The technique of leveraging data to generate extra revenue is known as data monetization. Data monetization has become an integral part of the strategy of the highest-performing and fastest-growing organisations. Direct data monetization entails supplying third parties with direct access to your data. You can sell data in its unprocessed state or in a state where it has already been turned into analysis and insights. Contact lists of possible business prospects or results that have an influence on the purchasers’ sectors and businesses are common examples.
Things start to become tricky when it comes to indirect data monetization. There is data-driven optimization, for starters, and this entails studying your data to uncover insights that will help your company perform better. Data can help you figure out how to connect with your customers and analyse their behaviour so that you can increase sales. Data can also show you where and how you may cut expenses, reduce risk, and optimise operations.
Second, there are data-driven business models, in which you leverage your data to find new consumers and business prospects. You can use analytics into your goods and services to benefit both the company and the customers. Customers can gain immediate access in using metrics and other data collected by each of the products they are already using. Providing this as a value add-on or a higher tier of service benefits you by fostering customer loyalty. As a result, you gain a deeper understanding of how your customers use your products.
Here’s an overview of essential data monetization methods for a great start to a company’s data monetization strategy.
Data Monetization Methods
As our world has become increasingly data-driven, so the ways of monetizing data have developed. The best methods provide you with the capacity and flexibility to get the most out of Big Data from the greatest variety of sources. As your organization grows, you need to decide which monetization approach fits best with your larger data strategy. It’s therefore important to consider the main methods, establish which of them are most suited to your current and future requirements, and which BI and analytics platform can provide you with the data monetization tools that are right for your needs.
Data As A Service
This is the simplest, most direct data monetization method. Data is sold directly to customers or intermediates. The data is either raw, aggregated or anonymized and the buyers mine the data for insights. Buyers don’t benefit from receiving insights — they derive those for themselves. Nor do they benefit from advanced analytics.
An example would be, telecommunications companies provide aggregated and anonymized customer geolocation data to local governments, allowing city planners to design more effective traffic management systems and officials to better establish “smart city” technology solutions.
Customers can also be the downstream or upstream players in a company’s value chain: Grocery retailer Kroger captures shopping data generated by its rewards card and sells it to consumer packaged-goods companies thirsty for a deeper understanding of their customers’ shopping habits and evolving tastes and preferences.
Insight As A Service
Internal and external data sources are combined, and analytics are used to generate insights. The information can be sold directly or in formats like analytics-enabled applications that supply real-time data. The insights are restricted to the buyer’s purchased datasets or contexts. To extract efficient activities from database systems, ” Insights as a service” utilises forecasting analytics and business intelligence. Companies frequently lack an adequate architecture and IT personnel required to extract significant insights from the massive amounts of data they generate on a regular basis.
“Insights As A Service” platforms answer this requirement for cloud-based in-depth analytics and deliver solutions to unique data-related difficulties. Furthermore, it helps businesses to dramatically save expenses by eliminating the requirement for data scientists and the costly infrastructure required to conduct analytics on-premise. “Insights As A Service” aims to provide a visually appealing and complete data set that aids in the optimization of operations and revenue generation. This is critical for businesses who store their data in various silos and try to scan it manually or on-premise.
Analytics-enabled Platform As A Service
This is a more adaptable method of data monetization that offers clients significantly more value. The analytics and business intelligence platform is installed and configured to give customers extremely versatile and adaptable real-time data analyses. It’s accessible on-premises and in the cloud, and it’s compatible with all cloud-based data warehouses and the broadest range of data formats, allowing people to work with data from any source and in any format. To get the most out of cloud-based products, companies will need expert installation and maintenance.
A platform that adds value to consumers by providing data-based services that improve the efficiency of its machines is one example. The platform provides commercial, industrial, and governmental clients with holistic and technology-enabled energy management systems (EMS) for lighting and energy. They bring together the advantages of energy-efficient LEDs, cutting-edge sensors, cloud-based software, and sophisticated analytical models. Companies can utilise the platform to forecast and prescribe analysis about energy use, upkeep, as well as other outcomes accessible to their customers, allowing them to make cost-cutting decisions by streamlining energy operations, which leads to automation and operational savings.
Embedded Analytics
This is the most innovative and exciting method of monetizing data that gives clients the most value. Simply defined, embedded analytics refers to the addition of Business Intelligence software elements to current applications, such as dashboard reporting, visualisation of data, and analytics tools. Development teams may create and grow specialised actionable analytic apps that seamlessly interface with other apps, resulting in new revenue streams and a significant competitive edge. Sisense is the first embedded analytics platform designed from the ground up to deliver both agility and scalability. It reduces time to market, decreases TCO, and delivers a fully customised embedded analytics solution for your individual needs.
You risk missing important insights that could benefit your business if you do not have the correct data monetization techniques in place. You are well prepared to sharpen your competitive edge if you have the right strategy in place. Furthermore, effective data monetization guarantees that you obtain the most value from your data by increasing profits, lowering costs, and optimising opportunities for your company, consumers, and partners.