Today, big data and blockchain technologies are commonly used in companies to improve processes. Blockchain is still transforming and operating in various industries while data science harnesses data to ensure proper administration and decentralized ledger to trust the data. Blockchain technology entails a public ledger in which all the transactions taking place in the system get recorded to prevent manipulation. Blockchain technology led to cryptocurrency emergence and transaction recording value. There is a high demand for blockchain technology, whereby most projects today use blockchain applications. However, professionals with blockchain skills have a better technical understanding and have a high demand. Big data collects data from both unstructured and structured data. It entails data analysis, statistics, machine learning, and more data processes.
Additionally, data is an essential resource, which helps businesses in succeeding. Big data applications include recommender services, internet engine protocol, and digital advertisements. For example, data analysis has been efficient in the healthcare industry in improving customer experience, tracking patient treatment, energy management, and more. Therefore, the world has a high demand for data scientists to help solve data problems and deal with large amounts of data that require expert skills. Big data and blockchain involve data whereby in big data, blockchain technology analyses data to be used by blockchain technology deals with data recording and validation. However, both technologies use algorithms that govern their interaction. Integration of big data and blockchain technologies results in various opportunities. Blockchain technology secures and interprets vast amounts of information thus, being a solution to big data analytics challenges. Blockchain technology enforces data integrity, decentralization, and immutability to data, required in big data management and analysis.
Prevention of malicious activities
There are increased cases of malicious activities today, and any resource, property, or asset is vulnerable to attacks. The activities include system and network intrusion, fraud, and identity theft. Fraud attacks benefit the attacker by deceiving people and result in consequences like the loss of property and finances (Maijor, 2021). There are various ways of identifying fraudulent transactions and activities deviating from the required behavior patterns. They include neural networks, decision trees, Bayesian belief networks, genetic algorithms, and support vector machines. For example, the genetic algorithm and neural networks detect credit card fraud in millions of credit card transactions. However, intrusions and hacking compromise data availability, confidentiality, and integrity in individual systems and computer networks. Intrusion detection methods are signature-based, which compares the signatures’ behaviors, and anomaly-based methods, comparing the normal activities for unusual activities (Xu, 2016). Blockchain uses a consensus algorithm in transaction verification, making it hard for a unit to threaten complete data security. Network distribution hinders the altering of the validation criteria in the system by a single party. Several blockchain nodes need to be brought together, making it impossible for the cyberattacks like data access and manipulation.
Blockchain technology impedes record hacking and double-spending activities. Double spending refers to making a couple of payments using the same payment method like bitcoin quantity, which happens between peer-to-peer networks due to delays due to pending payments. Blockchain solves the problem by delegating complex mathematical problems to the miner nodes to verify the transactions. After that, simplifying the system’s computation complexity take the least time possible in solving the problem. Using the blocks with mathematical problem correct answers for recording makes it hard for the individuals to double-spend funds. Centralized management systems and data storage systems are vulnerable to breaches, intrusion, and hacking. The community of miners verifies each transaction, impeding fraudulent transactions. The node networks constantly monitor the blockchain, hence hindering the insertion of fraudulent blocks without getting noticed. Constant monitoring hinders hackers from compromising the blockchain records integrity. However, in case one of the ledgers gets hacked, the other network part backs them up.
Additionally, blockchain technology prevents asset fraud like art by tracking the art financial, trading, and insurance transactions, hence hard to record counterfeit records. Furthermore, smart contracts efficiently ensure that those involved in transactions follow the policies and reduce defaults. Furthermore, the blockchain trustless feature reduces the losses and risks which may occur due to the third-party absence. However, blockchain technology is not immune to all malicious activities, and as technology evolves, organizations and individuals learn how to adapt to it. Therefore, various ways of preventing malicious activities like detection technologies by detecting behavioral patterns, monitoring, and profiling are based on every organization or individual’s transaction histories. Furthermore, blockchain anonymous transactions and cryptographic keys make it vulnerable because once a key gets lost, the identity is also lost on the network. Therefore, the technology should keep track of an individual’s life events like purchasing homes or cars, births, opening bank accounts, and more. Irreversibly recording the details makes it a digital identity and very hard to forge. Lastly, blockchain technology has to be malicious-free to be widely accepted by organizations and individuals.
Data reliability, honesty, and trustworthiness in blockchain technology enable data scientists to perform better big data predictive analysis. The predictive analysis involves modeling and statistics in determining future performances based on the historical and current data. The techniques check the probability of certain data recurring and improve performance based on possible future events. Blockchain data can be analyzed to give details about the trends and behaviors for future outcome prediction by providing both unstructured and structured data from devices (Kh, 2021). Data scientists determine the accuracy of social event outcomes like dynamic prices, customer preferences, customer lifetime value, and more during predictive analysis. The predictions are not limited to investment markers or social sentiments. Blockchain computational power and distribution nature help both large and small businesses in predictive analysis using the available data. Also, data scientists use the numerous computer computational power in a network in analyzing the social outcomes.
Integrating thousands of computers results in strong computational power. It creates a cloud that offers the other computers chance to predictive analytics world via a pay-as-you-use system. The computational power defines other models, and most businesses only have analysts to identify and solve problems. However, the data scientists’ result is to express the data through natural language, which machines cannot process directly (Brooke, 2021). Through natural language processing, the computers select the data sets providing answers by using the existing power. Analytics is crucial in various marketing applications like customer segmentation used to consider sociographic and demographic coordinates and define the market realities. Predictive analytics improves the recommendation systems’ performance by increasing engagement, customer perspective, and sales. Such policies help in increasing sales and also adjust the prices for higher profit margins and competitive offers. Blockchain data analysis focuses on the external and the internal data, like giving average sales estimates through the internal data and giving weather reports through the external data. The logic in predictive analytics is that people and creatures learn and adapt to new changes, thus replicating what they see around them (Castleman, 2021). The same applies to blockchain technology through dealing with vast data amounts. In addition, blockchain technology uses insights from stored data in forecasting future happenings.
Efficient data analytics requires sufficient historical data, which many companies have not been doing for a while. However, even with sufficient data, an expert in the analysis is crucial in making accurate predictions. Unfortunately, many brands do not have in-house expertise. Blockchain technology is commonly used in predicting cryptocurrency price fluctuations. Predictive analytics blockchain predictive analytics is effective because the experts collect data from the peer-to-peer network (Philips, 2021). Many businesses benefit from predictive analytics because they do not have enough data breadth to extract reliable information for forecasting. Predictive analytics clues the finance companies about the market trends like the trading volumes and coin price history, the variables affecting the coin price, whether the coin price is long- or short-term, the coin price fluctuations or how, why, and when the entrepreneurs make past decisions and if the same stimuli are likely to recur. The fifth-party logistics providers can leverage data technology for effective predictive analytics suites, thus reducing the need for in-house data experts. However, expert analysts are valuable in choosing and integrating particular algorithms to give better results.
Managing data sharing
Large organizations and the government collect large amounts of data from people and other small organizations. The data is then stored in systems where it is not useful, and various departments do not have full access to the data because they lack permission. The process wastes time tracking information from one government body to the other. Large and sensitive amounts of data are put in the same location, making it vulnerable to cyberattacks. Governments and institutions can solve the problem by decentralizing the information through blockchain technology (Wei et al., 2021). Blockchain technology solves the problem by decentralizing the information, making it easily accessible to firms and individuals across the network. For example, individuals grant organizations permission to edit or read particular fields and share the information with certain institutions. The institution can have permission to write, as the user controls it.
Every organization takes full advantage of the data because of data sharing demand among data-based companies like Alibaba, Facebook, or Amazon. However, the companies face challenges due to the large amounts of data that each generates every day, thus crucial to establishing an efficient data-sharing model. The traditional ways of sharing data face many challenges due to the advancing technology with increased and advanced cybercrime activities. In addition, they rely on traditional databases, which results in defects when sharing data. The traditional databases got several limitations like insufficient storage, lack of transparency, data can be tempered and go unnoticed, and accessing the data requires a central organization that requires supervision.
Blockchain-enabled data sharing in the government is categorized into comprehensive and basic information. Government information includes social credit, macroeconomic, legal person, population, geospatial and more. Basic information refers to detailed information, while comprehensive information deals with government departments, enterprises, and citizen interactions. The government has a problem with sharing all that information consistently and in real-time among various departments. In addition, government department divisions, reconstruction of the systems, and many departments do not share information with others due to security issues. Blockchain technology provides trusted and secure data sharing channels for cross-departmental data and cross-level connectivity. Blockchain grants various government departments permission to independently allow their accessors, improve law enforcement efficiency, track data breaches, record data call behaviors, and reduce e-government data are sharing security risks.
Blockchain-enable data sharing in energy solves user data security and privacy problems, information security challenges, and achieving real-time schedule control. Also, the technology optimizes the traditional energy transaction ways through data encryption, distributed consensus, equity proof, improving information transparency, and protecting private data. Blockchain technology also applies to the internet of things because the connected device communicates with the other, sharing information. Traditional internet of things devices are vulnerable to attacks. Blockchain consensus mechanism allows node verification, promotes distributed data storage promotes asymmetrical encryption technology, and reduces the chances of attacks. Thus, blockchain technology in the internet of things improves security levels. Blockchain technology also helps secure data sharing in the healthcare industry by synchronizing, preserving, and verifying medical records, which has been a problem. The technology reduces the challenges that the doctors and patients when trying to access their medical information.
Data integrity refers to data trustworthiness and reliability. It involves data maintenance, accuracy, and consistency. All organizations must comply with the state’s data rules and regulations concerning data integrity maintenance. Many businesses in the United States lose a lot of money due to data integrity. Blockchain technology helps control erroneous information and promotes data integrity by ensuring that data remains in the decentralized ledger. Blockchain data is trustworthy because it is verified for quality (Hasib, 2021). Besides, data integrity is when the details of the interacting and the interactions in the blockchain get verified. In addition, it provides transparency because the activities and transactions in the blockchain networks are traceable. Blockchain technology promotes data accuracy, completion, and consistency. Blockchain technology is the best solution for data integrity because once data cannot get deleted or changed once recorded into the system.
Blockchain technology maintains the highest data integrity standards for organizations. It is also a time-keeping mechanism, proofing that data can be reported and updated in seconds. Blockchain technology is also used when organizations have audits to improve data integrity and save money. The technology uses Merkle Tree in ensuring data integrity, which uses the cryptographic hash functions. The component ensures that every block stores data in a tree form, whose parent nodes comprise several child nodes, which recurs until the final node acts as a fingerprint. The significant blockchain feature is immutability and consensus. Consensus refers to the node’s ability to validate transactions and agree on the network’s state. Blockchain technology accomplishes consensus via the consensus algorithm. Immutability refers to the blockchain’s ability to block and prevent confirmed transaction alteration.
Blockchain technology is essential in systems that require both immutability and data integrity. The technology protects the data from attackers, thus reducing data loss chances and potential fraud. Blockchain technology distribution nature hinders cybercrime activities like corrupting the system because there are uncountable data chains. Blockchain technology resembles a decentralized ledger whereby whenever someone wants to alter data, it compares the data changes to the entire chain and prevents any unauthorized changes (Chain, 2021). Identity and access management is granted via blockchain technology because all the transactions get recorded. A blockchain-based identity and access management hinder unauthorized system access, and the trackers cannot erase details of every record accessed. Blockchain technology uses the cryptographic hashing function where algorithms receive any data size as input and give a fixed size and predictable feedback. The input size does not matter because the system gives the same length as output and if the input changes, the outputs also change. The hashes in blockchain technology define the data blocks and the hashes in every block result from the previous block. Therefore, hash identifiers play an essential role in security.
Real-time data analysis
Blockchain technology and big data help businesses in real-time data analytics. The blockchain contains a database for all transactions, which helps institutions in real-time pattern mining. However, extending the technology to other areas such as artificial intelligence, specialized data intelligence forms, and new data analytics. Due to fast data growth, organizations require effective data analytic techniques to help in better decision-making. Blockchain technology plays two significant roles in data analytics whereby data in the blockchain network is an information source, and it enables data analysis for trusted multi-party sharing. The types enable the use of analytic models for effective analysis. Blockchain network generates data, collected for visualizing and analyzing both current and historical system behavior. Besides, the system operators need analysis and visualization to understand the system transactions and the network layer. Data visualization is crucial in illustrating the system behavior because blockchain nodes are dynamic and connect and disconnect over time.
Big data require visuals to summarize the vast amounts of data, hence making blockchain understanding easier. Other data generated by the blockchain ecosystem include market and price cryptocurrency market share, reflecting the blockchain’s economic value. The data is essential for developers to make the right decision in choosing the appropriate platforms and analyzing the blockchain-based application costs (Lee et al., 2021). In addition, the information is crucial to crypto-investors to help them detect fraud, market manipulation behaviors, and understand market frauds. Therefore, online tools visualize various blockchain aspects like the Ethernodes and the Bitnodes. For example, they visualize bitcoin and ethereum networks. However, the traditional techniques did not link the transactions, thus challenging the data analysis in real-time. On the other hand, the immutable blockchain data is accessible to the public and can be used in analyzing the system behavior, and the data is extracted from different sources. Furthermore, blockchain data analysis utilizes the in-memory database, making it very fast than the tools in the databases.
Blockchain data analysis differs due to organizational interests. For example, some organizations focus on data privacy while others want to discover the historical transaction’s statistical properties. However, most organizations combine data source arrays with blockchain data. Blockchain-enabled data analytics got suitable features for analyzing distributed data. Blockchain technology is a neutral and secure platform for businesses with distributed systems. However, its anonymity allows users to contribute to data analysis without losing their privacy. Blockchain immutability secures data and its integrity, thus creating trustworthiness during the data analysis process. In addition, blockchain enables provenance and logging and gives users a collaborative platform. Data provenance is the historical data records and their entities and the processes influencing them. The record shows the processed and accessed data, the person who accessed and the reasons for accessing it. Therefore, blockchain is efficient in storing records as transacted data and utilizing the computational capacity maximally. Furthermore, blockchain decentralization permits people to control their data to know the data being collected and how it is accessed. Lastly, the analytic models show the users the ways of updating the tools.
To sum up, big data and blockchains are commonly used in large companies to secure data and improve customer satisfaction. Most businesses are customer-centered and try to ensure that their customers are satisfied. Billions of people worldwide use the internet, meaning that they have confidential data on the internet, requiring security. A lot of information is being generated by companies every day, requiring analysis to make it meaningful and resourceful. For example, the collected data is unstructured and requires analysis to make sense and safe storage. Big data involves vast amounts of data that require analysts and experts to keep the data safe from hackers. Blockchain technology offers data safety through various features that hinder cybercriminal activities. Both big data and blockchain technology are involved in data recording and validation. Blockchain decentralization is the main feature that protects data from hackers, and if one sector is manipulated, the whole chain out power the counterfeited data. Blockchain technology secures data through integrity, decentralization, and immutability. As technology advances, cybercriminal activities are increasing and inventing new ways of hacking data. Therefore, data require expert skills and a patched system from time to time to avoid making the data vulnerable. Blockchain technology prevents data from malicious activities through various algorithms such as neural networks, decision trees, Bayesian belief networks, and genetic algorithm and support vector machines, thus preventing intrusion and hacking. The blockchain node networks monitor the chain, thus making it hard for hackers to insert blocks. Predictive analysis improves data reliability, integrity, and trustworthiness. Blockchain technology enforces security on data in motion and helps in real-time data analysis.