Data Monetization, Forecast 2018–2022

Data Monetization, Forecast 2018–2022

Powering Innovation Through Data Monetization

RELEASE DATE
30-Jul-2019
REGION
Global
Research Code: K19A-01-00-00-00
SKU: CI00632-GL-MT_23361

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Description

An increasing number of companies are starting to build data-driven strategies to fuel growth in the upcoming data economy. Though in its nascent stages, data monetization is having a huge effect on many industry verticals. Every company will, at some stage, transform into a data company through direct or indirect approaches. However, there is a lack of strategic direction and standardization in business processes among companies for building a successful data monetization model. The success of a data monetization strategy lies in extending the frontiers of digital innovation. This study aims to identify the potential routes for monetizing data assets that would help companies transform digitally and have a sustainable growth in the data economy. These routes are designed to provide a clear understanding of the business processes and the evolution of data across the value chain.

Data Bartering:
Data bartering model refers to a transaction where data sets are exchanged between two parties without any monetary assets being involved.

Data Brokering:
It is a process of collecting and aggregating data from various data sources, such as public records, scrapping online activity, and purchase history, or from third-party data providers, and selling it to data consumers.

Insights Bartering:
The insight bartering model refers to a transaction in which data insights are exchanged for business intelligence solutions between two parties.

Business Intelligence (BI):
In this model, companies build predictive insights through BI tools in order to develop actionable insights using historical and current data.

Along with the above monetization models, the study also identifies 7 actionable framework plays that are plugged into various case studies on high-performing data monetization companies. These framework plays are designed to get a 360 degree view on data use cases by ranking them in terms of providing potential growth opportunities and innovativeness. The foundation of a successful data monetization model is identifying the right strategy, design, and architecture for building a compliant and secure data ecosystem, and, most importantly, having the right pricing framework in place that aligns with the expected business outcome. The study provides insights on the key implications of data monetization on a company’s operating models and business functions. Finally, the study also offers insights on growth opportunities in the space of data monetization along with a brief future prospect of how data monetization will evolve and disrupt traditional industries, such as automotive and healthcare, and what potential new opportunities will be created in the future by data monetization.

RESEARCH: INFOGRAPHIC

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Table of Contents

Key Findings of the Study

Scope of Study

List of Definitions

List of Definitions (continued)

Introduction

Data Monetization Overview—Routes to Data Monetization

Data Monetization—Increasing Levels of Data Generation

Routes to Data Monetization—Data Brokering Model

Data Brokering

Routes to Data Monetization—Data Bartering Model

Data Bartering

Routes to Data Monetization—Insights Bartering Model

Insights Bartering

Routes to Data Monetization—Business Intelligence Model

Business Intelligence

Data Monetization Framework Plays

Process Optimization—Key Areas

Product Optimization—Key Areas

Risk Prevention and Future Proofing—Key Area

Marketing Solutions—Key Areas

Sales Optimization—Key Areas

Create New Market—Key Areas

Create New Customers—Key Areas

Data Monetization Case Studies—Main Profiles

Data Monetization Case Studies—Acxiom Overview

Acxiom—Data Monetization Model

Acxiom—Route to Monetization and Data Products

Acxiom—Technology and Partnerships

Acxiom—Data Consumers and Pricing Model

Quandl—Overview

Quandl—Route to Monetization and Data Products

Quandl—Technology and Partnerships

Quandl—Data Consumers and Pricing Model

John Deere—Overview

John Deere—Route to Monetization and Data Products

John Deere—Technology and Partnerships

John Deere—Data Consumers and Pricing Model

Uber—Overview

Uber—Data Monetization Model

Uber—Route to Monetization and Data Products

Uber—Technology and Partnerships

Uber—Data Consumers and Pricing Model

Amex Advance—Overview

Amex Advance—Route to Monetization and Data Products

Amex advance—Technology and Partnerships

Amex Advance—Data Consumers and Pricing Model

Nexar—Overview

Nexar—Route to Monetization and Data Product

Nexar—Technology and Partnerships

Nexar—Data Consumers and Pricing Model

TomTom—Overview

TomTom—Route to Monetization and Data Products

TomTom—Data Monetization Model

TomTom—Technology and Partnerships

TomTom—Data Consumers and Pricing Model

AGERO—Overview

AGERO—Route to Monetization and Data Products

AGERO—Technology and Partnerships

AGERO—Data Consumers and Pricing Model

Barclays—Overview

Barclays—Route to Monetization and Data Products

Barclays—Technology and Partnerships

Barclays—Data Consumers and Pricing Model

Pricing Rational—Two Approaches

Pricing Rational—Two Approaches (continued)

Pricing Rational—Known Use Case Pricing

Pricing Rational—Unknown Use Case Pricing

Examples of Pricing Models

Service Offerings, Data Types and Pricing Matrix

Data Regulations and Protection Laws

US Laws Governing Data Handling

Steps to Ensure Data Security—US/EU

How to Start?

Key Implementation Steps

Data Monetization Future Prospects—Automobile

Data Monetization Future Prospects—Healthcare

Legal Disclaimer

List of Acronyms and Abbreviations

Key Questions Addressed in the Study

Data Monetization Service Architecture—Delivery Models

Potential Use Cases for Your Industry

List of Exhibits

Related Research
An increasing number of companies are starting to build data-driven strategies to fuel growth in the upcoming data economy. Though in its nascent stages, data monetization is having a huge effect on many industry verticals. Every company will, at some stage, transform into a data company through direct or indirect approaches. However, there is a lack of strategic direction and standardization in business processes among companies for building a successful data monetization model. The success of a data monetization strategy lies in extending the frontiers of digital innovation. This study aims to identify the potential routes for monetizing data assets that would help companies transform digitally and have a sustainable growth in the data economy. These routes are designed to provide a clear understanding of the business processes and the evolution of data across the value chain. Data Bartering: Data bartering model refers to a transaction where data sets are exchanged between two parties without any monetary assets being involved. Data Brokering: It is a process of collecting and aggregating data from various data sources, such as public records, scrapping online activity, and purchase history, or from third-party data providers, and selling it to data consumers. Insights Bartering: The insight bartering model refers to a transaction in which data insights are exchanged for business intelligence solutions between two parties. Business Intelligence (BI): In this model, companies build predictive insights through BI tools in order to develop actionable insights using historical and current data. Along with the above monetization models, the study also identifies 7 actionable framework plays that are plugged into various case studies on high-performing data monetization companies. These framework plays are designed to get a 360 degree view on data use cases by ranking them in terms of providing potential growth opportunities and innovativeness. The foundation of a successful data monetization model is identifying the right strategy, design, and architecture for building a compliant and secure data ecosystem, and, most importantly, having the right pricing framework in place that aligns with the expected business outcome. The study provides insights on the key implications of data monetization on a company’s operating models and business functions. Finally, the study also offers insights on growth opportunities in the space of data monetization along with a brief future prospect of how data monetization will evolve and disrupt traditional industries, such as automotive and healthcare, and what potential new opportunities will be created in the future by data monetization.
More Information
No Index No
Podcast No
Author Chaitanya Habib
Industries Cross Industries
WIP Number K19A-01-00-00-00
Is Prebook No
GPS Codes 9A3B,9B07-C1