Big Data Management and Relevance of Big Data to E-Business - Essay Example

BIG DATA MANAGEMENT (Name of the student) (Name of the Institution) (Date of submission) Introduction The concept of big data has been embraced as a technology, which is disruptive and will be in a position to reshape business in many ways. One such way is through E-commerce where huge data amount is involved. Big data is defines data as the volume, variety, and velocity of the prearranged and amorphous data driving through networks into storage and processors devices together with the change of such data into business advice for enterprises. In business, big data is all about delivering new insights to help decision, makers. Big data helps in changing not only the technology, which is being used in the organization but also the management process and help basic orientations and cultures within organizations. This paper discusses the relevance of big data and its management in current business world. The relevance of this ‘Big Data’ to E-Business The current business environment allows businesses to generate large amounts of data more especially with the exponential growth in the digitization and prevalent social media experiences (Provost & Fawcett 2013). The concept of big data has been embraced as a technology, which is disruptive and will be in a position to reshape business in many ways. One such way is through E-commerce where huge data amount is involved (Mayer-Schönberger & Cukier 2013). The application of big data promises to create unprecedented opportunities for new business models, new business processes, and new business strategies. All big companies over the recent years have advanced their initiatives by implementations of big data that have transformed the business. Chen, Chiang & Storey (2012) defines data as the volume, variety, and velocity of the prearranged and amorphous data driving through networks into storage and processors devices together with the change of such data into business advice for enterprises. In business, big data is all about delivering new insights to help decision, makers. Big data helps in changing not only the technology, which is being used in the organization but also the management process and help basic orientations and cultures within organizations (Provost & Fawcett 2013). The use of external data to help in predicting risk has become crucial to companies and organization. In the financial industries, analyst already closely and aggregate a wide range of statistical reports (Davenport 2014). Big data help businesses in cost reduction, as big data is believed strongly that MIPS and terabytes storage for structured data are now most cheaply delivered through big data technologies. For multinational companies and financial institution, cost saving is not only one of the organization objective but also the mandate of the executive so that they can maximize profit for the shareholders (Davenport 2014). Therefore, big data is relevance in today's business environment since it helps in cost cutting which is the aim of every business. The main strategy of the financial institution is to cut cost, therefore, the banks big data plans need to fit into this strategy. Most of the objectives of the financial institution involve around cost reduction embracing the idea of big data (Ohlhorst 2012). Big data help companies in time reduction, a classic application of time reduction is the pricing optimization in merchandise, where complex and large-scale analytical calculations from hours or days (Burgess & Bruns 2012). This application relevancy is also found in real-time communication with customers, where a large amount of data is being processed at the same time (Ohlhorst 2012). With social media in the rise and globalization, most companies are marketing their product online where they meet with a huge number of their clients, the big data is necessary and will help in ensuring efficient communications. The main reason behind small data analytics was to help in supporting internal business decisions, the decisions as how should the product be priced, how much inventory should be held among other decisions, all these decisions when new uses big data (Burgess & Bruns 2012). Less structured data sources that can be applied to the decision, therefore big data is very relevance to the present business environment where decision-making is being done technologically (Chen, Mao & Liu, 2014). Some of the financial institutions in the United States are using big data technology in making customer related decisions in the companies; the data sources on multi-channel customer's journey are unstructured, and sometimes semi-structured (Davenport 2014). These include website clicks, transaction records, banker's notes, and voice recordings from call centers. These particular decisions have large volumes of raw data, which needs big data concept to hold, this is where the need and relevance of big data come in (Davenport 2014). Business decisions with big data can also involve other traditional areas for analytics such as supply chains, risk management, or pricing. The only problem with the big data problem is the small use of external data to improve the analysis (Burgess & Bruns 2012). In the business of supply chain decisions, for instance, companies are increasingly using external data to measure and monitor the risk involve the supply chain. External sources of supplier data can furnish information on suppliers' and technical capabilities, financial health, quality management, delivery reliability, and whether there is any political risk, market reputation, and commercial practices (Chen, Mao & Liu, 2014). Investment in the big data has become very important in current business environment. Indeed, being data promises or threatens to depend on how you view it to upend legacy technologies at many big companies (Burgess & Bruns 2012). As modernizations and use of technologies gain pace in modern business, and the cost saving in the companies are taking center stage, executives in both lines of business and IT organizations are getting serious about technology solution that are connected to big data (Chen, Mao & Liu, 2014). Legacy is not only replaced with technologies in favor of open sources solution. Big data is part of these technological changes, which are currently taking places in companies. Big data are introducing specialized features that set it apart from legacy systems, which help in business decisions (Burgess & Bruns 2012). Every part of the heap is optimized about the large semi-structured and unstructured of big data. The parts, which move together, consist of a holistic solution that is of fine-tuned for a specialized, high-performance processing storage (Burgess & Bruns 2012). Data management and handling Big data is capable of providing organizations with the means to have better understanding of their customer preferences, behaviors, and expectations. Such concepts and tools can be used to help in maximizing business efficiency, help in cost reduction for both business operations and consumers, improve product and services, and assist in creating a personalized customer experience (Provost & Fawcett 2013).Very few if any people understand the benefits and how to use big data the supporters of big data should come up with a clear channel and regulations on how the big data should be used and managed. The basis of this section is to discuss management of big data in the organization (Mayer-Schönberger & Cukier 2013). As a company chief executive, one should first consider transparency in big data management. The organization should be transparent about the data practices. It should not just be about telling the customer that data is being collected, but rather explain how and why their interest is being protected and giving them choices. The company should be in a position to explain to customers what that data, which has been collected, are being used for, and the projection found out of the data are put for easy accessibility by everyone (Mayer-Schönberger & Cukier 2013). In managing big data, the customer interest should always come first. When considering on how to use the big data, the customer interest should always place first since is the customer who is the more major shareholders of any business organization, without customers companies may run out of business (Chen, Mao & Liu, 2014). There should be a clear distinction between personalization and service improvement as the customers see it. The company management should consider its brands not just the law, prior to using data; consider whether your activity will have a positive or negative impact on the brand (Mayer-Schönberger & Cukier 2013). Data privacy and data security are another important concepts when it comes to big data management. According to the Telecommunication Union, companies have the right to control the information that may be disclosed to the public and third parties. Information, which is posted by user's online profiles, is likely to be used in creating users profile so that it can be further used by organizations to develop their marketing strategies and extend their services to said clients (Chen, Mao & Liu, 2014). Managers need to take drastic measures when it comes to managing individual privacy data, allowing person whether to choose what to post or not to post, as part of the management team, this is one of the areas, which should be given more attention when it comes to implementation of big data in organization (Davenport 2014 Data sharing in the management is also very important. Sharing the information proves to be very valuable properties of development (Davenport 2014 Google search provides a better platform for information sharing, every person, and organization can access such information. On the other hand, organizations have a tremendous amount of information at their disposal and this should be shared selectively as too much information can be used against the company by its competitors and they will lack competitive advantage in the market. On the same note, companies within same industries need to have a platform to share information concerning market trends and how they should manage various risk rising in the same industry (Mayer-Schönberger & Cukier 2013). The big data management should be able to identify challenges that may be experienced in implementation and use of data (Mayer-Schönberger & Cukier 2013). In the case of marketers, the collection of anonymous information that can be combined with other information, thereby identifying the individual and effectively transforming that data into constructive information that can be used to bring changes in an organization. There are other challenges that include data legality and reliability of data, which should be considered by management (Mayer-Schönberger & Cukier 2013). Management should consider minimizing the amount of data they collect from outside, they should concentrate on the use of aggregated and anonymised data to help in obtaining insights through analytics (Burgess & Bruns 2012). A data minimization approach will help businesses to avoid collecting data for which they cannot justify its use, reduce the risk to everyone in the event of a legal breach. These are challenges, which are faced by big data management approach and should be factored in before implementation of any big data in an organization (Burgess & Bruns 2012). Reputation risk needs to be considered in the process of big data implementation, consider conducting customer expectation surveys on the issue of data collection and use. This will help in the process of managing data in the organization and help in avoiding third party conflict when it comes to big data management in an organization. Bibliography Burgess, J., & Bruns, A. (2012). Twitter archives and the challenges of" Big Social Data" for media and communication research. M/C Journal, 15(5). Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209. Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. Harvard Business Review Press. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. Ohlhorst, F. J. (2012). Big data analytics: turning big data into big money. John Wiley & Sons. Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision-making. Big Data, 1(1), 51-59. Read More