Big Data and Blockchain Final Project Portfolio

Big Data and Blockchain Final Project Portfolio

Big data and blockchain are some of the prominent technologies in the modern era. Big data is basically a large dataset that can be analyzed and evaluated computationally to detect trends, patterns, and associations of various elements of interest. Depending on its storage form, the data can be structured, semi-structured, or unstructured. Generally, any data with high velocity, volume, and variety qualifies to be big data. Blockchain technology is a unique system of documenting information in a stringent, impractical way to hack, change, or overrun the system (Ahram et al., 2017). It is commonly used to develop a permanent transparent ledger system necessary for handling sales and tracking digital payments. The digital ledger is usually duplicated and distributed throughout the whole computer network, making it convenient for numerous individuals to manage it. As documented in this paper, both big data and blockchain technology are used in multiple emerging concepts.

Predictive Analysis

Predictive analysis is the statistical algorithms and data to anticipate the likelihood of various outcomes manifesting in the future. It longs to go beyond what is known and identify the occurrences that may materialize later. This concept has become a central theme in most organizations as it is faster, easier to use, and capable of handling vast types and volumes of data. It is widely used by government organizations and private entities to detect fraud and improve operations. Besides, it is deemed an efficacious tool for marketing campaigns and reducing risks for credit companies. It is imperative to explore how it exploits big data and blockchain technology from a broader perspective.

Predictive analytics is fundamentally grounded in big data technology based on its mode of operation. It essentially establishes meaningful patterns in big data to make informed predictions. Using big data is the best option since it cuts costs, enhances efficiency, and strategically positions a business to recruit the right workforce. Integrating big data in predictive analysis amounts to significant business intelligence necessary to compete in the evolving markets. Companies acknowledge the value of referencing vast data whenever making critical decisions. Notably, big data technology is applicable in various forms of predictive analytics, including descriptive and decision-making modeling (Kvartailnyi, 2021).

The use of blockchain technology in predictive analysis is an irrefutable trend in the contemporary world. It is dominantly referenced in making accurate predictions of price movements for financial markets. This application is more pronounced in cryptocurrencies, especially in bitcoin, where it was first articulated. Besides, businesses are adopting blockchain to foster sales as it increases engagement when predicting potential trends. It makes it possible for firms to adjust prices accordingly to leverage profit margins while maintaining competitive offers.

Real-Time Data Analysis

The current competitiveness in manufacturing, marketing, and sales calls for quick and responsive handling of incoming data. For this reason, real-time data analysis ensures that information is prepared and measured immediately after it enters the database. This approach makes it convenient for a company to react to emerging issues without delay. Nevertheless, it increases business agility and results in exceptional customer experience. It also aids in proactively preventing problems and seizing the arising opportunities. Real-time data analysis embraces big data and blockchain technology to a great extent, as demonstrated below.

Real-time data analysis is oriented to substantially rely on big data as the focal point of reference. Big companies receive voluminous data, which has to be analyzed to foster decision-making. Therefore, real-time data analysis comes in to filter out and capture the critical details necessary and sufficient to acquire consumable insights. This makes it practical to implement the necessary steps without exceeding the allocated period. Combining the two technologies is much more relevant in tech-based companies where clients expect continued interactive sessions. They are also assimilated into financial databases as primary mechanisms of informing trading decisions. As such, real-time data analysis and big data are inseparable.

Blockchain technology has priceless features that leverage the effectiveness of real-time data analysis. For instance, the technology is founded on the idea of consensus, implying that the active nodes in the network are capable of coming to a faster agreement (Pilkington, 2016). As such, they are able to validate transactions within the shortest time possible, availing data to end users on time. Besides, blockchain is primarily decentralized, signaling that a third party does not monitor it. This aspect reduces the time that could otherwise be spent seeking validation. It places users in the front position, giving them the autonomy to make real-time analyses.

Managing Data Sharing

Data sharing is a common concept in the business realm for all companies, regardless of their sizes. It is the aspect of distributing the same set of data resources across numerous users. The major challenge is usually maintaining data fidelity for the data recipients. It is always imperative to maintain the integrity of the data on transit for both internal and external use. Data transfer is a delicate process and takes place daily. As a result, organizations must incorporate the best practices whenever transferring details.

The inception of big data in the management of data sharing creates the need for cloud-based infrastructure. This makes it possible for enterprises to govern and secure information while ensuring it lands in the appropriate destination (Yaqoob et al., 2016). This achievement, in return, inhibits latency and segregation of data insights among external entities and internal departments. Generally, sharing big data makes a company realize its potential in managing vast details. It gets to acknowledge the benefits of investing in cloud-based storage whenever data has to be retrieved and distributed to multiple parties frequently. Data sharing in the cloud is a game-changer that enables a firm to eliminate data silos and increase operations efficiency.

The complexity of data sharing necessitates the implementation of blockchain technology to create seamless transitions. Blockchain is an immutable technology implicating that it cannot be altered once a transaction is complete. This is a priceless feature in data sharing, considering the multiple potential vulnerabilities for data on transit. The technology ascertains that details remain unchanged, whether they are shared internally or externally. In addition, blockchain employs encryption to reinforce information security. Doing so ensures that information will remain uncompromised even if it lands in the unintended hands. Indeed, data sharing is entirely reliant on the competitive characteristics of blockchain.

Smart Contracts

Smart contracts are programs autonomously run when the predetermined conditions are fulfilled. They are typically used to execute agreements such that the engaging parties gain trust from each other. Smart contracts do not require an intermediary and, once completed, cannot be changed. However, they have faced criticism with the allegations that they can run forever if there are no appropriate mechanisms to stop them. They are also prone to malicious actors and bugs but are heavily dependent on big data and blockchain technology.

Smart contracts run big data in various sectors, including healthcare and the supply chain. They use big data in hospitals to promote confidential and secure research, among other events. Smart contracts have private keys that accompany the big encoded data pertaining to healthcare records. This approach ensures that only the authorized parties access and retrieve the contained information. At the same time, smart contracts run on the big data acquired in the supply chain to leverage various operations (Yaqoob et al., 2016). They introduce credible and accessible digital versions of big data used in the chain. Nevertheless, the contracts are invoked in inventory management to handle the vast information. This shows that smart contracts are primarily meant for big data; otherwise, they would not be necessary.

Blockchain is the founding framework under which smart contracts operate. Therefore, the computer protocols are entirely dependent on blockchain for their executions. They are embedded in the blockchain system from where they serve their rightful roles. For this reason, blockchain facilitates faster settlement of the deals at hand between the involved signatories. It accommodates digital signatures used to approve or disapprove contractual terms. The fact that blockchain is fault-tolerant eliminates the chances of contract failure or interruptions.

Augmented Analytics

Augmented analytics is the articulation of enabling technologies to prepare data, generate insights, and explain the outcomes to leverage how individuals analyze and explore data (Luu et al., 2016). It is highly relevant in business intelligence and analytics due to its quest for accuracy. The commonly used technologies to facilitate augmented analytics include machine learning, blockchain, artificial intelligence, and big data analytics. It is in its early development stage as it was first introduced in 2017. Therefore, it has a wide degree of edifying options to explore in the bid to foster its functionality.

Augmented analytics uses big data to accomplish its mission of ensuring that organizational decisions are based on crucial insights obtained from pertinent data. The mechanism is inclined to handle complex and large-scale data, unlike the traditional approaches, which specialize in small chunks of information. It is a great relief to large organizations as they no longer have to hire data scientists for data interpretation. Augmented analytics is a realistic solution and has made it possible for all enterprises to become data-driven.

As already stated, augmented analytics is dependent on blockchain technology to attain various aspirations. Currently, its primary building blocks are artificial intelligence and machine learning. Given that it is a new field, the gradual introduction of blockchain is projected to gain momentum with time. The decentralization and immutability features of blockchain technology will make the analytics more valid and convincing. It will further expand its capacity now that blockchain is supported by multiple computers (Pilkington, 2016). Security issues will also decline due to the protective mechanisms in the node. Generally, blockchain is designed to work in the best interest of augmented analytics.

Conclusion

Big data and blockchain technology are widely used in multiple emerging concepts. They are referenced in predictive analysis to explore possible future outcomes and applied in real-time data analysis to help make quick, informed decisions. Besides, they manage data sharing in an unbiased and credible way in large organizations. The technologies are also applicable in smart contracts to support agreements. Augmented analytics is an emerging field striving to assimilate big data and blockchain technology to overcome the prevalent hurdles. The technologies appear flexible, hence fitting a wide array of emerging innovations. Their use is projected to expand to numerous fields in the future as the need arises.

References

Ahram, T., Sargolzaei, A., Sargolzaei, S., Daniels, J., & Amaba, B. (2017, June). Blockchain

Technology innovations. In 2017 IEEE Technology & Engineering Management Conference (TEMSCON) (pp. 137-141). IEEE.

Kvartailnyi, N. (2021). “Complete Guide to Predictive Analytics and Big Data Analytics.”

Xinoxoft. Retrieved on 3^rd Dec 2021 from: https://inoxoft.com/blog/complete-guide-to-predictive-analytics-and-big-data-analytics/

Luu, L., Chu, D. H., Olickel, H., Saxena, P., & Hobor, A. (2016, October). Making smart

Contracts smarter. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (pp. 254-269).

Pilkington, M. (2016). Blockchain technology: principles and applications. In Research

Handbook on digital transformations. Edward Elgar Publishing.

Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A.

1247. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1247.
      

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The Final Portfolio Project is a comprehensive assessment of what you have learned during this course.
There are several emerging concepts that are using Big Data and Blockchain Technology. Please search the internet and HIGHLIGHT 5 EMERGING CONCEPTS that are exploring the use of BLOCKCHAIN & BIG DATA.
Conclude your paper with a detailed conclusion section.

The paper needs to be approximately 6-8 pages long, including both a title page and a references page (for a total of 8-10 pages). Be sure to use proper APA formatting and citations to avoid plagiarism.
Your paper should meet these requirements:
Be approximately six to eight pages in length, not including the required cover page and reference page.
Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations in addition to your textbook.
Be clearly and well-written, concise, and logical, using excellent grammar and style techniques.