What is data monetization? Gaining value through strategy

Just because you have massive amounts of data, that doesn’t mean it’s valuable to your business. Data monetization turns the information your business already has into an economic gain. Done right, it can unlock new revenue streams, identify and fix inefficiencies, and help you carve out a competitive edge in your industry.

In this guide, we break down what data monetization is, why it matters, and how you can put it to use.

What is data monetization?

Data monetization is the process of turning data insights into economic opportunities. This often means selling information to third parties for market research or customer sentiment analysis purposes, but internal data monetization shouldn’t be overlooked. By collating and analyzing your data, you can use your findings to improve customer experiences, reduce risk, and enhance efficiency across your business. 

Why is data monetization important?

Many strategic reasons make data monetization worth your attention, including:

• Unlock new revenue streams: Analyzing metrics helps you identify fresh revenue streams, whether that’s selling data to a third party or a better way to sell to your customers.
• Gain a competitive advantage: By investigating which products and services your customers are interested in, you can identify gaps in the market and new ways of building loyalty.
• Enhance operational efficiency: In-depth data analysis can identify new efficiencies and opportunities for you to act on, allowing you to improve internal processes, cut costs, and streamline product development. 
• Develop stronger partnerships: It’s natural to be protective of your company’s data, but sharing information with another business through a data partnership can improve cross-company relationships and give you both an advantage over less forthcoming organizations. 

Key monetization strategies 

While there are many different data monetization strategies‌, they generally fall into two major categories. 

Internal/indirect

Internal data monetization is about improving performance. Depending on your specialism, this could mean anything from building better products to enhancing customer services. 

Examples include:

• Improving products and services based on usage data and customer feedback
• Providing more personalized customer service to improve engagement
• Reducing internal risk or identifying inefficiencies in existing processes

External/direct

External monetization is the sale or exchange of data with third parties. As it’s a transaction, it typically results in an immediate or near-immediate financial boost.

Examples include:

• Selling raw or aggregated data directly to third parties
• Selling analytics, insights, and reports from internal data investigations
• Offering data-as-a-service (DaaS) that other businesses can use in their analytics engines

4 steps to creating a data monetization strategy

Building a reliable monetization strategy doesn’t necessarily mean starting from scratch, especially if you’re already using data across your organization. You likely already have the required data integration, storage, and analytics capabilities in place.

To turn your existing architecture into one that facilitates monetization, follow these four steps:

1. Identify high-value assets

Look for the content that could provide the most value. A good standard is anything unique or difficult to replicate. Perhaps your business has proprietary systems or long-standing customer data you can draw from, or maybe you have access to operational processes that your competitors lack.

2. Define your strategy

After identifying value, break down how you intend to monetize each dataset. Internally, this includes outlining any potential efficiency gains or cost reductions it could provide. For external cases, analyze the market demand to see how your data could be useful to third-party companies. 

Bringing in stakeholders from different departments can help you manage this process. For instance, product leaders can help assess feasibility and pricing, while data engineers can break down how to share your content. Build your strategy around collaborative team insights. 

3. Ensure compliance

Any data you share must fall within compliance regulations. Even internally, you need to ensure that any sensitive or private information aligns with data protection frameworks.

Work with legal and compliance teams to review which specific regulations and legislation apply to your data. Depending on your field, this could include HIPAA or PCI-DSS, and you could be required to anonymize or aggregate data while defining a clear usage policy. 

Since you should already have a comprehensive compliance policy in place, these standards will likely just build upon your existing strategy. Automated compliance tools can really streamline this process. 

4. Choose the right platform

If you already democratize your content in a data platform, it’ll be much easier to perform analytics and deliver useful insights, but you’ll also need to ensure the data you ingest is high-quality, timely, and accurate. 

The easiest-to-use data monetization platforms build on your existing data architecture. Look into automated ELT pipelines that manage common issues like schema drift, such as those offered by Fivetran.

When ingestion, storage, and visibility are all accounted for, you can also consider tools like analytics engines or dashboards. A well-ordered data system makes it easier to scale your monetization efforts, as you’re less likely to run into roadblocks like poor data quality or a lack of observability.

Data monetization use cases

Everything from your business sector to your specific products and services can influence how you draw value from your data. 

To put this into perspective, here are a few data monetization examples:

• Retail data monetization: Storefronts collect enormous volumes of customer, product, and transaction data. Product development teams can use this information to see what’s selling, which features customers are drawn to, and what trends to look out for.
• Banking data monetization: Financial teams can use customer data to enhance fraud detection systems and improve online banking experiences. 
• Telecommunications monetization: Companies can sell performance and coverage data to telecom suppliers to help them understand which infrastructure upgrades to prioritize.

Challenges of data monetization

While there are some big economic upsides to data monetization, there are also some challenges you should be aware of:

• Data privacy and ethics: Before you can monetize data, you need to get consent from all involved parties. You must also rigorously comply with privacy regulations throughout the entire process. 
• Data quality: No one wants to buy poor-quality data. Your underlying systems need to be scalable and high-quality, and should always deliver accurate information.
• Accurate valuation: It can be difficult to work out a dataset or report’s true value, especially if you work in a unique field without competitors to compare to.

Ensure accurate, valuable data with Fivetran

Data monetization relies on a consistent stream of high-quality information that you can use internally or distribute externally. Fivetran automates the extraction and loading of data, powering your monetization strategies with real-time, accurate content. By eliminating the need for manual pipeline maintenance, your engineers can spend more time turning your collected data into valuable insights

Fivetran Transformations, powered by dbt core, lets you automate the cleaning and modeling of data directly within your warehouses. Whether you’re analyzing for internal use or to sell to third-party clients, Fivetran makes your data accurate, secure, and ready for market.

Learn more about streamlining your monetization strategy with Fivetran architecture by booking a live demo today.

FAQs

Can you provide examples of data monetization?

Here are two examples of data monetization:

• Internal data monetization: Analyzing your production line to find inefficiencies and optimizing them to improve performance.
• External data monetization: Selling the results of a research report to other companies. 

What are some ways to monetize your data?

You can monetize data by using insights to reduce risk, improve performance, and build products that your customers respond better to. You could also choose to sell your data to third-party companies.

Is data monetization legal?

When done responsibly and in line with compliance requirements, data monetization is a legal process. Data monetization markets allow you to research companies that may wish to buy your insights or could be interested in a data-sharing agreement.

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