Discuss about the Significance of Big Data for Literature Review Paper.
A world without data storage is imaginable. Such a world means that the details of the organization or a person, every detail of a storable document, or every performed transaction is lost directly after its use (Nada Elgendy, 2014). Firms would therefore lose the ability to conduct a detailed analysis, access valuable knowledge and information, as well as providing new advantages and opportunities. Any information has become very crucial for the day-to-day operations. For any organization to thrive, they have to use data as the building block.
With an increase in technological advancement especially the internet, there is a huge surge of information and data. Enormous amount of data is easily available with the increase of data collection methodology and storage capabilities (Nada Elgendy, 2014). Every minute, there a lot of data that is being created. For this data to make any sense, it should be stored and analyzed. This study analyzes the available literature on the significance of big data.
The term big data is used to refer to the large database that cannot be stored and analyzed by the traditional methods of data management (Tulasi, 2013). The size of the big data is so huge that the commonly used storage system and software tools are incapable of capturing, storing, managing, as well as processing all the data within the required time.
Currently the sizes of big data are increasing continuously to many petabytes from a few terabyte dozens in one data set. visualizing, searching, capturing, analytics, and storing are some of the challenges related to big data (Wagner, 2014). Organizations are exploring huge volumes of detailed data so as to extract information that they are do not understand. Therefore, big data analytics involves application of analytic techniques that are highly advanced on sets of big data. Large data sample analytics reveals and influence change in businesses. Nonetheless, it becomes more difficult to manage larger data sets.
From the perspective of a decision maker, the importance of big data is in its potential to give valuable information and knowledge on which decisions can be based (Partners, 2012). Throughout the years, the topic and significance of the process of managerial decision making has been covered thoroughly in different researches. Organizations benefit significantly from the highly detailed big data from varied sources such as loyalty cards, social network, scanners, mobile phones, and websites. However, this can only be achieved if the data is analyzed properly to reveal insights that are of value, which can be capitalized by the decision makers to access the opportunities presented from the wealth of real-time and historic data created through customer behaviors, processes of production, and supply chains.
Moreover, companies are currently used to examining internal data. Some of the internal data include inventory, shipments, and sales (Elena Geanina, 2012). Nonetheless, there has been a rise in the need for external data analysis. Some of the external data required by the different organizations include supply chains and customer markets. The application of big data can provide knowledge and cumulated value. With the increasing types and sizes of data that is unstructured, it has become essential to base on meaningful data inferences to make more informed decisions.
Similarly, (Nada Elgendy, 2014)developed a framework known as B-DAD which maps techniques and tools of big data into the process of decision making. The intention of those frameworks is the enhancement of the process of decision making in respect to dealing with huge data. Intelligence phase is the first phase of the process of decision making. At this phase data is collected from both external and internal sources to be used in the identification of opportunities and problems.
First, there should be an identification of data sources (Rasmus Wegener, 2013). The data identified should be collected from different sources, analyzed, and stored before being migrated to the final user. The acquired data is stored in the big data management systems (Magnusson, 2012). After the storage, the data is processed after it has been organized and prepared. This is accomplished using huge data processing tools or by a high-speed network such as ELT.
The process of decision making which is the phase of designing is the next phase. At this phase, there is development and analysis through conceptualization of all possible actions. This phase is divided into three steps by the B-DAD framework. The three steps are data analytics, analyzing, and model planning. At this stage, different data analytics’ models are selected, planned, applied, and analyzed (Nada Elgendy, 2014). Consequently, the process of decision making is followed by the choice phase. At this phase, various methods are applied to evaluate the effects of the suggested course of actions or solutions from the design step. Implementation phase is the last stage of the process of decision making. At this stage, the previous phase suggested solutions are implemented.
Different companies across various sectors are becoming interested in how to analyze and manage big data, as its amount continues to grow exponentially (Jacquiline, 2012). At such, they are making efforts to get the best insights presented by the big data opportunities. These organizations are implementing the big data analytics in order to make faster and better decisions that will result in economic gains (Sudhakar, 2015). At such, they are adopting big data analytics so as to analyze the big amount of data quicker, and expose the customer intelligence, sentiments, and patterns that were unseen previously. This section will analyze different proposed and implemented applications of big data, and how they can help businesses across various industries to enhance decision making and gain valuable insights.
Big data helps organizations to enhance the existing new services and products, design new ones, and invent completely new business model. These benefits are gained when the entities apply big data analytics in different business areas such as performance, fraud detection, customer intelligence, quality and risk management, and supply chain intelligence. (Nicholas Graves, 2011)lists the main sectors that can gain from the big data analytics. Examples of those sectors include healthcare, banking, central government, manufacturing, retail, and telecom industries.
Big data analytics possess huge customer intelligence ability. It is important to industries like telecommunications, banking and retail. Big data creates openness and makes important data accessible to shareholders in time. Also, it offers companies the ability to segment and profile its clients according to various socioeconomic characteristics and increases the customer retention and satisfaction levels (Peersman, 2014). The analysis of Big data allows customers to come up with marketing decisions that are in line with their preferences as well as recognize marketing and sales opportunities. Additionally, social media application can be used in informing the organizations about their customer’s preference. Through, the analysis of sentiment on big data, companies can be notified if their clients are turning or shifting to other products and enabling the company to take necessary actions.
By using Social Network Analysis to examine customer sentiments concerning brands and identification of influential person, organizations can be able to carry out direct marketing and react according to the emerging trends (Lieberman, 2014). Big data analytics also enables the formulation of customer behavior models and purchasing patterns hence, raising revenues. They also benefit companies through the creation of better market targets, prediction and definition of market sentiments as well as the analysis and understanding of market mix and various customer behaviors.
Big data analytics can be used in supply chain management to forecast changes in demand, and match their supply appropriately. This is of high significance for transport retail, logistic, and manufacturing industries (Dupre, 2013). Through the analysis of geospatial data on deliveries and stock utilization, companies make decision making automatic, which will reduce process interruption, lead times, and minimize delays and costs. In addition, by basing on price or quality competitiveness of suppliers, the decisions on changing them can be easily taken by monitoring and analyzing their performance. Also, alternative pricing situations can be made automatic, which will facilitate an increase in profit margins and inventories reduction. Furthermore, big data can lead to better forecasting and planning by identifying the root causes of costs.
Big data is also very can be of high significance in the management of performance. The healthcare and governmental sectors can benefit easily from this performance management. With the increasing need to improve staff performance a and productivity information can be forecasted and monitored by applying tools used for predictive analysis (Nada Elgendy, 2014). This enables different departments to increase efficiency by link their user outcome or service with the strategic objectives. In addition, with the easily accessible performance information and big data, the organizational managers can adopt the application of dashboards, balanced score cards and predictive tools within their organization which can facilitate transparency improvement, performance monitoring, setting objectives and planning, and functions of management,
Big data can be applied in managing quality especially in industries such as telecommunication, utilities and energy, and manufacturing. The big data is used in these industries to reduce costs and increase profitability by improving the quality of the offered products (Lieberman, 2014). For instance, in the process of manufacturing, performance variability can be reduced by applying predictive analysis on big data. This can also help to provide early warning alerts which in turn prevent issues of quality. Through the identification of production disruptions before they happen, the organizations can be able to reduce the time to market and decrease scrap rates by saving on the expenditure. Furthermore, big data can result in lead improvement of manufacturing. Also, the monitoring of machine logs and analysis of real time data can help the managers deliver quality management by making quicker decisions. The big data analytics also can sanction the monitoring of network demand in real time, in supplementing the bandwidth forecasting in customer behavior response.
Moreover, the information technology (IT) systems in healthcare can improve the quality and efficiency of care by integrating and communicating the data for the patient across all institutions and departments, while protecting the patient’s privacy (Karthik Kambatlaa, 2014). The individual’s continuity care can be improved by analyzing the health record, as well as massive database creation which through it outcomes and treatment can be compared and predicted. Hence, with the advancements in analytical tools and the increasing application of electronic health records, there is an opportunity to extract the information of the patient and use that information to assess the healthcare quality, health services, and disease control and management.
The citizens’ quality of lives can be improved by using big data. For instance, in healthcare sector, hospitals and homes can use sensors to provide patient monitoring that is continuous and use the streamed data to perform real-time analysis (Elena Geanina, 2012). The information obtained can be used to alert care givers if there is detection of any health anomalies so that the necessary interventions can be made. The analysis of adherence of the patients to their prescription can be done remotely so as to improve the treatment and drug options.
Transportation can be improved and transformed by analyzing information from distributed sensors on vehicles, road, and handheld devices. The sensors provide real-time information on the traffic situations. By having real-time traffic information, the drivers can operate with less disruption safely and also traffic jam can be controlled by predicting it before it occurs (Nada Elgendy, 2014). How the roadways are used can be transformed by having intelligent connected vehicles with the new traffic ecosystem. Also, the use of big data can base in the real-time information to provide smart routing that use personal location data. Such applications can automatically solicit assistance when there is detection of trouble by the sensors, and inform the road users if there is any congestion, scheduled road-work, or accident in real time.
Big data can also be used for understanding of frequency, location, and climate and weather intensity changes. This can be of much benefit to businesses and individuals who depend on weather, such transportation, tourism, and agricultural sectors (Nada Elgendy, 2014). Additionally, there is the new development of analytical techniques and sensors which can be used to forecast weather and climate changes. This can help to predict natural disasters that are related to weather and adopt preventive measures before they occur.
Conclusion
In the era of information, decision makers easily access huge amount of data. Big data refers to the huge information that is high in velocity and variety, hence making it difficult to handle using conventional techniques. Currently the sizes of big data are increasing continuously to many petabytes from a few terabyte dozens in one data set. visualizing, searching, capturing, analytics, and storing are some of the challenges related to big data. From the perspective of a decision maker, the importance of big data is in its potential to give valuable information and knowledge on which decisions can be based. With the increasing types and sizes of data that is unstructured, it has become essential to base on meaningful data inferences to make more informed decisions. Big data helps organizations to enhance the existing new services and products, design new ones, and invent completely new business model. These benefits are gained when the entities apply big data analytics in different business areas such as performance, fraud detection, customer intelligence, quality and risk management, and supply chain intelligence.
References
Dupre, 2013. Big Data Analytics For Supply Chains. [Online]
Available at: https://www.duprelogistics.com/in-the-news/big-data-analytics-for-supply-chains
[Accessed 18 4 2017].
Elena Geanina, 2012. Perspectives on Big Data and Big Data Analytics. Database Systems Journal, 3(4), pp. 3-10.
Jacquiline, B., 2012. Analytics in Higher Education, Louisville: s.n.
Karthik Kambatlaa, ,. ,. G. K. ,. V. K. ,. A. G., 2014. Trends in big data analytics. ournal of Parallel and Distributed Computing, 74(7), p. 2561–2573.
Lieberman, M., 2014. Visualizing Big Data: Social Network Analysis. Texas, Multivariate Solutions.
Magnusson, J., 2012. ocial Network Analysis Utilizing Big Data Technology , s.l.: s.n.
Nada Elgendy, A. E., 2014. Big Data Analytic: Literature Review Paper. Research Gate, pp. 2015-2023.
Nicholas Graves, A. G. B. K. H. C. C. B. C., 2011. The Importance of Good Data, Analysis, and Interpretation for Showing the Economics of Reducing Healthcare-Associated Infection •. Chicago Journal, 32(9), pp. 927-932.
Partners, N., 2012. Why is BIG Data Important?, s.l.: s.n.
Peersman, G., 2014. Overview: Data Collection and Analysis Methods in Impact Evaluation, s.l.: s.n.
Rasmus Wegener, V. S., 2013. The value of Big Data: How analytics Differentiates Winners, Atlanta: s.n.
Sudhakar, K., 2015. Data analytics – the changing use of Data Within Internal Audit, s.l.: s.n.
Tulasi, B., 2013. Significance of Big Data and Analytics in Higher Education. International Journal of Computer Applications, 68(4), pp. 21-23.
Wagner, D., 2014. The importance of big data analytics in business. [Online]
Available at: https://www.techradar.com/news/world-of-tech/the-importance-of-big-data-analytics-in-business-1267606
[Accessed 2017].
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