Monetizing Your Data Using Google Cloud

Most companies today collect enormous amounts of data, from supply chains and daily operations to customers, partners, and competitors. Yet despite this wealth of information, an MIT study found that only 8% of companies fully monetize their data. That gap has pushed more organizations to explore practical data monetization use cases that turn raw information into measurable value.

At the same time, cloud platforms have matured, making large-scale analytics far more accessible. As a result, many leaders are beginning to look at Google Cloud data monetization as a structured, scalable way to unlock revenue opportunities that were previously out of reach.

5 MOST COMMON DATA MONETIZATION USE CASES

There are many ways to monetize data, but the following five models appear most frequently across industries. They’re practical, proven, and adaptable to companies at different stages of data maturity.

IoT data monetization

Millions of smart devices, from manufacturing sensors to connected home appliances, capture valuable information every second. For many organizations, this data is still an untapped asset./p>

To become profitable, however, IoT data must be organized and interpreted. When companies refine raw data to uncover patterns, such as energy consumption, failure trends, or environmental conditions, they transform basic device outputs into marketable insights. For example, a manufacturer selling smart equipment can package aggregated performance data and offer it to service providers or component suppliers who benefit from real-world usage insights.

Supply chain data monetization

Supply chain disruptions are costly, and companies are willing to pay for information that helps them reduce risk. Organizations involved in logistics, transportation, or parts distribution can monetize data by providing visibility into demand changes, delivery performance, or capacity constraints.

This information helps other businesses forecast downstream impact, reduce delays, and plan around potential bottlenecks. Because the value of preventing disruption is high, actionable supply chain data has become a significant source of external revenue.

Data pooling strategy

Data pooling occurs when two or more companies combine their data to create a richer, more comprehensive dataset.

There are two primary ways pooled data drives value:

• Internal use: organizations gain deeper market insights and make better strategic decisions.
• External use: data can be packaged and sold as an industry insight platform.

Pooled datasets are especially valuable for predictive analytics and are widely used in sectors such as healthcare, life sciences, and financial services, where broader context leads to far more accurate forecasting.

Monetizing customer data

Advertisers and marketers constantly look for more precise ways to reach the right audience. This creates opportunities for organizations to monetize customer data in several formats:

• Raw Data: minimal processing — lower revenue.
• Processed Data: cleaned, categorized, or enhanced — higher value.
• Insights: analysis, segmentation, or predictions — the most valuable form.

Different buyers have different needs and budgets, so offering multiple tiers expands your addressable market. The key consideration, of course, is compliance. With privacy regulations growing stricter worldwide, companies must follow all rules governing data collection, storage, and resale.

Internal data monetization

Internal monetization is the most common, and often the most underused, approach. It focuses on using your own data to improve products, reduce costs, and strengthen decision-making.

Many organizations rely on BI tools such as Tableau or Power BI, but few fully tap into what their data can reveal. Effective internal monetization depends on understanding two core categories of insights. This distinction is often described as performance vs predictive analytics data, and recognizing the difference is key to using data strategically.

1. Performance contributors: These answer “How are we doing?” They help benchmark performance, compare against industry standards, and guide operational decisions.
2. Predictive contributors: Instead of looking backward, this category focuses on what may happen next. Predictive contributors use data as a signal, such as weather patterns affecting retail sales or IoT-based driving behavior influencing insurance rates, to guide future planning.

Evaluating both types of data gives companies a complete view of how internal insights can drive growth and operational efficiency.

HOW GOOGLE CLOUD SOLUTIONS SUPPORT DATA MONETIZATION

Google Cloud has helped organizations unlock data-driven revenue for years by combining Apigee, Looker, and BigQuery into an end-to-end platform.

Apigee Analytics captures and analyzes API data, offering dashboards, custom reports, and performance trends. Companies can export this information to Google Cloud Storage or BigQuery, where they can apply BigQuery’s built-in querying and machine-learning capabilities.

To simplify adoption even further, Google recently introduced a BigQuery extension for Apigee, making integration more seamless and reducing engineering overhead.

This approach isn’t just theoretical. SoftServe recently used Apigee to help Keller Williams optimize its data monetization strategy.

Conclusion

Turning data into value doesn’t have to be complicated. With the right approach and the right tools, companies can move from collecting information to actually using it in meaningful, revenue-driving ways. What matters most is choosing a path that aligns with your goals and setting up a framework that supports long-term growth.

Ready to explore your best data monetization path? Let’s talk.

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