Monetizing your data : a guide to turning data into profit-driving strategies and solutions / Andrew Wells and Kathy Chiang.

Format:

Book

Author/Creator:

Wells, Andrew, author.

Chiang, Katherine S., author.

Series:

THEi Wiley ebooks.

THEi Wiley ebooks

Language:

English

Subjects (All):

Finance--Data processing.

Finance.

Physical Description:

1 online resource (369 pages)

Edition:

1st edition

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

System Details:

Access using campus network via VPN at home (THEi Users Only).

text file

Summary:

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.

Contents:

Cover

Title Page

Copyright

Contents

Preface

Acknowledgments

About the Authors

Section I: Introduction

Chapter 1: Introduction

Decisions

Analytical Journey

Solving the Problem

The Survey Says…

How to Use This Book

Let's Start

Chapter 2: Analytical Cycle: Driving Quality Decisions

Analytical Cycle Overview

Hierarchy of Information User

Next Steps

Chapter 3: Decision Architecture Methodology: Closing the Gap

Methodology Overview

Discovery

Decision Analysis

Monetization Strategy

Agile Analytics

Enablement

Summary

Section II: Decision Analysis

Chapter 4: Decision Analysis: Architecting Decisions

Category Tree

Question Analysis

Key Decisions

Data Needs

Action Levers

Success Metrics

Category Tree Revisited

Section III: Monetization Strategy

Chapter 5: Monetization Strategy: Making Data Pay

Business Levers

Monetization Strategy Framework

Decision Analysis and Agile Analytics

Competitive and Market Information

Chapter 6: Monetization Guiding Principles: Making It Solid

Quality Data

Be Specific

Be Holistic

Actionable

Decision Matrix

Grounded in Data Science

Monetary Value

Confidence Factor

Measurable

Motivation

Organizational Culture

Drives Innovation

Chapter 7: Product Profitability Monetization Strategy: A Case Study

Background

Decide

Data Science

Monetization Framework Requirements

Section IV: Agile Analytics

Chapter 8: Decision Theory: Making It Rational

Probability

Prospect Theory

Choice Architecture

Cognitive Bias

Chapter 9: Data Science: Making It Smart

Metrics

Thresholds

Trends and Forecasting

Correlation Analysis

Segmentation

Cluster Analysis.

Velocity

Predictive and Explanatory Models

Machine Learning

Chapter 10: Data Development: Making It Organized

Data Quality

Dirty Data, Now What?

Data Types

Data Organization

Data Transformation

Chapter 11: Guided Analytics: Making It Relevant

So, What?

Guided Analytics

Chapter 12: User Interface (UI): Making It Clear

Introduction to UI

The Visual Palette

Less Is More

With Just One Look

Gestalt Principles of Pattern Perception

Putting It All Together

Chapter 13: User Experience (UX): Making It Work

Performance Load

Go with the Flow

Modularity

Propositional Density

Simplicity on the Other Side of Complexity

Section V: Enablement

Chapter 14: Agile Approach: Getting Agile

Agile Development

Riding the Wave

Chapter 15: Enablement: Gaining Adoption

Testing

Adoption

Chapter 16: Analytical Organization: Getting Organized

Decision Architecture Team

Decision Architecture Roles

Subject Matter Experts

Analytical Organization Mindset

Section VI: Case Study

Case Study: Michael Andrews Bespoke

Decision Analysis Phase

Monetization Strategy, Part I

Monetization Strategy, Part II

Closing

Bibliography

Index

EULA.

Notes:

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed March 14, 2017).

ISBN:

9781119356257

1119356253

9781119356271

111935627X

9781119356264

1119356261

OCLC:

973932828