Data Monetization: 8 Things You Should Know

As businesses grow, more and more data is generated — leveraging that data is where the value lies…

...is the opening sentence of so many BI and data-related articles, especially those about monetization or business growth. It’s found its way into more than a few of ours. And while you may be sick of reading it, when it comes to monetizing data, it certainly rings true.

Organizations with a modern outlook, see more and more that data, and data analytics, should be viewed as an opportunity for increased value, rather than a necessity that costs money. And, this is the first step to understanding what data monetization is and why it holds so much value.

What is data monetization?

Let’s start with the ‘glossary’ definition and take a step back. Gartner defines the monetization of data as:

Data Monetization refers to the process of using data to obtain quantifiable economic benefit. Internal or indirect methods include using data to make measurable business performance improvements and inform decisions. External or direct methods include data sharing to gain beneficial terms or conditions from business partners, information bartering, selling data outright (via a data broker or independently), or offering information products and services (for example, including information as a value-added component of an existing offering).

As Gartner touches on, data monetization has the same purpose as monetization in general — gaining economic benefits from assets or converting them into money through product or service sales to customers and businesses. However, in data monetization, the focus lies on offering and selling data to achieve increased revenue, reduced costs, or a combination of the two. But, there are also softer value-added outcomes, not directly related to economic benefits, such as strengthening partnerships, making better customer experiences, and so on.

Data monetization examples

You can see data monetization examples among the biggest players in the market:

• Amazon analyzes customer behavior and preferences to personalize product recommendations, optimize inventory management, and offer said data/insights to related brands and vendors.
• Google monetizes data through advertising. If you want to reach your specific audience based on their preferences and behavior, Google helps you in collecting that data.
• IBM uses data monetization by offering various data-driven solutions and services (analytics, cloud computing, AI, etc.), it helps your business to leverage your data for insights and future improvements/innovations.
• LinkedIn monetizes its data by offering premium services to professionals and recruiters to provide access to insights and networking opportunities.
• Salesforce monetizes its data by offering customer relationship management (CRM) and cloud-based services, which help businesses manage and utilize their customer data effectively for sales, marketing, and customer service.

To see specific industry examples of monetization, check out our whitepaper.

Data monetization methods

The monetization of data and data analytics can be broken down into two sub-categories often referred to either as external and internal monetization or as direct and indirect monetization.

What is internal data monetization?

Internal data monetization is a concept used for boosting your internal operations. You can use your data for internal purposes by establishing analytics to gain valuable insights that allow you to make data-driven decisions.

This is a way of gaining economic benefits from your data indirectly. Indirect data monetization is not focused on monetizing data or data analytics by selling it, but rather on your business processes, products, and services leading to cost savings, improved competitiveness, and overall operational efficiency. You can align data monetization for the following internal purposes:

• Product improvement: You can identify potential strengths and areas for improvement by a deeper understanding of your products by analyzing customer feedback, and current product features or comparing them with competitor products. You can boost existing products or create new ones that cater to your target audience, all based on data.
• Customer experience: You have a better opportunity to create targeted marketing campaigns that highly resonate with your customers in order to encourage them to take action, such as making additional purchases or recommending your products/services by analyzing their behavior, preferences, and interactions with your brand.
• Supply chain: You can closely monitor inventory status (how many supplies you have), demand, and supplier performance, you can make data-driven decisions to minimize delays, reduce operational costs, and ensure a seamless flow of products between suppliers and customers.
• HR: You can make data-driven decisions to boost productivity and job satisfaction by analyzing employee performance and training/knowledge needs.
• Business management: You gain valuable insights for strategic decision-making and overall business optimization by tracking financial and operational performance.

What is external data monetization?

External data monetization allows you to turn your internal data assets into a valuable product or service (raw data, insights, or even an analytics interface), ready to sell the market and attract third parties — vendors, business partners, customers, etc.

It is a kind of revenue source whereby you gain money directly by one-time selling or by enabling third parties to access your stored data, providing them with insights/dashboards to drive their business decisions successfully by paying for them. To achieve this, you can align your monetization strategies with data monetization by:

• Sharing data with third parties: External parties can, for a fee, be provided with access to raw data with which they can make personalized insights and dashboards, make data-driven decisions, or develop new, innovative solutions.
• Selling insights to third parties: Rather than sharing raw data, your organization can analyze and derive valuable insights from it. These insights can then be sold to third parties to help them make data-driven decisions, streamline processes, or gain competitive advantages.
• Offering premium products: Your organization can provide high-quality products or services using your data and charge product fees. These might include advanced analytics tools, predictive models, or advanced data visualizations that offer valuable insights to your customers.
• Offering subscriptions to data and analytics: Your organization can provide data and analytics for free or offer a tiered approach whereby increased detail or granularity of analytics insights can be delivered for an increased fee. Rather than one-time buys, customers can subscribe to regularly access your data and analytics services.

Why is data monetization important?

As alluded to, data monetization is your key to unlocking new opportunities across your business. A good strategy to monetize your data guarantees you the ability to gain a competitive edge, strengthen partnerships, track new market trends, and arguably most importantly, unlock untapped revenue streams. Its importance is highlighted through its use in hospitality data analytics to optimize guest experiences, for example.

Questions to ask before monetizing data

1. What data do I have? How much value does my data hold? Does it offer value to customers and partners?
2. What is the purpose of monetizing my data? Do I want to leverage my data to streamline internal processes in order to drive growth? Or do I want to offer data or data insights to customers or partners as part of a paid service or product?
3. How does monetizing my data fit into my strategy? How does data monetization affect my overall strategy — is it about new product innovation, expansion to new markets, or other strategic objectives?
4. Do I need to pay for data only once or is it a regular purchase? Am I entering new markets or regions where data is needed on entry? Or do I need an updated data stream to develop products or innovations over a longer period?
5. What is more important: a direct revenue stream or increased retention? Do I want to open a new revenue stream by charging for data insights or promote increased growth with existing customers through value-added services by offering analytics insights for free?

By answering these questions, you can understand which strategy is right for you — selling data and gaining direct revenue streams or “exchanging” data with other related organizations gaining mutual benefits instead of direct financial benefits.

Data monetization types

As businesses slowly, but surely become data-driven, there are more and more requirements on choosing the right data monetization type. As your business expands, managing your data and exploring opportunities to generate additional revenue becomes increasingly critical.

Once you decide to pursue an external data monetization strategy, which involves offering your data, or the analysis of it, to third parties, there are several options to consider.

Data as a Service

DaaS is similar to software as a service (SaaS) — Just as SaaS eliminates the necessity of local software installation and management, DaaS shifts most data storage, integration, and processing tasks to the cloud in order to streamline these operations.

The most common place to find these data sources is on a data marketplace — a platform where you as an individual and your organization can buy, sell, or exchange raw data. It connects data providers and consumers to make data access and utilization easier.

On the provider side, you are ready to offer your data when you have already understood your data as a business asset that not only adds value to your organization but also to third parties.

Insights as a Service

Insights as a service (IaaS, but not Infrastructure as a Service), is a type of cloud service where you get precise data insights. It empowers businesses to tap into data analytics and insights without the need to handle infrastructure, software, or data management on their own.

Insights as a service offers a convenient pathway for businesses to utilize data analytics and uncover valuable insights, all without the complexities of managing the underlying technology.

Analytics-Enabled Platform as a Service

Analytics-enabled as a Service (AaaS) is a type of service that provides access to data analysis software and tools through the cloud instead of needing to invest in on-premise software solutions. Analytics as a Service combines Data as a Service and Insight as a Service into one comprehensive cloud service, which is analytics.

Embedded analytics

Monetizing data with embedded analytics involves incorporating analytics capabilities directly into your product or service offerings and then charging customers for access or insights. Embedded analytics allows you to integrate your analytics interface or parts of the analytics into other applications.

Its purpose is the same as that of traditional analytics — connect data, create metrics, and visualizations, combine them into dashboards, and share with other users. But the difference lies in the seamless integration of the analytics interface into other applications acting as one product, rather than having a separate analytics interface and the need to switch between applications.

How to monetize data via a tiered data product

We’ve touched on data monetization methods (internal vs external) as well as types, finally, let’s look at an example of utilizing a tiered product offering to gain revenue.

Imagine you are an e-commerce marketplace looking to increase revenue. By creating ‘free’ and ‘paid’ tiers within your data offering (likely via access to a partner/brand portal), you can tap into a new revenue stream while giving your customers the ability to improve their performance by supplying them with data insights into the performance of their brand on your marketplace. And as that data helps them streamline their business, so does their desire to access more data to further increase their performance.

Below is an example of how your tiered data offering might look. The free data tier is your brand partners’ entry point to analytics, giving them access to operational insights, and the paid data tier lets them expand to in-depth sales analysis and consumer behavior.

Free data tier:

The free data tier might, for example, focus on data relating to product planning and quality to optimize stock levels and reduce return rates. It will help your brand partners answer the following questions:

• Do we have enough stock for every product and size S/M/L?
• Is there any new trend emerging that might require increased stock for a specific product?
• What is the main reason for returning the product?
• Which products are the best sellers this week?

Paid data tier:

The paid data tier might, for example, focus on sales and consumer insights to bolster and optimize marketing activities. It will help your brand partners answer the following questions:

• How are we doing in sales, growth, and margin — weekly, monthly, and annually?
• Are we going to achieve the sales target for this month?
• What product is the most popular per demo/geo?
• How do we compare to other brands selling on the platform?

This tiered product structure can be used across any number of industries in slightly different forms. To learn more about using it in e-commerce, read this short whitepaper.

Data monetization with GoodData

As you can see, there are several opportunities for the effective monetization of data, both directly via paid data product tiers and API access, and indirectly through increased revenue and cost optimization.

The ideal solution for analytics data monetization? A multi-tenant, highly scalable, flexibly embeddable analytics platform like GoodData. But don’t just take our word for it, read on to understand how organizations like Stackless, Fourth, and Zartico leveraged GoodData analytics to successfully monetize their data analytics offerings. Want to see GoodData in action? Request a demo and take a tour of the platform, tailored to your use case.