The data revolution is here. According to a study by , worldwide revenues for big data and business analytics are likely to grow from $150.8 billion (2017) to $210 billion by ; that's a compound annual growth rate of 11.9%. The question is, are companies ready for it? It’s a question that companies like , the world’s first decentralized data ecosystem are trying to answer. Unfortunately, only a handful of companies are in a position to leverage the new data economy. In this article, we’ll cover the five major setbacks that are preventing companies from participating in the data economy.
The ability to purchase and sell data is a core component of the Data Economy. However, companies have limited marketplace options. Most centralized data platforms operating in this space lack the comprehensive design needed to ensure the safety and privacy needed for large-scale data transactions. Furthermore, companies are losing ownership of the data once it has been shared with a data broker. Overall, since there is no global directory or matching engine that enables buyers and sells to exchange data, companies must rely on the data brokers networks.
Given the current systems, establishing trust between parties is extremely difficult, if not impossible; this is due to the absence of frameworks and systems that enforce data validity and integrity. Data is inherently sensitive, without stringent security measures, companies risk becoming a victim of malicious behavior. In the realm of trusted transactions, data abuse through unauthorized multiple-utilization and data abuse by transferring the data to a third party (e.g., a broker) without traceability are huge problems.
Companies are also hesitant to participate due to the potential of post-sale ramifications. The recent scandal is a great example of how data can be misused and manipulated for negative actions. In current systems, once data is exchanged, the tracking of data flows and utilization is extremely difficult. As a result, there is no way to enforce royalty pricing and maintain full ownership of data. Companies and individuals alike fear failed anonymization and leaked sensitive personal information such as medical data. Company data may also be transferred to third parties without consent.
Data management and utilization can be incredibly complex, and most companies don’t have in-house expertise. For example, raw data must be filtered, categorized, and curated before it can be extracted and used in an actionable manner like data sales or in-house utilization; this is a technical and financial burden for many companies that requires substantial allocation of human resources.
Data pricing has been an interesting phenomenon across the data market and data economy.
As it stands, parties are not aware of how to correctly measure and evaluate the market price of data; this creates a potential conflict between providers and buyers because neither can accurately calculate an optimal deal. In reality, the proper and fair valuation of datasets requires strong market expertise as well as extensive historical pricing information. Consumer data is a great example; the value varies from cents to more than several hundred US dollars per attribute. The more a consumer profile is enriched, analyzed, and leveraged for specialized uses, the more its value increases. Additionally, networks need a mechanism that enables sequential payments for recurring data within the existing systems.
The current data transaction methods are fragmented, intransparent, unsafe, and inefficient. Because of this, both businesses and consumers are not able to take full advantage of the data revolution through monetization of their data. Blockchain has provided a potential solution, that’s why companies like are so promising. The potential for change is there; the solutions just need to be realized.