Big Data Management - Essay Example

Summary of 3 articles, comparison and discussion Table of Contents A and explanation of why this topic is relevant to the data managementcommunity and your organization 3 A summary of each article 4 A contrastive analysis, comparison and evaluation of each articles 6 A discussion of the magnitude to which the ideas are set out and might or might not be used to change policy and practice in data management, along with suggestions 7 Big Data Management 8 Key problems of organization 8 Solutions 9 Discussion of key ideas and its success in solving the difficulties 10 References 12 A description and explanation of why this topic is relevant to the data management community and your organization In 21st century, data management has shifted their development from vital proficiency to a crucial differentiator. The term big data describes the availability and exponential growth of data that can be both unstructured and structured. The common topic of the articles is based on increased emphasis and challenges of big data. In recent years, big data has gained increased popularity of sources comprising social media, user-generated content, mobile transactions and internet clicks (George, Haas and Pentland, 2014, p.321). Other added big data pervasiveness sources are the industrial internet, operations management, finance, health care, genomics and engineering. Big data is viewed as an effective tool to deliver projecting likelihood of an event and analyze patterns. The long run success of an organization is determined by additional of organizational asset with data and information. The topic provides vivid explanation about the broader application of big data in society and management research. It is believed that experiential study in management can often deduces relationships such as two companies can link through customer-supplier relations, or collaborates in production or compete in same market. In the era of globalization and technological advancement, it has become imperative for organization to manage its data and information in an effective way. The additional use of big data is crucial to exemplify the significance of the topic to organization like NHS trust and data management community. The survey conducted by American Management Association in the year 2013 reveals that the need of building analytical skills within the organization is critical for development (AMA, 2014, p.1). The organization selected for analyzing and researching is the drawback of data management is National Health Service trust. The trust serves either on specialized service or geographical area. The NHS trust provides varied range of specialist and general services to patients. The new system is being introduced to serve patients and maintain GP records. The use of powerful technology and massive influx of data are two reasons to increase the future use of big data in NHS trust. A summary of each article The three literature sources provide vivid explanation about big problems, opportunity and harness of big data. These articles discusses that firms enjoying success are able to implement big data to create new businesses and improve their existing trades. This rapid alteration results in authority shifting to decisions and analytics experts are completed in real time. The first article big challenges and opportunities of big data processing introduce several techniques of big data processing from application and system aspects. At the time of broad array of application extents and floods of data, the data is collected and gathered at unprecedented level. Previously, the decisions that were cantered on meticulously fabricated models of reality and guesswork. But now these decisions can be effectively completed on the collected data. It is believed that the use of cloud computing system can be beneficial for organizations to store huge data and information. In the era of data explosion, the use of distributed algorithms and techniques are vital to achieve improved performance and scalability. The article focused on two processing models such as MapReduce-like and MapReduce (Ji, Li, Qiu, Jin, Xu, Awada and Li, 2012, p.1250009-7). The article also states that Mobile Cloud Data Analytics, Virtual Worlds Data and Complex Multi-media Data are some of the imperative programs of present data applications and processing that effectively contributes towards the successful deployment of big data applications. Furthermore, the important aspect of challenges encountered with the processing of big data is considered in the article. The second article is based on exploring the application of big data in management research and methodical assessment of big data applications. It is believed that the ever changing upsurge on volumes of data has forced organizations to jump on this bandwagon to tera and petabytes of total volume storage capacity. Big data and management editorial explores that big data create new opportunities and promises for new practices and theories. Self-quantification data, community data, data exhaust, public data and private data are five sources of big data. These sources are effectively presented in this article. In present-day information technology foundations, the network connectivity is one of the provisions of services that are connected with Service level Agreement. The realm of agreements based on big data-sharing acts as significant barriers to management research and social science. The article tries to reveal the goal and promise of strong management study constructed on big data. It is supposed that some of the datasets of big data are single and multidimensional channel. For instance, on specific communication behaviour and transaction there is an increased chance and opportunity to accumulate and examine the multidimensional data sets (George, Haas and Pentland, 2014, p.324). The prime purpose of this editorial is not to deliver guide for future exploration or systematic review, but to trigger wider discussions of big data in management study and community. The harnessing big data volumes article deals with scientific and technical concerns to handle big data volumes. It is considered that the complete potential of all external and internal information are tap in real time by large volumes of data. The big data demands to assist the society and customers by innovation and quick decisions ways. At business-unit level, organization highlights their decisions and investments on level of data. A fragmented tactic can lead to lack of trust within organization and different groups of people. The article provides example of multinational corporations on leveraging big data. For example, the collection of resolutions for big data volumes has been expanded by HP to help companies to achieve valued information (Dinu and Iovan, 2014, p.252). The article reveals that extensive set of security software has been introduced by organizations to secure identities and volumes of data. One of the prime issues of big data management is the security. This makes huge databases stores in an organization vulnerable to hacking. The article states that the introduction of improved security system can be beneficial for nullifying the risk of hacking. Some of the companies have released mobile security framework to improve treat protection and access of data. The value of cloud computing enables companies to realize introduction of new system to address the growing treat fears. A contrastive analysis, comparison and evaluation of each articles These three articles are quite different to explore the relevance and interestingness of big data management but at the same time mentioned articles are relatively similar to introduce significance of big data. This part of the paper is based on evaluation, comparison and contrastive analysis of big data (Gomaa, 2013, p.102). The article big data challenges describe key issues of big data including computing architecture and cloud storage. In the perspective of cloud computing, the article effectively presented the systematic and methodical flow of study on the big data processing. Furthermore, the article states that distributed approach and scalable storage index are required to retrieve results. Additionally, the article tries to follow the optimization strategies and cover data storage, data virtualization and distributed file system (Zhu, 2014, p.28). The article big data management tries to present the additional use of big data, rather than solely focusing guide of future research and general big data applications. In this article, suitable methodologies are analyzed through appropriate sources of data that can be easily manageable and acceptable by editors. The prime purpose of this report is use notes of editors as stage setters. Moreover, this editorial notes can be implemented in future work and opening new analysis in management study. The movement of pattern of correlation is viewed as challenging task of big data. On the other hand, the article harnessing big data volumes analyses the data that helps to find benchmarks, information and social insights. These facts are hidden and can be difficult to find. The article states that it is crucial to have skilled workforces to meet the demands of big data. The author tried to focus on creating skilled workforce. It is believed that execution of big data solutions within organizations can generate high value of demand for trained professionals (Jain, 2013, p.5). The study reveal that major obstacle to implementation of analytics is gap between the global request for local supply of talent, analytics talent and big data. A discussion of the magnitude to which the ideas are set out and might or might not be used to change policy and practice in data management, along with suggestions This part of the report will evaluate several aspects regarding the application and use of Big Data. Big Data Management The emerging concept and phenomenon named big data is driving organizations or business to get business value from big data. In terms of intended use, structure and content, the use of traditional data and new big data is relatively dissimilar (Sawant, 2013, p.5). The software solutions for big data management try to accommodate diversity by including multiple types of data management platforms and tools. This rise of increased number of data collected augments the risk of security and management. The research provides vivid explanation that scalability, congestion, network connectivity architecture and system problem are some of the primary problem faced by organizations. It is known that the problems of big data management rose when firms recognized the importance of creating data warehouses to run their large relational queries and historical business data (Barske, 2014, p.8). The prime function of data management is to exceed and meet the needs of information of stakeholders. Furthermore, this information needs are related in terms of quality, security and information availability. The chosen organization, NHS trust helps to deliver high quality services to patients. The quality of information and data is one of the beneficial goals of data management. It is revealed that application of re-construction of information framework can enhance the ability of storage capacity and fulfil the needs of big data. Key problems of organization The traditional database machines involved in the mix of prehistoric parallel processing techniques and old hardware architectures. Within few years, it is realized the importance of developing effective policies and system to maintain huge amount of data. Consequently, organizations have to face several problems to manage big data. Big data security- the prime concern of big data is privacy or security. In case of electronic health records, several laws have been introduced by the governing bodies to limit the accessibility of information. The privacy of large volume of information can be a sociological and technical problem. These problems can be effectively addressed by introduction of new architectures (Snasel, 2015, p.55). The data security management activities of data management can be implemented to execute develop and plan effective strategy to provide proper authorizations, auditing of data and authentication. It is known that potential malicious server or attacker can decode cracked information and assume identity of the source. One of the NHS trusts in UK has lost valuable data of patients through management failure. Timeliness- it is known that the larger volumes of data can take huge time for study. The scanning of entire selected data can take longer time. Scale- over decades, the rapid increases scale of data and managing these data has been one of the challenging issues of an organization. These unprecedented alterations increase importance of introducing new storage subsystem (Verma, 2014, p.81). Storage of big data- the increasing speed of storage and needs of big data is not satisfied by current data management systems. Therefore, it is believed that the total overhaul on information framework is required to maintain the needs of big data Solutions Effective network architecture- Now-a-days, companies perform social media analytics and system log analytics for brand management, customer retention, risk assessment, etc. the application of effective network architecture can act as cost efficient tool. Data security management- the introduction of data security management within business operation can benefit to nullify privacy issues (Mosley, Brackett, Earley and Henderson, 2009, p.151). These procedures and policies can help to prevent inappropriate access to data assets. Cloud Computing- the enhance security system of cloud computing enables organization to store and manage large volume of data. It is known that NHS trust uses large volume of data to store records of patients. The application of cloud computing can enhance the performance of data management in NHS trust. Effective data analytics system- the NHS trust of Leeds teaching Hospitals improving its operations by the help of data analytics system and data management (Savvas, 2014, p.1). Furthermore, the NHS trust developed integrated digital care record to recognize patients from several care settings and healthcare applications. Discussion of key ideas and its success in solving the difficulties This can be concluded that provided important ideas and solutions are imperative solve problems of data management. The massive use of third-part infrastructures and services to perform critical processes and host significant data can severely affect the big data processing and storage (Akhgar, 2015, p.12). The challenge of security protection and data monitoring is augmented by rise of security issues. Consequently, it becomes challenging for organization to execute effective privacy protection program to nullify hacking from third-party or potential malicious attacker. The big data enables organizations like NHS trust and communities with an opportunity to develop big data analytics capability to upsurge the speed of decision making and shifts power in the organization. The rapid upsurge of amount of data has accelerated the importance of using big data. The application of big data within organization is dependent upon on-going initiative of data management. The limited capacity of the architecture and increased amount of workload has amplified difficulties of traditional architecture system to manage large volumes of data. The security of large volume of database has been prime concern of organizations. It is revealed that the introduction of effective network architecture and cloud computing system can benefit NHS trust to develop data management system. The application of meta-data management can benefit NHS trust to reduce training cost, lessen time for data-oriented research and raise value of strategic material such as CRM, data warehousing, etc (Jogensen, 2014, p.213). The creation of healthcare patient index helped NHS trust to identify numerous application of healthcare. It is vital to understand the need of clients. The rapid rise of volumes of data can lead to big data drawbacks. The NHS trust need to introduce new computation resources and technologies to incur huge cost by serving the community and maintaining huge volumes of data. The optimization of MapReduce can also act as fundamental framework to work conventionally. The increased demand of expertise on the field of data management has increase the risk of lessen skilled workforce. This type of information can be managed by generating trained professionals. The rise of IT experts can benefit NHS trust to recruit professionals to maintain the database management system. References AMA., 2014. Companies See Need to Build Analytical Skills in Their Organizations: A Study of Analytical Skills in the Workforce. [Online]. Available at: http://www.amanet.org/training/articles/Companies-See-Need-to-Build-Analytical-Skills-in-Their-Organizations.aspx. [Accessed on March 31, 2015]. Barske, A., 2014. Big Data handbook. New York: Springer. Dinu, B., and Iovan, S., 2014. Harnessing Big Data Volumes. West University of Timisoara, 1(1), p.252. George, G., Haas, M., and Pentland, A., 2014. Big Data and Management. Academy of Management Journal, 57(2), p.321. Gomaa, A., 2013. IBM Platform Computing Integration Solutions. London: Routledge. Jain, P., 2013. Big Data Networked Storage Solution. London: Kogan Page. Jogensen, A., 2014. Microsoft Big Data Solution. New York: Springer. Li, Y., Qiu, W., Jin, Y., Xu, Y., Awada, U., and Li, K., 2012. Big Data Processing: Big Challenges and Opportunities. Journal of interconnection networks, 13(3&4), p.1250009-7. Mosley, M., Brackett, M., Early, S., and Henderson, D., 2009. Thed DAMA Guide to the data management body of knowledge. New Jersey: Technics Publication. Savvas, A., 2014. Leeds NHS trust improves patient data management with Inter Systems: Digital Care Record Boosts patient Safety. [Online]. Available at: < http://www.computerworlduk.com/news/public-sector/3526765/leeds-nhs-trust-improves-patient-data-management-with-intersystems/>. [Accessed on March 31, 2015]. Sawant, N., 2013. Big Data Application. New Jersey: Pearson. Snasel, V., 2015. Big Data in complex systems. Stamford: Cengage Learning. Verma, A., 2014. Creating Business Agility. London: Routledge. Zhu, W., 2014. Building Big Data and Analytics Solutions. London: Sage. Akhgar, B., 2015. Application of Big Data. New York: McGraw-Hill. Read More