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What is Data Monetization? Types and Challenges

  • Yashoda Gandhi
  • Feb 12, 2022
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Today, every business is a data business. The majority of them have access to a wealth of information about their supply chains, operations, strategic partners, customers, and competitors. 


Despite this, most businesses are squandering money, with only one out of every twelve utilizing data to its full potential. Data has value on its own, but insights derived from it vastly increase that value.


Data monetization


“Data monetization is the process of using data to increase revenue. The highest-performing and fastest-growing companies have adopted data monetization and made it an important part of their strategy”, According to McKinsey & Co.


In simpler words, The process of using company-generated data to generate a measurable economic benefit is known as data monetization. As a result of monetizing their data, businesses often see benefits such as increased revenue or lower costs.


Companies can also use their data to build less tangible benefits like new partnerships or better supplier terms by sharing it with third parties in a mutually beneficial arrangement.


In some cases, businesses realise their data is valuable enough to start selling data services to a large number of outside businesses. Facebook and Google were early adopters of this trend, leveraging their free platforms to create massive data assets that could be sold all over the world.


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Types of data monetization


As our world has become more data-driven, so have the methods for monetizing data. The best methods give you the ability and flexibility to extract the most value from the advantages of Big Data from a wide range of sources. 


As your company grows, you'll need to figure out which monetization strategy works best with your overall data strategy. 


As a result, it's critical to weigh the options, figure out which ones are best for your current and future needs, and figure out which BI and analytics platform can provide you with the data monetization tools you need.


  1. Data as a service


This, also known as data syndication, is the most basic of the three business models. Anonymized and aggregated data is sold to either intermediary companies or end customers who mine it for insights.


Customers can be either downstream or upstream in a company's value chain: Kroger collects and sells shopping data generated by its rewards card to CPG companies interested in learning more about their customers' shopping habits and changing tastes and preferences.


  1. Insight as a service


Advanced analytics can also be used to combine internal and external data sources in order to provide actionable insights. AkzoNobel has developed a decision-support model for ship operators to enable fuel and CO2 savings. 


They provide ship operators and owners with an advanced analytics-enabled mobile iOS app that provides continuous performance prediction of coating technologies.


This method gives vessel operators more power by allowing them to compare the financial and performance benefits of different coating options, allowing them to make better investment decisions.


  1. Analytics-enabled platform as a service


The most complicated of the three business models, it also provides the most value to customers. Companies generate enriched, highly transformed, customised real-time data that is delivered to customers via cloud-based, self-service platforms using sophisticated and proprietary algorithms.


GE's Predix platform, for example, adds value to customers by providing data-driven services that improve machine efficiency. GE provides commercial, industrial, and municipal customers in San Diego, California, and Jacksonville, Florida, with integrated and technology-enabled energy management systems (EMS) for lighting and energy.


Customers can use GE's Predix platform to get predictive analytics and prescriptive analysis about energy use, maintenance, and other outcomes, which can help them save money by simplifying energy processes and resulting in automation and operational efficiencies.


  1. Embedded analytics


This is the most cutting-edge and exciting method of monetizing data that provides the most value to customers. Simply put, embedded analytics refers to the integration of BI software features such as dashboard reporting, data visualisation, and big data analytics tools into existing applications.


Sisense is the only embedded analytics platform built from the ground up with agility and scalability in mind. It shortens the time to market, lowers the total cost of ownership, and creates a fully customised embedded analytics solution tailored to your specific requirements.


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Challenges of data monetization


Companies are racing to figure out how to move their business forward in order to open up new revenue and growth opportunities by overcoming the following major challenges in data monetization:


  • There is currently no law that specifically addresses the monetization of government or other data. In its current form, the proposed Personal Data Protection Bill does not adequately address this issue.


  • Data collection is highly decentralized, with different ministries collecting data in their own ways. As a result, each ministry only has a small piece of the individual/firm jigsaw puzzle. Consolidating data is difficult due to the lack of a common identifier.


  • Legal and social implications- Legal experts are not enthusiastic about the idea of selling data.


  • Governments rarely monetize citizen data, but such measures have been implemented in the past. The Ministry of Roads and Highways, for example, has announced a policy for the sale of vehicle registration data. This is concerning because India lacks any meaningful data protection legislation.


  • There are currently no specific provisions prohibiting the government from granting the private sector commercial access to certain public databases.


  • However, two proposed legislation (the Digital Information Security in Healthcare Bill, 2018 and the Personal Data Protection Bill, 2018) is currently pending in Parliament that, if passed in their current form, could have a direct impact on the proposed activity and, at the very least, could result in the government directly violating its own laws by engaging in such activities.


  • Research has shown that there is no fully secure method of data anonymization and that data can be re-identified, implying that data of minors can be re-identified or misused. (source)


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5 rules of data monetization


Monetization is the starting point for using data as a competitive asset. It can generate a new revenue stream, allowing the company to increase investment in data capture, processing, and insights while lowering other costs and increasing profitability.


The amount of data available to businesses is increasing at an exponential rate. Unlike tangible assets, the value of data can increase as it is used and analyzed in more ways. Your company can start treating data as an asset and reap the benefits of maximizing its value through data monetization.


  • Recognize the importance of data in your company


Making sure you have the right data to support your business is the goal of good data management. Smart data usage also aids risk management and ensures adherence to all applicable laws and regulations. Companies frequently fail to value their data accurately because it is not strictly accounted for as an asset.


  • Organize your information


Metadata, or data about data, such as data quality, storage location, and meaning, is lacking in far too many businesses.


In fact, many businesses are more likely to have a detailed inventory of their office furniture than their own data. Companies must first determine what kind of data they have about their partners, customers, products, assets, or transactions, as well as what publicly available data can be used to boost the value of their proprietary data.


  • Integrate data monetization into your business strategy and create the necessary structures


Too often, related data management initiatives do not support corporate strategy, and vice versa. Executives should consider how data can help them achieve their key business goals and strategic initiatives.


You can put the right structures in place to monetize data once you understand its quality and how it relates to business strategy. 


Creating a platform for data-driven business innovation often entails assembling a multi-disciplinary, cross-functional team – including information product leads, data management experts, and executives from sales, marketing, and operations.


  • Welcome new possibilities


Data has enormous potential to add value to many aspects of the business. However, because companies are so used to pursuing growth through established strategies and revenue streams, it can be difficult for them to envision exactly what the opportunities are.


As a result, all businesses should be willing to learn from one another and collaborate in data-driven ways.


  • To encourage growth, communicate the value of data both internally and externally


Many businesses are still learning how to monetize data, and even when successful initiatives are in place, the rest of the company isn't always aware of them. 


As data becomes more important, businesses will need to educate and communicate with internal and external stakeholders so that they fully understand the value data can provide.


One way to raise internal awareness is to hold a brainstorming session with data professionals or business function leads to demonstrate how data can help them improve their processes. Then there are the success stories of companies that have successfully monetized their data, which tend to persuade even the most sceptic executives.

Finally, the earlier a company educates itself and decides on a data monetization strategy, the better. With the amount of data growing at an exponential rate, companies that understand its potential and can monetize it should have a significant advantage over competitors who are still trying to figure out what it means for their business. (source)


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