Decision Architecture Methodology: Closing the Gap???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 55]
8.3.1 - Methodology Overview?????????????????????????????????????????????????????? [Seite 56]
8.3.2 - Discovery???????????????????????????????? [Seite 60]
8.3.3 - Decision Analysis???????????????????????????????????????????????? [Seite 62]
8.3.4 - Monetization Strategy???????????????????????????????????????????????????????? [Seite 64]
8.3.5 - Agile Analytics???????????????????????????????????????????? [Seite 65]
8.3.6 - Enablement?????????????????????????????????? [Seite 70]
8.3.7 - Summary???????????????????????????? [Seite 73]
9 - Section II: Decision Analysis [Seite 75]
9.1 - Chapter 4: Decision Analysis: Architecting Decisions?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 77]
9.1.1 - Category Tree???????????????????????????????????????? [Seite 78]
9.1.2 - Question Analysis???????????????????????????????????????????????? [Seite 81]
9.1.3 - Key Decisions???????????????????????????????????????? [Seite 85]
9.1.4 - Data Needs?????????????????????????????????? [Seite 88]
9.1.5 - Action Levers???????????????????????????????????????? [Seite 91]
9.1.6 - Success Metrics???????????????????????????????????????????? [Seite 92]
9.1.7 - Category Tree Revisited???????????????????????????????????????????????????????????? [Seite 95]
9.1.8 - Summary???????????????????????????? [Seite 98]
10 - Section III: Monetization Strategy [Seite 101]
10.1 - Chapter 5: Monetization Strategy: Making Data Pay???????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 103]
10.1.1 - Business Levers???????????????????????????????????????????? [Seite 105]
10.1.2 - Monetization Strategy Framework???????????????????????????????????????????????????????????????????????????? [Seite 108]
10.1.3 - Decision Analysis and Agile Analytics???????????????????????????????????????????????????????????????????????????????????????? [Seite 109]
10.1.4 - Competitive and Market Information?????????????????????????????????????????????????????????????????????????????????? [Seite 119]
10.1.5 - Summary???????????????????????????? [Seite 121]
10.2 - Chapter 6: Monetization Guiding Principles: Making It Solid???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 122]
10.2.1 - Quality Data?????????????????????????????????????? [Seite 123]
10.2.2 - Be Specific???????????????????????????????????? [Seite 126]
10.2.3 - Be Holistic???????????????????????????????????? [Seite 127]
10.2.4 - Actionable?????????????????????????????????? [Seite 128]
10.2.5 - Decision Matrix???????????????????????????????????????????? [Seite 130]
10.2.6 - Grounded in Data Science?????????????????????????????????????????????????????????????? [Seite 131]
10.2.7 - Monetary Value?????????????????????????????????????????? [Seite 132]
10.2.8 - Confidence Factor???????????????????????????????????????????????? [Seite 133]
10.2.9 - Measurable?????????????????????????????????? [Seite 135]
10.2.10 - Motivation?????????????????????????????????? [Seite 136]
10.2.11 - Organizational Culture?????????????????????????????????????????????????????????? [Seite 137]
10.2.12 - Drives Innovation???????????????????????????????????????????????? [Seite 137]
10.3 - Chapter 7: Product Profitability Monetization Strategy: A Case Study?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 139]
10.3.1 - Background?????????????????????????????????? [Seite 139]
10.3.2 - Business Levers???????????????????????????????????????????? [Seite 141]
10.3.3 - Discovery???????????????????????????????? [Seite 141]
10.3.4 - Decide?????????????????????????? [Seite 142]
10.3.5 - Data Science?????????????????????????????????????? [Seite 149]
10.3.6 - Monetization Framework Requirements???????????????????????????????????????????????????????????????????????????????????? [Seite 149]
10.3.7 - Decision Matrix???????????????????????????????????????????? [Seite 152]
11 - Section IV: Agile Analytics [Seite 155]
11.1 - Chapter 8: Decision Theory: Making It Rational?????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 157]
11.1.1 - Decision Matrix???????????????????????????????????????????? [Seite 158]
11.1.2 - Probability???????????????????????????????????? [Seite 160]
11.1.3 - Prospect Theory???????????????????????????????????????????? [Seite 163]
11.1.4 - Choice Architecture???????????????????????????????????????????????????? [Seite 164]
11.1.5 - Cognitive Bias?????????????????????????????????????????? [Seite 165]
11.2 - Chapter 9: Data Science: Making It Smart?????????????????????????????????????????????????????????????????????????????????????????????? [Seite 169]
11.2.1 - Metrics???????????????????????????? [Seite 170]
11.2.2 - Thresholds?????????????????????????????????? [Seite 173]
11.2.3 - Trends and Forecasting?????????????????????????????????????????????????????????? [Seite 174]
11.2.4 - Correlation Analysis?????????????????????????????????????????????????????? [Seite 175]
11.2.5 - Segmentation?????????????????????????????????????? [Seite 178]
11.2.6 - Cluster Analysis?????????????????????????????????????????????? [Seite 180]
11.2.7 - Velocity?????????????????????????????? [Seite 184]
11.2.8 - Predictive and Explanatory Models???????????????????????????????????????????????????????????????????????????????? [Seite 185]
11.2.9 - Machine Learning?????????????????????????????????????????????? [Seite 186]
11.3 - Chapter 10: Data Development: Making It Organized???????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 188]
11.3.1 - Data Quality?????????????????????????????????????? [Seite 188]
11.3.2 - Dirty Data, Now What????????????????????????????????????????????????????????? [Seite 193]
11.3.3 - Data Types?????????????????????????????????? [Seite 194]
11.3.4 - Data Organization???????????????????????????????????????????????? [Seite 196]
11.3.5 - Data Transformation???????????????????????????????????????????????????? [Seite 200]
11.3.6 - Summary???????????????????????????? [Seite 204]
11.4 - Chapter 11: Guided Analytics: Making It Relevant?????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 205]
11.4.1 - So, What????????????????????????????????? [Seite 205]
11.4.2 - Guided Analytics?????????????????????????????????????????????? [Seite 208]
11.4.3 - Summary???????????????????????????? [Seite 220]
11.5 - Chapter 12: User Interface (UI): Making It Clear?????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 221]
11.5.1 - Introduction to UI?????????????????????????????????????????????????? [Seite 221]
11.5.2 - The Visual Palette?????????????????????????????????????????????????? [Seite 222]
11.5.3 - Less Is More?????????????????????????????????????? [Seite 223]
11.5.4 - With Just One Look?????????????????????????????????????????????????? [Seite 230]
11.5.5 - Gestalt Principles of Pattern Perception?????????????????????????????????????????????????????????????????????????????????????????????? [Seite 233]
11.5.6 - Putting It All Together???????????????????????????????????????????????????????????? [Seite 236]
11.5.7 - Summary???????????????????????????? [Seite 244]
11.6 - Chapter 13: User Experience (UX): Making It Work?????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 245]
11.6.1 - Performance Load?????????????????????????????????????????????? [Seite 245]
11.6.2 - Go with the Flow?????????????????????????????????????????????? [Seite 249]
11.6.3 - Modularity?????????????????????????????????? [Seite 252]
11.6.4 - Propositional Density???????????????????????????????????????????????????????? [Seite 253]
11.6.5 - Simplicity on the Other Side of Complexity?????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 255]
11.6.6 - Summary???????????????????????????? [Seite 256]
12 - Section V: Enablement [Seite 257]
12.1 - Chapter 14: Agile Approach: Getting Agile???????????????????????????????????????????????????????????????????????????????????????????????? [Seite 259]
12.1.1 - Agile Development???????????????????????????????????????????????? [Seite 259]
12.1.2 - Riding the Wave???????????????????????????????????????????? [Seite 260]
12.1.3 - Agile Analytics???????????????????????????????????????????? [Seite 261]
12.1.4 - Summary???????????????????????????? [Seite 265]
12.2 - Chapter 15: Enablement: Gaining Adoption?????????????????????????????????????????????????????????????????????????????????????????????? [Seite 266]
12.2.1 - Testing???????????????????????????? [Seite 266]
12.2.2 - Adoption?????????????????????????????? [Seite 269]
12.2.3 - Summary???????????????????????????? [Seite 274]
12.3 - Chapter 16: Analytical Organization: Getting Organized?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [Seite 275]
12.3.1 - Decision Architecture Team?????????????????????????????????????????????????????????????????? [Seite 275]
12.3.2 - Decision Architecture Roles???????????????????????????????????????????????????????????????????? [Seite 283]
12.3.3 - Subject Matter Experts?????????????????????????????????????????????????????????? [Seite 285]
12.3.4 - Analytical Organization Mindset???????????????????????????????????????????????????????????????????????????? [Seite 286]
13 - Section VI: Case Study [Seite 289]
13.1 - Case Study: Michael Andrews Bespoke???????????????????????????????????????????????????????????????????????????????????? [Seite 291]
13.1.1 - Discovery???????????????????????????????? [Seite 291]
13.1.2 - Decision Analysis Phase???????????????????????????????????????????????????????????? [Seite 302]
13.1.3 - Monetization Strategy, Part I???????????????????????????????????????????????????????????????????????? [Seite 310]
13.1.4 - Agile Analytics???????????????????????????????????????????? [Seite 311]
13.1.5 - Monetization Strategy, Part II?????????????????????????????????????????????????????????????????????????? [Seite 327]
13.1.6 - Guided Analytics?????????????????????????????????????????????? [Seite 337]
13.1.7 - Closing???????????????????????????? [Seite 348]
14 - Bibliography?????????????????????????????????????? [Seite 351]
15 - Index???????????????????????? [Seite 355]
16 - EULA [Seite 369]

Dateiformat: PDF
Kopierschutz: Adobe-DRM (Digital Rights Management)

Systemvoraussetzungen:

Computer (Windows; MacOS X; Linux): Installieren Sie bereits vor dem Download die kostenlose Software Adobe Digital Editions (siehe E-Book Hilfe).

Tablet/Smartphone (Android; iOS): Installieren Sie bereits vor dem Download die kostenlose App Adobe Digital Editions (siehe E-Book Hilfe).

E-Book-Reader: Bookeen, Kobo, Pocketbook, Sony, Tolino u.v.a.m. (nicht Kindle)

Das Dateiformat PDF zeigt auf jeder Hardware eine Buchseite stets identisch an. Daher ist eine PDF auch für ein komplexes Layout geeignet, wie es bei Lehr- und Fachbüchern verwendet wird (Bilder, Tabellen, Spalten, Fußnoten). Bei kleinen Displays von E-Readern oder Smartphones sind PDF leider eher nervig, weil zu viel Scrollen notwendig ist. Mit Adobe-DRM wird hier ein "harter" Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.

Bitte beachten Sie bei der Verwendung der Lese-Software Adobe Digital Editions: wir empfehlen Ihnen unbedingt nach Installation der Lese-Software diese mit Ihrer persönlichen Adobe-ID zu autorisieren!

Weitere Informationen finden Sie in unserer E-Book Hilfe.


Download (sofort verfügbar)

32,99 €
inkl. 7% MwSt.
Download / Einzel-Lizenz
PDF mit Adobe-DRM
siehe Systemvoraussetzungen
E-Book bestellen