Companies nowadays generate and collect vast quantities of data as they engage in daily business operations; however, too few genuinely treat data as a strategic asset. As companies increasingly rely on digital strategies and evolve into digital entities, they draw on data as a key source of the firm’s value. The goal of data monetization is to create value from data and implement data-driven process improvements.
A study by Research and Markets showed that the Middle East and Africa data monetization market is expected to grow at a CAGR of 4.65% over the period of 2019-2024. The growth of this market is majorly attributed to good growth of BFSI and other industries in this region, which are drifting continuously towards digitization of business processes. To maintain their market share, industry players need to constantly evolve their growth strategies as competition among them grows. Moreover, digitized business processes generate huge volumes of data that can be used for actionable insights, which will help improve growth strategy planning. Apart from increasing the demand for big data analytics solutions, it is also driving the demand for efficient data monetization solutions, thus boosting market growth.
In addition, according to research by Data Monetization in Capital Markets 2021, data monetization generally represents only 6%, on average, of total revenues of stock exchanges in the MENA region. Although stock exchanges in the region are aware of the potential of data monetization, few have formulated a full monetization strategy.
Data monetization use cases
Thanks to the data monetization examples, businesses can start treating information as an asset and gain benefits by taking its value to the maximum. Data monetization use cases are all around us; here are a few real-life examples of how companies can make profits from the analysis of data value:
- Technological giants and their data production: Companies like Google and Amazon are known as platforms that make the shopping experience easier for customers. There is more to it than typing the known address and searching for items to shop; the mentioned companies continue to optimize their platform experience through data, which they reinvest back into their platform. Creating new data-based features such as reviews, suggestions and personalized content makes customers come back to the platform. In turn, more companies are willing to promote their products through Google and Amazon, a landscape enriched with data, thereby producing even more data. So, exponential data production helps keep their platforms relevant.
- Location-based analytics: GPS technologies allow ridesharing services such as Uber to know where their users are. With users’ permission, Uber may sell this data to other businesses. As a result, other companies can use this data to offer vouchers, promotions or discounts that encourage consumers to spend money on their products.
Market Dynamic Keys
One of the key drivers in the market is the rise in business data volume and variety: organizations have generated more data in terms of volume and variety in recent years due to factors such as digitalization, IoT and advances in traditional technologies. According to Forbes, about 95% of businesses face problems while dealing with unstructured data. One of the significant factors for generating huge amounts of data is the number of devices linked to the internet; the use of these connected devices is expected to grow in the future, which would also result in the generation of huge amounts of data. So, the data generated through these sources is stored in different formats at various physical locations, and there is an advancement in various applications, including cloud, Software-as–a-Service (SaaS) and IoT, which are contributing to huge volumes of data. With data monetization solutions, you can improve decision-making and determine new revenue streams using the available data.
Furthermore, where there are drivers, there are restraints, such as varying structure of regulatory policies. New regulation policies have been incorporated by regulatory bodies for the regular functioning of enterprises; organizations should be more cautious while merging data from different sources and making it available to employees. Changing policies can be a time-consuming process, and authorities must be notified. For example, General Data Protection Regulation (GDPR) approved by the European Union (EU) Parliament has replaced Data Protective Directive 95/46/EC. After the implementation of GDPR, organizations should make changes across their data monetization solutions to cope up with the changing regulatory policies.
The collection and monetization of data is not without challenges, like the quality of the data itself. The adoption of data monetization is growing across industries, and therefore data quality is one of the important factors in monetizing data. Sharing data across industries and integrating data products with existing systems might result in a reduction in data quality, which can lead to false facts and inconsistent information. Hence, appropriate data quality directly impacts organizations’ abilities to make the right decisions. And without quality, data is not of any use, and can potentially lead to undesired outcomes. Consequently, the quality of data collected by organizations is expected to impede the adoption of data monetization tools.
