1 Discussion Case Study Paper and PPT
BIG DATA ANALYTICS 1
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The topic is rather interesting but I think the reader would like examples. You mentioned that "many enterprises are keen on developing new business models to discover, analyze, navigate, and efficiently manage mission-critical big data", which enterprises? How will it impact their business specifically. Maybe you can include a case study to validate your claim.
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What is your closing comment on Big Data Analytics? Is it a step in teh right direction? Is it something that must be done? Do the benefits far outweigh the negatives of it?
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“…analytics to help them improve on organizational performance as they keep with the trends within the market niche…”
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Big Data Analytics
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Big Data Analytics
Big data analytics is the utilization of advanced analytic techniques against diverse and large sets of data that include unstructured, structured, and semi-structured data from different sources. The same data can also be in different sizes and that is from terabytes to zeta-bytes (Wei-Dong et al., 2014). In that case, big data analytics can be a process for extracting meaningful insights like market trends, unknown correlations, customer preferences, and hidden patterns. Similarly, big data is known as the flood of digital data from several digital earth sources such as digitizers, sensors, numerical modeling, scanners, social networks, e-mails, mobile phones, videos, and the internet. Big data analytics also provides several advantages, and therefore, it can be used for better decision-making process and prevention of fraudulent activities as well. In other words, big data describes a large volume of data – both unstructured and structured to inundates daily business operations. However, not the amount of data is considered important (Wei-Dong et al., 2014). Big data can also be analyzed for insights that will help with strategic moves in business. The evolution of big data, especially adoption by governments and industries expands on its meaning and content. The original volume-based definition of big data consists of data itself, expertise to help with the generation, collection, management, storage, analysis, processing, and relevant technologies as well. Therefore, in this case, study, it will be imperative to discuss the benefits, features, challenges, and risks that are associated with big data analytics.
Benefits and Features of Big Data Analytics
Currently, almost everybody has heard about big data analytics and the wave it has created in the business world (Wei-Dong et al., 2014). Every piece of information about the benefits and features of big data analytics is always in the media. For that reason, many companies are leveraging big data analytics to promote decision-making processes that are data-driven (Chaowei Yang et al., 2016). Nowadays, the benefits of Big Data Analytics are also witnessed in the manufacturing, health, education, supply chain management, and retail industry. Therefore, almost every organization and enterprise, whether big or small is already leveraging on the benefits of big data analytics. Using big data analytics has enabled them high the right employees (Wei-Dong et al., 2014). [Prepositions are function words that indicate how a noun or noun phrase relates to the rest of the sentence. ]The right employees here are workers with the required talents and skills for specific job descriptions (Chaowei Yang et al., 2016). In that case, recruiting companies can search the resumes and profiles of the potential candidates through big data analytics. Such companies only need to use keywords that match the described job description. The recruitment process is no longer based on how a person is perceived and how the candidate looks like on the paper.
Moreover, big data analytics is known for cost optimization, and the common tools that help with it include Spark and Hadoop. These tools offer cost advantages to businesses when it comes to analyzing, processing, and storing large amounts of data. Big data tools can also help a business organization identify cost-savvy and efficient ways of doing business. An excellent example is a logistic industry – the sector highlight the cost-reduction benefits promoted by big data analytics (Chaowei Yang et al., 2016). [An article is a short word like a or the that goes before a noun to make it clear what the noun refers to. ]With big data analytics, the cost of product returns can be 1.5 times greater than the actual shipping costs. For that reason, big data analytics allow companies to minimize the product return costs by predicting the possibility of product returns. [Using the indicates that you’re talking about something specific or familiar. Don’t use the with generic or abstract ideas ]In the process, companies can estimate the products that are most likely to be returned as they take suitable measures for reducing losses on returns.
Adoption of big data analytics by companies can improve efficiency since most of the tools improve operational efficiency by bounds and leaps. Therefore, by interacting with clients and receiving valuable feedback from them, big data tools can help with the organization of the large amounts of important data received from customers (Chaowei Yang et al., 2016). The collected data can then be analyzed and interpreted to extract meaningful patterns hidden within buying behaviors, pain points, and customer preferences and tastes. The analysis can also allow companies to create personalized services and products, depending on the feedback received from customers (Chaowei Yang et al., 2016). Big data analytics can also analyze and identify the latest market trends and allow companies to keep pace with the competition in the market. The advantage of using big data tools for analysis is that they can automate routine tasks and processes. The automation of processes frees up the valuable time of human workers that they can devote to other tasks that need cognitive skills.
Presently, one of the business drivers is big data analytics because many enterprises are keen on developing new business models to discover, analyze, navigate, and efficiently manage mission-critical big data. They are seeking to harness their organization's big data to help them improve productivity, employee morale, and understanding of customers’ behaviors and customer preferences. Ideally, the primary aim is to improve service delivery while maintaining customer loyalty. Business organizations are also in need of big data analytics to help them improve on organizational performance as they keep with the trends within the market niche. Another important key driver for big data analytics is IT and optimization of business operation. Such areas are optimized by developing a big data strategy that utilizes existing enterprise investments, including applications and data. In that case, a well-integrated data architecture will be required to support advanced analytics platforms. Even though it is a big challenge to incorporate various types of data sources across the organization, the process remains a necessity. However, the incorporation can be achieved, mostly in phases and with help of available software and tools. Therefore, an excellent strategy incorporated with a well-integrated collection of data will always support a robust foundation for big data analytics.
Big data analytics foster competitive pricing as it facilitates real-time monitoring of market trends and competition. With big data analytics, it is possible to keep track of the past actions taken by competitors and the current business strategies adopted by them. For that reason, big data analytics offers real-time insights, which can allow an organization to calculate and measure the business impacts brought by price changes. Through competitive pricing, business organizations can implement competitive positioning that will help them with the maximization of company profits (Chaowei Yang et al., 2016). Besides, it is the responsibility of every business organization to evaluate their finances to get a clear understanding of their financial position. Big data analytics also initiate the implementation of pricing strategies based on customer purchasing behavior, competitive market patterns, and local customer demands. Through big data analytics, it is possible to automate the pricing process for the business to eliminate manual errors and maintain price consistency as well.
Big data analytics monitor and control online reputation for many businesses. Since most companies are increasingly shifting towards the online domain, it is becoming crucial for them to monitor, improve, and check on their online reputation. What customers say about the company on various social media and other online platforms can greatly affect the perception of potential customers (Chaowei Yang et al., 2016). Again, several big data tools are unequivocally designed for soppiness analysis. Such tools can help the management to surf through the vast online sphere and find out what people are saying about the products and services. In that case, an organization can improve its services by only understand the grievances of its customers, and thus, improving the online reputation.
Challenges and Risks of Big Data Analytics
While looking into the life cycle challenges associated with traditional data, big data analytics poses technological challenges because of the 5V features in several different sectors of sciences, government, and industry (Chaowei Yang et al., 2016). The common challenges and risks associated with big data analytics include data storage, data architecture, data transmission, data management, data visualization, data processing, data analysis, data integration, privacy challenges, data security, and data quality (Bahrami & Singhal, 2015). The storage risks or challenges are caused by the volume, variety, and velocity of big data (Bahrami & Singhal, 2015). For instance, storing big data on traditional storage systems such as HDDs always fail. Again, traditional data protection mechanisms like RAID are considered not to be effective with PB-scale storage (Chaowei Yang et al., 2016).
Additionally, big data velocity will require storage systems to be scaled up quickly, but this is difficult to be achieved with traditional storage systems. Fortunately, cloud storage services such as EBS and Amazon S3 provide virtually unlimited storage capacity with high provide fault tolerance (Alberto et al., 2019). Such storage services provide potential solutions for addressing the challenges that come with data storage. However, hosting and transferring big data on the cloud is expensive and the process depends on the volume and size of the big data (Bahrami & Singhal, 2015).
The life cycle of data transmission proceeds includes data collection, data integration, data management, and data analysis (Alberto et al., 2019). In that case, transferring large volumes of data will obviously pose challenges in the mentioned stages. Perhaps, the challenges might be solved by using smart pre-processing techniques ad compression algorithms for big data as they are reducing the size before transmission (Alberto et al., 2019). Also, it is difficult for computers to efficiently analyze, visualize, and manage big, heterogeneous, and unstructured data (Bahrami & Singhal, 2015). The veracity and variety of Big Data are defining the paradigm of data management, and therefore, demand for new technologies (Alberto et al., 2019). Such technologies will help with the cleaning, storage, and organization of unstructured data.
Furthermore, processing a big volume of data requires dedicated resources and this also includes network, storage, and increasing speed of CPU (Bahrami & Singhal, 2015). Nonetheless, the computing resources needed for the big data process exceed the processing power offered by commuting paradigms of traditional types (Alberto et al., 2019). Cloud computing also offers on-demand and virtually unlimited process power as the necessary partial solution (Alberto et al., 2019). Nevertheless, shifting towards the cloud ushers in new issues. The first issue is the limitation of network bandwidth of cloud computing, and this can impact the computation efficiency over a large volume of data (Alberto et al., 2019).
In conclusion, many advancements have been made in big data analytics because of the benefits it brought to the business environment. Many advancements have been possible in terms of big data storage, processing, analysis, and retrieval. Business organizations can enjoy the benefits of big data analytics if they are in a good position to solve the associated risks and challenges. Therefore, attention should always be given to analytics challenges, management issues, privacy, data validation, and data transmission.
References
Alberto, E., J., Stephen, K., Frank, A., & William, H., M. (2019). Big Data Redux: New Issues and Challenges Moving Forward. Retrieved from: https://scholarspace.manoa.hawaii.edu/bitstream/10125/59546/0106.pdf
Bahrami, M., & Singhal, M. (2015). The Role of Cloud Computing Architecture in Big Data. Retrieved from: https://cloudlab.ucmerced.edu/sites/cloudlab.ucmerced.edu/files/documents/mehdi_bahrami_et_al._the_role_of_cloud_computing_architectures_in_big_data.pdf
Chaowei Yang, Qunying Huang, Zhenlong Li, Kai, Liu & Fei Hu. (2016). Big Data and cloud computing: innovation opportunities and challenges. Retrieved from: https://www.tandfonline.com/doi/full/10.1080/17538947.2016.1239771
Wei-Dong, Zhu Manav, Gupta Ven Kumar, Sujatha Perepa, Arvind Sathi, & Craig Statchuk. (2014). Building Big Data and Analytics Solutions in the Cloud. Retrieved from: http://www.redbooks.ibm.com/redpapers/pdfs/redp5085.pdf