How to Do Data Monetization the Right Way

Data has become one of the most valuable assets for businesses in the digital age. Leveraging data correctly through data monetization can lead to new revenue streams, better decision making, improved products and services, and deeper customer insights.

The global data monetization market is booming, expected to reach $9.1 billion by 2030 according to Fortune Business Insights. With the right strategy, your business can tap into this high-growth opportunity.

In this comprehensive guide, we‘ll cover everything you need to master data monetization the right way, including:

What is data monetization and why it matters
Types of data to monetize
Data monetization models
Benefits for your business
Step-by-step implementation process
Common challenges and solutions
Real-world examples and case studies
Top data monetization platforms

Let‘s dive in!

What is Data Monetization?

Data monetization refers to the process of generating measurable economic value from your business data, either by using it internally or by selling it externally.

Internally, data can be monetized through business intelligence and analytics to improve operations, decision making, and products/services. Externally, data can be packaged and sold to other parties or used to create valuable new data offerings.

Leading digital companies like Facebook, Google, and Amazon pioneered data monetization at scale to fuel their massive growth. But today, data monetization is accessible to organizations of any size in any industry.

Types of Data to Monetize

Nearly any type of business data can be monetized, provided it meets two key criteria:

It provides value to potential customers/buyers – There must be demand for the data you are offering and it should help solve a problem or enable new opportunities for the buyer.
You have the rights to use the data – You must own or have legal rights to collect, store, process, and sell the data.

Here are some of the most common data types with monetization potential:

Customer data (contact info, demographics, behavioral data, etc.)
Operational data (metrics, performance indicators, forecasts, etc.)
Transaction data (purchase history, pricing data, etc.)
Location data
Industry/market data
Social media data
Web traffic and analytics
Sensor/IoT data
Public data (government, weather, etc.)

Structured vs. Unstructured Data

Data can also be categorized based on structure:

Structured data – Organized, formatted data that is easy to analyze. Examples include CRM records, transaction histories, and SQL databases.
Unstructured data – Raw data with no predefined structure. Examples include social media posts, emails, and multimedia.
Semi-structured data – Data that has some organizational properties but doesn‘t strictly conform to fixed formats. Examples include web server logs and sensor data.

Unstructured and semi-structured data often requires additional processing to extract value but can contain hidden insights.

Data Monetization Models

There are two core models for monetizing data:

1. Indirect/Internal Data Monetization

With this approach, data is used internally within an organization to improve operations, services, decision making, and ultimately drive higher profits, lower costs, and better efficiency.

Companies perform extensive analytics on their internal data to uncover value. This is the most common form of data monetization among enterprises.

2. Direct/External Data Monetization

Direct data monetization involves packaging, productizing, and selling your data assets to external parties. This provides new revenue streams from your data.

Common direct monetization models include:

Raw data sales – Selling access to raw datasets.
Insights packaging – Selling reports, dashboards, and analyzed datasets containing insights.
Data subscriptions – Providing access to data through usage-based subscriptions.
Data services/tools – Offering data through APIs or software tools/platforms.
Data partnerships – Sharing/co-marketing data with strategic partners.

Direct data monetization has greater revenue potential but also requires greater effort and data commercialization capabilities.

Benefits of Data Monetization

Adopting a data monetization strategy offers many benefits:

1. Additional Revenue Streams

Data monetization enables organizations to tap into new revenue sources beyond existing products and services. According to Gartner research, enterprises can boost revenue by up to 20% through selling data assets and analytics capabilities.

2. Improved Decision Making

Analyzing real-time internal data gives decision makers enhanced visibility across all business functions. This enables data-driven decision making to optimize efficiency and strategy.

3. Better Customer Experiences

Customer data helps companies understand pain points and preferences. This allows tailoring products and services more closely to customer needs.

4. Increased Operational Efficiency

Data insights can pinpoint inefficiencies in processes and operations. Companies can take targeted action to streamline operations and reduce costs.

5. Competitive Advantage

Unique data provides differentiation in the marketplace. Companies can innovate with data-driven offerings competitors can‘t easily replicate.

6. Risk Reduction

Data helps identify potential risks and mitigate them through preventative strategies.

7. New Data Partnerships

Sharing data externally enables forging mutually beneficial partnerships with customers, resellers, and other partners.

How to Implement Data Monetization

Follow these steps to execute a successful data monetization strategy:

1. Identify Your Goals and Audience

Define your specific objectives for monetizing data. Are you primarily seeking new revenue streams or operational improvements? Who is the target audience – internal business units or external parties?

2. Catalog Your Data Assets

Take an inventory of existing data across your organization. Look for unique datasets that could provide real value. Prioritize high-quality, well-organized data.

3. Invest in Data Processing

Clean up raw datasets and structure unorganized data. Aggregate, validate, and enhance data quality to maximize value.

4. Perform Gap Analysis

Identify potential "gaps" in your data based on your monetization goals and audience needs. Consider acquiring additional external data sources if required.

5. Develop Monetization Strategy

Choose suitable pricing models tailored to your goals and audience. Build data products/services or package datasets to meet buyer needs. Outline promotional strategies.

6. Optimize Data Storage and Pipelines

Ensure you have scalable and secure data infrastructure for monetization. Automate data pipelines from collection to commercialization.

7. Protect Data Privacy

Implement robust cybersecurity measures. Anonymize personally identifiable data. Comply with all regulatory policies around data use.

8. Promote Your Data Assets

Spread awareness to both internal and external audiences about your data products. Leverage digital marketing and sales enablement strategies.

9. Provide Ongoing Value and Support

Focus on delivering a excellent customer experience. Continually enhance data value with new sources and capabilities.

Key Data Monetization Challenges

While data monetization offers big benefits, it also comes with challenges to navigate:

Data Silos

Data often gets siloed across different business units and IT systems, making it hard to aggregate into valuable datasets.

Solution: Implement data lakes/warehouses to centralize and integrate data from diverse sources.

Poor Data Quality

Flawed, fragmented, or outdated legacy data lacks integrity and monetization potential.

Solution: Invest in data cleansing, validation, and enrichment to fix quality issues.

Lack of Analytics Expertise

Many organizations lack the skills to perform advanced analytics required for monetization.

Solution: Train existing staff or hire data scientists and business analysts for your analytics team.

Legal and Compliance Risks

Monetizing customer data has significant privacy, security and regulatory implications.

Solution: Anonymize personal data. Build compliant data governance procedures.

Choosing the Right Pricing Model

Picking the optimal monetization pricing strategy is challenging.

Solution: Test different models and analyze customer response. Offer tiered pricing for broader appeal.

Data Monetization Case Studies

Here are real-world examples of successful data monetization across different industries:

Mastercard

Mastercard built its Data & Services division to productize its transaction data and sell analytics, benchmarks, and advisory services to merchants and banks.

This provides a significant new revenue channel beyond its payments processing business, leveraging Mastercard‘s data assets.

Monsanto

Agriculture giant Monsanto monetizes weather, soil, and field data to provide analytics services for farmers through its subsidiary The Climate Corporation.

Farmers can use these tools to optimize crop yields and increase operational efficiency. Monsanto benefits from both the data insights and a new data-driven revenue stream.

Rolls Royce

Rolls Royce is monetizing IoT data from its aircraft engines. Its Data Innovation Cell analyzes sensor data from over 13,000 connected aircraft to provide maintenance insights to airlines.

Airlines pay Rolls Royce for data-as-a-service based on flight hours. Rolls Royce also uses the data internally to refine engine design and health monitoring.

Top Data Monetization Platforms

Specialized data monetization platforms provide the capabilities to build data monetization pipelines. Here are top options:

1. Dawex

Dawex provides a data exchange platform to monetize, share, and subscribe to data feeds securely. It facilitates cataloging, syndicating, and marketing data products.

2. 1WorldSync

1WorldSync helps consumer brands monetize product data through data syndication across ecommerce sites, mobile apps, and retailers. This expands reach while earning incremental revenues.

3. Snowflake

The Snowflake cloud data platform supports advanced data monetization with its Snowpark development environment and Snowsight analytics tooling.

4. Terbine

Terbine‘s IoT data exchange provides tools to monetize real-time data from connected devices and sensors. Data can be sold through APIs or flat-fee subscriptions.

5. Adastra

Adastra offers a data monetization engine to help enterprises market and sell their data through integrations with ecommerce platforms and CRM systems.

Conclusion

Data monetization allows companies across all industries to unlock the profit potential in their business data. Following the strategies outlined in this guide will enable you to build a scalable data monetization practice to drive revenue, lower costs, improve offerings, and outpace competitors.

The key is choosing the monetization models aligned to your business goals and investing in the data infrastructure and skills to extract maximum value from your data assets. With the exponential growth in data globally, the opportunities for data monetization will only continue expanding in the coming years.