Big Data Analytics

Big Data Analytics

Introduction

The inception of big data analytics has transformed the face of the world by harnessing organizational data to make smarter business moves. Its application spans various fields, such as government to make informed decisions, marketing to develop target ads, and the medical field to create new cures. However, the most significant milestone involves the application of big data analytics in the Internet of Things (IoT). A combination of the two disruptive technologies enhances the production process and streamlines the supply chain. Evaluating the benefits and challenges of applying big data in manufacturing IoT can help determine their relationship from a broader view. Our assignment writing help is at affordable prices to students of all academic levels and academic disciplines.

Benefits of Big Data Analytics for Manufacturing IoT

Big data analytics plays a key role in providing real-time forecasting and monitoring, which is essential in the production field (Rabah, 2018). Most IoTs such as wearable health monitors, biometric cybersecurity scanners, and intelligent factory equipment keeps evolving, implying that the manufacturers must remain informed about market trends. This gap is perfectly filled by big data analytics due to its prowess to assess voluminous relevant data with ease. As a result, a company manages to gain a competitive edge by releasing up to data items.

Blending big data analytics into IoT manufacturing promotes innovation to a great extent. A recent study indicates that 68% of innovation in most companies is inspired by big data analytics (Ahmed et al., 2017). It has introduced data-driven business lines that are more compelling than previous means of operation. The massive data analysis reveals new designs and models that can suitably address the prevalent needs in the market.

The effectiveness of big data analytics is crucial in reducing machine downtime. Hardware malfunction is an extreme work-related killer in the manufacture of IoT and other industries. It renders employees idle and calls for urgent intervention by the engineers. However, this crisis has been solved through predictive maintenance installed in machines (Rabah, 2018). It is now possible to prevent unnecessary crashes before they occur. In fact, the results of big data enable machines to self-diagnose and shut down without human intervention.

The customer experience has further been leveraged as their feedback is readily examined and addressed. Unique mechanisms, including standard deviations, moving averages, clustering, and distribution histograms, are used to prioritize data gathering and assessment (Shah, 2016). Data visualization ensures that the key issues are identified rather than generalizing all details. This is a critical achievement in IoT as the tailored users can now enjoy gadgets that fit their needs and expectations.

The correlation of products and performance across multiple manufacturing companies is a critical task that has been revolutionized by big data analytics. The traditional benchmarking was relatively tedious and time-consuming; hence it has been eliminated. Organizations can now make decisions based on a broader perspective rather than using minimized data. They can readily identify areas that require improvement based on competitors’ reports.

The capability to analyze structured and unstructured data mitigates risks of unforeseen threats (Rabah, 2018). Several home safety products, such as pressure cookers, smoke alarms, and thermostats, have been recalled from the market for various reasons. This is an adverse situation that deprives the company of ROI and profits. However, it can be resolved through big data analytics by gauging products based on previous versions before releasing them to the market. However, IoT companies have gradually begun to appreciate the role of big data.

Challenges of Big Data Analytics for Manufacturing IoT

Despite its robustness and prowess to solve multiple challenges in IoT manufacturing, big data analytics has numerous limitations. For instance, data visualization might be challenging given that the amassed data is heterogeneous, semi-structured, or even unstructured (Mourtzis, Vlachou & Milas, 2016). The varied formats make it inconvenient to devise a graphical representation of the information at hand. Visual noise requires extra attention before the collected data can be processed and made ready for IoT manufacture.

Analysis depth is yet another challenging task when dealing with voluminous and variable data (Bi et al., 2021). It may be confusing to determine to what extent a dataset must be analyzed to capture more value. At the same time, drawing quick analyses from massive amounts of data is relatively inconvenient, especially when pressured by top management. Given that there are more than 12.3 billion IoTs worldwide, the related information is equally large; hence, the depth of analysis remains a primary concern.

Data storage and management in the context of big data analytics has further sparked inconveniences in the manufacture of IoT. The enormous data volume requires an elaborate infrastructure that incurs high costs. Besides, there are possible data accessibility problems, especially in IoT firms with unclear data management protocols. Nevertheless, compatibility, accessibility, data corruption, and the appropriate scaling remain principal obstacles.

Integration issues also hinder the effective use of big data analytics as information is obtained from different sources (Shah, 2016). Combining data from financial reports, employee reports, clients’ logs, and emails is too demanding. This challenge increases in decentralized systems and can potentially limit essential insights.

Data security is a prevalent challenge and can deteriorate a company’s reputation and lose customers’ trust once mishandled. Apparently, some firms concentrate on analyzing and understating data sets and thus end up marginalizing security concerns. This is an unprofessional approach and opens up ways for cybercriminals to perpetrate their malicious deals. The intrusion of data devices and centers incapacitates the effective and reliable manufacture of IoT.

Choosing the most efficient data selection tools has also become a fundamental concern in big data analytics. IoT companies are unable to settle on the best-fit instruments for handling giant data. This dilemma leads to poor choices that finally frustrates the companies. Eventually, they waste time, money, work hours, and effort that could otherwise be spent on other productive activities. This challenge is more evident in startups as they have minima experience.

Finally, a lack of knowledge of professionals and inadequate comprehension of big data are daunting issues in IoT entities (Mourtzis et al., 2016). Running big data analytics and the related systems require exceptional expertise from data analysts, data scientists, and data engineers, who are absent in most companies. Besides, data management tools keep evolving, but the experts stagnate with the basic skills. Therefore, there is a lack of competent specialists in collecting, processing, and implementing the critical data.

Conclusion

The application of big data analytics in IoT is a significant breakthrough in the 21st Century. It has simplified the way of doing things and improved the productivity of tech-based companies. The acquired benefits are pivotal strengths towards the growth and full realization of IoT products. However, the associated drawbacks are equally felt and incapacitate some firms from exploiting technology in data collection and analysis. The future of IoT relies on unlocking the potential of data-driven manufacturing.

References

Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. 471. (2017). The role of big data analytics in Internet of Things. Computer Networks, 129, 459-471.

Bi, Z., Jin, Y., Maropoulos, P., Zhang, W. J., & Wang, L. (2021). Internet of Things (IoT) and big data analytics (BDA) for digital manufacturing (DM). International Journal of Production Research, 1-18.

Mourtzis, D., Vlachou, E., & Milas, N. J. P. C. (2016). Industrial big data as a result of IoT adoption in manufacturing. Procedia cirp, 55, 290-295.

Rabah, K. (2018). Convergence of AI, IoT, big data and blockchain: a review. The Lake Institute Journal, 1(1), 1-18.

Shah, M. (2016). Big data and the internet of things. In Big data analysis: New algorithms for a new society (pp. 207-237). Springer, Cham.

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The recent advances in information and communication technology (ICT) has promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can extract huge business values while it can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data.
For this assignment, you are required to research the benefits and challenges associated with Big Data Analytics for Manufacturing Internet of Things.