Introduction
In the contemporary, the streaming process is ideal for data streams. Streaming is planned to analyze and also act on real-time streaming data. Stream analytics denotes the process through which data is analyzed and processed immediately as it is produced instead of storing the data for bunch processing. Stream analytics emphasizes the constant data flow. Allowing companies to attain insights, notice patterns and make crucial decisions at that time. As a result of harnessing information, organizations can benefit by making informed decisions, gaining competitive advantage as well as enhancing customer experiences. In addition, stream analytics is a powerful tool that enables businesses to analyze and derive actionable perceptions from constant data streams. For this reason, there is a need to explore the notions of stream analytics, applications, benefits and importance in various organizations as well as how analytics systems can facilitate activities such as empowerment, mass customization, and teamwork.
According to Ullah & Narain (2020), there is significance in investigating the impacts of flexible manufacturing competence (FMC) and workforce management practices (WMP) to facilitate the advancement of mass customization capability (MCC). In the current evolving world, organizations face massive pressure in order to satisfy clients looking for distribution of individually tailored commodities at affordable prices in the shortest time possible. Thus, firms are shifting emphasis towards embracing clients to understand better or pre-empt their necessities or choosing mass customization (MC).as the strategy to reach a competitive edge. As a result, the advancement of mass customization abilities has become the core management concern. Recognition of various means that enhance company flexibility is vital for accomplishing MC.
Tang et al. (2017) argue that there is a need to study the effect of product modularity(PM) on the mass customization capability (MCC), which is moderated by numerous contextual elements such as teamwork. Data collected from various states showed that product modularity impacts MCC strongly. The findings show that organizations which desire t be successful in mass customizers must create flats and be nimble with workers who are well-trained in various tasks or encourage teamwork.
Li et al. (2021) state that Human-robot collaboration in the current world has attracted much interest from researchers for the improved flexibility or efficiency of mass personalization. But HRD advancement undertakes a human or robot-centred manner where operations are done by following certain instructions.
Ethical Issues
The rapid development of stream analytics is associated with many ethical issues. The first ethical issue is that stream analytics is data privacy. In systematic analytics, organizations gather much personal or sensitive customer information. As a result, concerns are raised about how personal information is collected, used or shared. In some scenarios, customers may not be aware of how their information is being used. So, there is a need for transparency and getting informed consent to ensure people have control over their information.
Besides, stream analytics comprises processing data which can include sensitive personal information. The process has raised concerns about data security or a possible breach of personal information. Organizations should implement security measure that protects data from hackers. In addition, algorithms or models used in systematic analysis might make for management to explain how a particular decision was arrived at. So, there is a lack of transparency and accountability, which is vital to ensure customers can understand decisions, are impacted by these decisions, or challenge them whenever necessary.
Applications and Benefits of Stream Analytics
There are various applications of on-application stream analytics. According to Ahmad et al. (2017), the recognition of irregularities in real-time streaming data shows significant applications in many industries, such as in the cases of preventative maintenance, prevention of fraud, fault detection, and monitoring various industries departments such as finance, information and technology, security, medical, social media, agriculture, energy and e-commerce. In addition, detection anomalies help in giving actionable information in crucial scenarios. However, reliable solutions are not existing.
In addition, there are many benefits associated with stream analytics. First of all, organizations can make positive decisions based on up-to-date insights promoting quickness and operational efficiency. Besides, real-time analytics enables organizations to identify emerging trends and capitalise on the opportunities before their competitors. Stream analytics also is beneficial as it enhances clients’ experiences through personalizing endorsements and interactions. The technology used in systematic analytics helps in predictive maintenance, dropping downtime as well as operational cost through the identification of equipment and tools failures before their occurrence.
There are many examples of how analytics systems can facilitate empowerment, mass customization, and teamwork. For instance, an analytic system is used in the retail industry. According to research, stream analytics is used in the retail industry. For example, stream analytics is applied, especially in supermarkets. System analytics integrates data from the point of sale, inventory database, monitoring sales, stocks and customers. Besides, the insights help retailers to maximize inventory, minimize shortages, and regulate pricing strategies. Consequently, supermarkets report increased sales and substantial improvement in client satisfaction. Thus, location tracking-based clustering offers the possibility of personalized services in the retail industry.
However, there are challenges for privacy and considerations. Stream analytics require robust infrastructure as well as specialized tools that can handle high-speed data streams effectively. Establishing robust infrastructure might be expensive to some organizations because it could require huge capital. So, some organizations might fail to invest in stream analytics due to high costs. In addition, guaranteeing data privacy or confidentiality is essential, particularly when dealing with highly sensitive customer information. Ensuring the guarantee of privacy and confidentiality of clients’ information also makes the system more costly. Additionally, system analytics should consider data quality matters because inaccuracies might lead to poor decision-making.
How analytics systems can facilitate empowerment, mass customization, and teamwork.
Stream analytics is crucial in facilitating empowerment by empowering people in organizations by providing accessible data. Through this data, individuals from various departments can make informed decisions that contribute to the organizational success based on the system analytics information. Besides, stream analytics enables mass customization by allowing organizations to manufacture products that suit customers’ tastes and preferences. Through the provision of quality products that suit customers’ satisfaction, these organizations have enjoyed loyalty from their clients and have been able to have a competitive advantage.
In addition, stream analytics promotes teamwork. Helpe in promoting teamwork as well as collaboration in organizations. Data is sharing in various departments and levels. Also, there is a collaboration of analytic tools by employees that enhances effective communication and quality decision-making among the workers. The Orange data mining is significant as it is used in experienced clients or stream analysis in data mining or machine learning requiring to make or test-own algorithms while also using codes as applicable.
Conclusion
There are many benefits and challenges associated with stream analytics. Organizations achieve the aptitude to make positive decisions which are founded on updated insights. As a result, it promotes swiftness and operational efficiency. Analytics enables firms to identify emerging trends. Stream analytics also is useful as it enhances clients’ experiences through personalizing authorizations and interactions. Systematic analytics technology helps in predictive maintenance, dropping downtime as well as operational cost through the identification of equipment and tools failures before their occurrence. However, there are challenges for privacy. Stream analytics necessitate robust infrastructure as well as specific tools that handle high-speed data streams. Establishing robust infrastructure might be expensive for some organizations.
References
Li, S., Wang, R., Zheng, P., & Wang, L. (2021). Towards proactive human–robot collaboration: A foreseeable cognitive manufacturing paradigm. Journal of Manufacturing Systems, 60, 547-552. doi.org/10.1016/j.jmsy.2021.07.017
Tang, M., Qi, Y., & Zhang, M. (2017). Impact of product modularity on mass customization capability: An exploratory study of contextual factors. International Journal of Information Technology & Decision Making, 16(04), 939-959.
Ullah, I., & Narain, R. (2021). Achieving mass customization capability: the roles of flexible manufacturing competence and workforce management practices. Journal of Advances in Management Research, 18(2), 273-296.
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