Can We Trust Big Data? ✓ Solved
CAN WE TRUST BIG DATA? 6 Can we trust big data?
Each business firm adopts IT infrastructure based on its business processes and organizational structure. This case study portrays the importance of big data in business decision-making processes and outlines its significance in business optimization. Big data analytics has recently attracted the attention of both the academic and the business environment, due to the possibility of analyzing the underlying structure of big databases, in which commonly used statistical tools tend to fail (Elgendy & Elragal, 2016).
In this regard, the managers of companies worldwide tend to consider big data analytics tools among the most promising tools in assisting the decision-making process at the company (Sivarajah et al., 2017). Through better data organization, big data analysis enables managers to optimize the efficiency of the company by identifying the crucial variables that affect the company’s performance and market variability (Sivarajah et al., 2017).
Despite its broad acceptance in the past decades, big data analysis still presents multiple critical problems that may hamper its applicability in different real-life scenarios. The most common challenges are data challenges, process challenges, and management challenges. According to Michael Walker, data challenges result from the intrinsic characteristics of data that face Hadoop distributors and the implementation of advanced technology (Sivarajah et al., 2017). Thus, these challenges are related to the immense volume of available data that needs to be sorted out and analyzed. Gantz and Reinsel estimated that the generated information by the end of 2020 would be as high as 40 trillion gigabytes (Gantz & Reinsel, 2012).
Process challenges group the different challenges faced by the big data analyst while processing the data (Sivarajah et al., 2017). These challenges include multiple problems arising from the acquisition and safe storage of data, the grouping, categorization, and integration of different data, the analysis of existing trends, and the validation of developed models. Finally, management challenges result from the need to maintain both the safety and privacy of the gathered data and the evaluation and recognition of data ownership, as well as the lack of skills among most big data analysts (Sivarajah et al., 2017).
The most commonly used big data analysis tools that enhance productivity and increase sales include text mining, audio analytics, and video analytics. The tools used for the extraction and identification of such data will vary depending on whether the information is represented in text, audio, or video messages (Gandomi & Heider, 2015). Text mining focuses on interpreting underlying information contained in text messages, while opinion mining evaluates and categorizes opinions presented regarding specific products. Audio analytics and video analysis tools focus on interpreting information in audio and video formats, respectively, utilizing morpheme identification for deeper analysis (Gandomi & Heider, 2015).
Alternative specific big data analysis tools include social big data analysis and predictive big data analysis. Social big data analysis is used for interpreting data published on social media platforms, while predictive analysis applies statistical tools such as regression to numeric datasets. Big data analysis is instrumental in helping managers gather, interpret, and analyze vast amounts of uncategorized data. Nonetheless, it is vital that analysts select the best analysis tools based on the data type involved. The main challenges analysts encounter involve the volume, variety, speed, and safety necessary for effective data analysis (Gandomi & Heider, 2015).
Paper For Above Instructions
In this reflective exercise, I will discuss the readings and thoughts I developed after studying Chapters 5 and 6 of the Lilly textbook. I believe that these chapters provided a comprehensive overview of the techniques and challenges associated with big data analytics while emphasizing its critical role in decision-making within organizations. The insights shared within these chapters resonated with me, and I feel compelled to express my thoughts regarding the material.
Chapter 5 presented a fascinating exploration of the various challenges that organizations face regarding data acquisition and analysis. I found it incredibly interesting to learn about the sheer scale of data that companies generate and how this can impact decision-making processes significantly. I noticed that the chapter outlined vital aspects such as data quality and the urgency of making timely decisions based on rapidly changing information, which I think are key to the success of any organization today.
One point that particularly struck me was the chapter’s emphasis on the importance of selecting appropriate analysis tools based on the data type. I have observed in my own experiences how inadequate tools can lead to misguided conclusions, which ultimately impacts a company's performance. I believe that organizations need to invest in training their personnel on how to utilize various big data analysis tools effectively to mitigate these issues and enhance productivity.
Furthermore, Chapter 6 expanded on the topic of the ethical implications of big data usage. I felt that this discussion was not only timely but essential, given the increasing reliance on big data to drive business decisions. It made me reflect on the balance between maximizing value through data analytics and maintaining user privacy. I think it is imperative for organizations to assess how they manage data ethically while obtaining insights from the information they collect.
I felt a personal connection to the ethical discussions, as privacy concerns are becoming a prominent aspect of our digital lives today. I believe that customers are increasingly aware of how their data is being used, and organizations must prioritize transparency and informed consent when utilizing big data for analytics. This framework is what I think could lead to sustainable relationships between companies and their customers.
In conclusion, the material presented in Chapters 5 and 6 of the Lilly textbook provided me with valuable insights into big data and the decision-making process within organizations. I believe that understanding both the analytical techniques and the ethical implications are paramount in today's data-driven landscape. I felt that the narrative encouraged me to think critically about how organizations can achieve an effective balance between leveraging data for business success while also maintaining ethical standards. Moving forward, I will carry these reflections with me as I navigate the complex world of big data analytics.