Emerging Concepts Using Blockchain and Big Data

Blockchain technology has been known to exist for almost two decades now. However, it became popular after the news on Bitcoin became widespread globally. Blockchain is defined as a revolutionary innovation that applies distributed ledger systems. It is a redistributed network topology with an improved security level. In Blockchain systems, details are recorded in a new block where the users involved or connected to the network have to check its validity after every transaction. All these blocks are connected to form a chain hence the name blockchain. Every transaction timestamp is recorded so that nothing can tamper with the records; thus, any alterations or addition cannot be made (Arora, 2018). Blockchains offer advanced levels of security and transparency, and the recorded data retains its consistency and integrity. Blockchain technology offers smart contracts that enable users to record and settle transactions in a network automatically. App developers can code and build in the specifics about payments such as wallet address, information on bank accounts, and other important details. After the execution of the transaction, users connected to the network can then validate and settle them automatically (Arora, 2018).

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On the other hand, as the name indicates, big data is an assembly of an enormous amount of collected datasets. Due to the amount and complexity of data, traditional data processing cannot retrieve, analyze, modify, or store it. Nowadays, the need for advanced data analytics is in high demand due to the ever-up surging real-world big data applications, i.e., these datasets can help estimate behavioral patterns and trends of the market. Applications such as cloud-based web storage enable enterprises to store their data and information and evaluate data to ascertain the market response for a certain company’s product or service is one of the most core roles in big data analytics. At this point, Big Data Analytics plays a vital role in blockchain technology as a large amount of information must be stored (Arora, 2018). This research paper discusses the emerging concepts of blockchain technology and big data analytics.

Blockchain is an effective model for the online storage of data. Additionally, it is a kind of network topology that allows multiple users at a time. Several participants can store transaction details in different blockchain ledgers. The participants are then granted access to the information via a single network with blockchain’s help; tracking the transaction processes becomes easier with this framework. The Application of blockchain technology to store Big Data is more beneficial in terms of costs for its ability to store a hefty amount of data for very long periods. Companies no longer need to incur expenses for other data storage applications, thanks to these technologies. Smart contracts also contribute to reducing the cost due to their ability to perform an automatic transaction.

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Emerging Concepts

Predictive Analytics

The first emerging concept of a blockchain that uses big data is predictive analysis. Predictive analytics refers to a process of using statistics gathered from the collected data to model and determine the future (Phillips, 2019). Blockchains offer a high level of transparency, reliability, and veracity to data. It is easy to realize that it is effective for data analytics to use in cryptocurrencies predictive analysis (Ryan. Kh, 2020). Deploying a predictive analysis approach through blockchain would benefit the involved crypto companies’ investors in a massive way, such as offering intelligent market tools. The main essential goal for traders in need of advanced price forecasts models is to find a way to forecast the future price of a financial product (Ryan. Kh, 2020). However, problems occur when companies lack to collect enough, noting that these analyses need an enormous amount of data. Moreover, many companies and brands cannot afford high-level analytics to forecast even after data collection accurately.

In the blockchain, data is converted into currency. Predictive analytics and blockchain are used to forecast the price fluctuations of crypto-currency from enormous data collected from peer-to-peer networks. Predictive analysis helps companies get clues such as the history of the coin prices and the trading volumes, the variables impacting the coin price, for example, its past demand and the new regulations of the market. It determines the movement of the price, whether it will be long term or short term, the shifting of high or low values as well as the time, the reason or how traders have made past decisions, and whether they will make them again in the future (Phillips, 2019).

In addition to that, data analytics use bulk datasets to retrieve important and accurate information that contains customers’ preferences to offer them the product of their choice. Using this approach, Data analysts can use the data from other sources available in the blockchain network. This attainment from other available sources enables analysts to learn and track various market trends from the prediction made that aid them in making a detailed and critical decision (Sayadi et al., 2018). This also helps them reduce the expenses and the time they would have used when using traditional methods.


The second emerging concept of blockchain is integrity. Data is an important asset that ensures that data is reliable, real, and useful to its users. Normally, users store their data in a cloud storage environment, and the service provides sure it remains as the users stored it (Pengcheng et al., 2020). In the supply chain world, data integrity is not a new concept and for a long time, capturing the right data with integrity is a priority. Although blockchain does not ensure the accuracy of the inserted data on-chain, they ensure protection against modification of fixed data once it is recorded in the shared ledger. Once the data is stored and validated through the consensus process, the blockchains offer protection from any changes since the network users notice them. Blockchains technology creates high levels of traceability and visibility to data so that if the data happened to be stored inaccurately before the regulations needed to store it, it could be traced back to its origin. Within the blockchain, achieving data integrity is normally composed of three requirements: Data origin integrity, Oracle integrity, and digital twin integrity.

Data origin integrity; It is a common belief that data integrity can only be ensured by blockchain. However, though blockchains guarantees that the data stored is not tampered with after it is validated, blockchain will only work on the data that has been stored. If the data happen to be inaccurate, to begin with, then fixing it by storing it in blockchain will not help at all. In other words, in blockchains, it is garbage in garbage out. Hence to guarantee data integrity in the blockchain systems, accuracy and reliability must be ensured from the start (data origin integrity).

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Oracle integrity; It is a common mistake that can occur when submitting data to the blockchain. Since blockchain systems cannot access the information themselves, they must depend on third parties to submit it. Such third parties are known as oracles. Oracles are entities that submit information and also data providers. Oracles are trustable, but it is important to ensure that the data is not altered or omitted before submitting it to the blockchain. Failing to ensure oracle integrity exposes the blockchain system to manipulation and malicious exploitation.

Data-twin-integrity: Real-world objects like digitalized materials and products such as tokens in the blockchain are often presented by both blockchain and supply-chain solutions. The digital presentation is referred to as the digital twin. Its functionality involves ensuring that the object’s real-world data, such as its identity, current location, and other important details, are attached to its digital twin. Useful insights about the state of the object can then be attained and can also update if needed. However, the problem with this design is whether the attached data is as accurate and timely as the presented object or if the connection between the physical and the digital twin object has been tampered with. These two considerations will determine the digital twin integrity; otherwise, its representation to the real world will be inaccurate.

Data Sharing

The third emerging concept is data sharing. This is one of the concepts in blockchain that uses big data. In management data, blockchain transactions allow the involved users to manage their data via public and private domains, allowing them to own it. Third-party intermediaries cannot alter it or even have access to it unless the owner allows it. Additionally, blockchain technologies have been noted to play a vital role in solving problems involved in data sharing. Moreover, blockchains have improved the security and the controllability of data sharing processes after transforming from traditional methods of storing data. Blockchains ensure not only ownership of data transactions but also transparency and credibility. Some of the advantages of blockchain-enabled data include secure and controllable data transmission. Blockchains are desensitized with encryption algorithms like smart contracts and hash processing to ensure the transmitted data remains private, and that security is enhanced by the decentralized data storage method. Also, blockchains ensure that transactions are uniquely determined. Once a transaction is recorded on a block, it cannot be modified; this prevents the recorded information from being copied. Additionally, transaction details about the data can be recorded on the blockchain that matches the establishment of a trusted data asset environment. Unlike other tangible assets, data assets consist of a core factor where data owners cannot be traced back (Jiapeng et al., 2019).

Blockchain uses distributed accounting functions to solve problems that involve sharing, exchange, and data transactions. It enhances data circulation in different industries and protects the legitimate rights of the data owners and their rights. Data can be monetized by data transactions (Jiapeng et al., 2019). Data transactions that are secure, credible, and confidential help break the data island phenomenon that has long been a challenge to the entire industry and help uncover new business ideas, opportunities, and models. For example, in credit industries, data transactions that are secure reinforce data circulation within the credit reporting company. Additionally, the multi-source data integration offers a strong reinforcement for credit bureaus to completely analyze the credit level of industries or individuals (Jiapeng et al. 2019)

Prevention of Malicious Activities

The fourth emerging blockchain concept that uses the big data process is the prevention of malicious activities. In the digital network realm, blockchains have developed into one of the most foolproof methods of data transactions (Legrand, 2020). As modeled and intended, blockchain technology has been approved of its ability to ensure information integrity, and many organizations have benefited from it. Blockchain offers several approaches to cyber-security that can reach far beyond endpoints. They include; user data security, enhanced communication, transactions security, and critical infrastructure supporting an organization’s processes (Legrand, 2020). Some of blockchain cybersecurity functions involve;

 Securing private messages, as the internet continues to transform the world into a vast global village, more and more people continue to join social media, and the number of platforms and applications continues to rise. During these interactions, an enormous amount of data is being collected. To secure their users’ data, most messaging companies choose the blockchain end-to-end encryption method as a priority. Blockchain systems can build a standard security protocol and a unified API framework to enhance cross-messenger communication capabilities. Recently there have been numerous attacks on media platforms such as Twitter and Facebook. They have to lead to the breaching of millions of accounts and exposing users’ info to people with malicious intent. If blockchains can be well implemented, such cyber-attacks can be prevented. Secondly, in the Internet of Things, hackers have been known to use edge devices like routers and thermostats to access the overall systems (Legrand, 2020).

The current AI (Artificial Intelligence) obsession has allowed hackers to gain access even in automated systems. In such cases, blockchains can help ensure security in this system or devices by decentralizing their administration. With this approach, devices are given the capability to enhance security decisions on their own without relying on a central admin. Also, they get more secure since they can detect and act upon suspicious commands from suspicious networks (Legrand, 2020). In most cases, hackers gain automatic access via the central admin of a device and take full control of them. With blockchain, decentralizing the systems and devices make it impossible for hackers to crack them. Other protection approaches include verification of cyber-physical infrastructure, blockchains systems are used to authenticate the cyber-physical infrastructure status, and protecting data transmission by ensuring no data is tampered with during transit, and no unauthorized party can get access to it (Legrand, 2020).

Real-Time Data Analysis

The last emerging concept in the blockchain is real-time data analysis. Real-time data is the information that is delivered instantly after it is collected. It is often used for tracking or navigation. Real-time analytics have enabled organizations or businesses to act without delay, and they can prevent risks before occurring or seize opportunities for improvements. Real-time examples include; Real-time credit scoring, which enables a financial organization to determine whether or not to extend the credit, Customer Relationship Management that optimizes customers’ satisfaction during their interaction with the business outcome; and points of sale fraud detection. With the above examples of real-time data analytics, blockchain’s well-known purpose is to transmit secure transactions and offer a means for financial institutions to mine for the real-time pattern if necessary (Abhinav & Noah, 2017). Additionally, blockchains significantly enhance transparency and credibility in data analytics. Unlike previous approaches, blockchain systems design denies any input that it cannot validate or approve. Rather it deems it as suspicious. Hence, an organization can ensure that the customer’s behavior pattern identified by the blockchain system is whole and accurate (Abhinav & Noah, 2017).


Conclusively, Blockchain technology is a growing concept; considering its functionality, it has improved business and other organizations and made room for potential improvements.  Moreover, we have seen that, as the rate of databases continues to upsurge, more organizations have opted to apply the emerging technology innovation such as data analytics that plays a vital role in blockchain systems. This paper has detailed emerging concepts that use blockchain systems and big data, including predictive analysis, managing data sharing, integrity, real-time analysis, and malicious activities. These are crucial concepts that contribute to improving

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