What is Data Monetization?

Data monetization is the process of utilizing data to procure economic benefits. Direct or external data monetization involves selling data to third parties independently or via a broker, data sharing to obtain beneficial business terms and conditions, and offering information services or products.

Data can be sold in its raw form or in a form that already incorporates insights and analysis. A simple example of direct data monetization is contact lists of potential business prospects that influence buyers’ businesses or trades. Indirect or internal data monetization involves making measurable business performance improvements and informed decisions using data.

This method also focuses on identifying how to reach customers and understand customer behavior to drive sales. It also highlights where and how to save costs, avoid potential risks, and streamline operations.

How to Use Data Monetization: Use Cases and Examples

When data monetization is used efficiently by a business, it increases the scope and flexibility to obtain the most out of big data from various sources. However, as the business grows, business users need to decide which monetization approach best fits their data strategy. This means it is essential to consider different methods, establish which are most suited to current and future business requirements, and which platform provides the data monetization tools that are right for their business needs.

Data as a Service

Data as a Service (DaaS) is the most simple and straightforward data monetization method. The data is sold directly to intermediates or customers, in either an aggregated or raw form. Buyers can then mine the data for insights relevant to them. These data buyers do not get insights or analytics from the data, but instead, derive this information themselves.

Insight as a Service

The organization merges external and internal data sources and applies analytics to derive insights. These insights can be directly sold or transformed and sold in different formats. These insights are limited to the context, datasets, and specific information purchased

Analytics-Enabled Platform as a Service

This is one of the most flexible types of data monetization, and it can provide a significant amount of value to customers. Here, a business intelligence and analytics platform is installed and implemented to provide customers with scalable and highly versatile data analytics in real time.

Embedded Analytics

This is the most advanced, and often the most appealing way, of data monetization. It provides the most value to customers. In simple words, embedded analytics involves adding features associated with business intelligence software such as analytics tools, dashboard reporting, and data visualization to existing applications. Using this technique, product teams can create and implement customized, actionable analytics apps at scale and integrate them into other applications that the company uses. This opens up new revenue streams and provides a strong competitive benefit.

Data Monetization Use Cases

For any company, data is precious. But how to find its worth? A company’s data value can grow in three main ways:

• Gain more insights about customers to create higher value sales
• Sell insights to third parties
• Generate more data

Irrespective of the company’s domain, data monetization pays off. There are many examples of how companies can increase revenue from data value analysis.

E-Commerce Data Monetization

E-commerce companies in particular are known for making the shopping experience easier for customers. But this is also a way of increasing their customer data. Users save their addresses, other contact information, what items they’ve searched for, and their preferred payment methods. While these functions are all helpful to the user, they are all valuable data for the organization too.

These companies continue to optimize their platforms through customer data, which they reinvest back into their platform. Search suggestions like “Customers who bought this also bought that” and “you might also be interested in” are helpful to customers while generating more revenue.

They ensure customers visit their platform more frequently by creating helpful, personalized features.

Location-Based Analytics

Data monetization can also be location-based for services like rideshares. With customers’ permission, many rideshares sell location-related data to other businesses. Other businesses then use this data to offer location-based discounts, vouchers, and advertising.

Telecommunication

Telecom players typically adopt external data monetization methods through partnership models in B2B and B2C segments. In a few cases, companies have also acquired startups for collaboration and assistance. The gathered data allows promoters and advertisers to target messages to specific users better.

Why Data Monetization Is Important

By investing in a company’s data collection, organizations can achieve higher revenues. Good data monetization strategies guarantee that organizations get the most value from their data both internally and externally. They can sell the data externally and increase profits, minimize costs internally, and optimize opportunities for the organization.

Creates New Customer Opportunities

Several organizations are recognizing the value of their data. With an adequate data volume, they leverage the tapped and untapped market to create new sources for revenue. By further refining market segments, they can better target their ideal customers.

Increases Data Value

Tech giants and social media platforms collect all the activities associated with a user. This means they identify many features about their users such as their interests, shopping preferences, and level of income. These attributes enhance internal data and maximize the value of the data they collect.

Provides Market Information on a Broad Scale

Customer data provides businesses with insights such as market trends, geographic demand patterns, impact of competition, and the shelf life of customer data. Is the data worth as much in six months, or is it stale and useless by then?

Increases Internal Productivity

Data can maximize productivity as well as decrease the amount of waste or excess consumption.

Creates Competitive Advantage

Successful companies monetize data by understanding their customers’ preferences. This helps them offer products or services that are highly relevant to their customers and create a competitive advantage in the market.

Boosts Profitability

Data is essentially valuable, but it is the comprehension derived from data that builds value for an organization. It can help segment customers to allow better targeting, predict demands, optimize price, and manage costs—resulting in overall profitability.

Enhances Customer Experience and Bolsters Customer Loyalty

Understanding customer needs and preferences improves customer experience. This makes the customer remain more loyal to the product offering and reduces customer churn.

Uplifts Revenue Streams

Data monetization helps segment the customer database based on gender, industry, preferences, demography and a range of other socio-economic groups. These classifications allow business owners to deliver customized messaging, provide a better user experience, and increase revenues.

Strengthens Partnerships

The process of purchasing and selling data happens in a data marketplace. Data owners can set data prices, and consumers can choose from whom they wish to buy data. This improves data collaboration and sharing between internal and external stakeholders.

Streamlines Decision Making and Planning

The data marketplace divides audiences and offers the right set of consumers for the right kind of data. It gives a depth of insight that allows decision makers to understand their business better, anticipate changes in the market, and manage risk better.

Identifies and Mitigates Risk and Improves Compliance

Data is a key asset for any organization in today's world. However, it needs to be leveraged as per individual privacy rights. Data monetization requires an organization to have their data organized, legally obtained, managed, and protected. If a business wants to sell its data, it needs to be completely compliant with regulations.

Data Monetization Challenges

Every business generates potentially valuable content and data. As with all emerging new technologies, companies are responding to new opportunities—but not always successfully. With data monetization, business owners often face some strategic, organizational, and technological challenges:

Strategic Challenges: New Opportunities in Unfamiliar Markets

For companies operating outside the information services industry, processed data is generally a by-product of their core activity. In this case, the monetization opportunity represents a new business area with product offerings, revenue models, and regulatory constraints the company may be unfamiliar with.

Strategic Challenges: So Many Possibilities, Such Little Time

With so many information assets, organizations must decide where they want to play in the data value chain, which in turn raises numerous questions about the data value, products, services, internal assets, and technology choices. So, with different implications and options, the problem is often about finding quickly which products or services to pursue before the opportunity window closes. Without proper prioritization, companies may not achieve their desired result.

Organizational Challenges: Technology-Driven Decisions

With technology-driven decisions or capabilities, new products often display unnecessary or complicated features and prove disconnected from actual end-user demands or needs. When this happens, business owners or executives trying to monetize data find themselves in a tough situation: finding an issue with the solution they just developed.

Organizational Challenges: Requires New Skills and Expertise

For many data providers, new opportunities means moving up the value chain. This involves building off existing data and shifting to productivity tools and workflow solutions more embedded in customers’ businesses. Such migration assures new revenue streams, longer-term relationships, and higher margins. However, this process typically needs more sophisticated skills, technology, and expertise.

Organizational Challenges: Data Ethics

Is this data collected legally? Stored correctly? Is it allowed to be sold, or used in the manner that’s intended? This is an ever-changing and challenging area of regulatory compliance.

Technological Challenges: Data and Content Are Locked Away

In companies that have already invested in prior-generation technology, data is often designed to be locked within the company’s firewall. However, turning these contents into revenue-generating products may prove expensive and present several technical and operational issues.

Technological Challenges: Lack of Scalability

Companies that are new to the content or data space typically have traditional infrastructure that lacks scalability. For any business, flexibility and scalability are critical to support self-service subscriptions and increased business velocity.

Things to Consider While Implementing Data Monetization

While organizations share many attributes and requirements, each business has its own set of features needs. As a business owner, there are a few things to consider when choosing a data monetization system:

Is the Choice of Analytics and Business Intelligence Platform Future-Proofed?

Your analytics and business intelligence platform of choice must have the scope to handle both current and future data. This is because in any business, the rate of data generation grows rapidly. Some large businesses might generate tens of petabytes of data daily.

Is Data Monetization Comprehensive?

If the business entity gathers more data, then there will be more formats as well. You must ensure that your data monetization platform supports a wide range of formats, including Excel, JSON, XML, EBCDIC, and others. In addition, your choice of data monetization tool must handle inputs from a wide range of enterprise software.

Is It Scalable and Flexible?

As your business grows and changes, there should be scope to scale the business’s data monetization capability. Your data monetization platform should also be adept at coping with other changes that the company might experience over time.

Does The Analytics and BI Platform Meet Data Monetization Needs?

Ideally, an analytics and business intelligence platform should meet specific needs, so you should consider customizing it accordingly. Also, ensure that the platform is purpose-built for developers to reduce development time. Additionally, you should be able to embed analytics everywhere and integrate them into your business’s services and products.

Is the Data Monetization Capability User-Friendly?

To increase the value derived from your business intelligence platform, it should enable more users across the organization to visualize, analyze, and act on business data. It should also automate repetitive tasks and offer coding-free drag-and-drop functionality so that non-technical users can transform data easily.

Data Monetization is Complex but Rewarding

Collected, stored, and used correctly, data can be an invaluable resource. It offers another revenue stream to your business, as well as proving value within the organization through the value of insights.

However there are significant challenges when implementing data collection, storage, and sale, and it shouldn’t be an ad-hoc decision. Time and resources should be used to ensure everything is done correctly and to the benefit of the organization.

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