Effects of Big Data on Sports Analytics-Free-Samples for Students

Question: Discuss about the Business Intelligence using Big Data. Answer: Introduction Sports Analytics are a collection of data that are related to the statistics of different players within single system. These stats are used by the coaches of different sports teams in order to evaluate a project performance of the players. Sports analytics are mostly popular in sports like baseball, basketball, American football and soccer. Sports analytics were first successfully used in a season by former baseball coach Billy Beane where he used the statistical data of players instead of their current performances to buy from other teams and play him in his own team. He successfully used the technique (known as sabremetrics) to become the league champion in that season in 2001. Sports analytics have come a long way from that revolutionary year and is now used by coaches of different sports. Moreover, with the introduction of Big Data, sports analytics find more applicability and practicality in different sports. Previously, sports analytics data was stored in written documents. Ho wever, paper documents easily got destroyed or worn out and hence, the data could not be stored for a long amount of time. However, with the development of Big Data, this particular problem seems to be solved. After the development of information system, data could be stored in storage devices as well as virtual storage interface. There are several web based companies that have designed different websites where the consumers can check different players and teams statistics. Some of these websites are goal.com (soccer), cricbuzz.com (cricket), flashscore.com (baseball) and others. In this report, the effects of Big Data on Sports Analytics and the related factors are analyzed and discussed. Data Collection and Storage Data Collection System Data collections in sports have been done manually before the development of modern automatic data collection techniques and Big Data. Due to the manual handling of the system, the data was often inaccurate. Moreover, due to the high pace of the game, the scorers were not able to cope up with the speed of the game and hence, made many errors. Another issue with the manual system was that the stats were changed so quickly that the viewers could not catch the exact score or statistic. This system called for a massive change in order to make it more effective and efficient as well as recordable so that it can be rewinded whenever needed by the user (Agarwal and Dhar 2014). Big Data has been able to solve the problem of the storage and management of the data but data collection process also needed an overhaul. Use of RFID techniques solved this problem and it now finds extensive use in almost every sport for data collection. This involves use of miniature sensors that are attached at var ious places like ball, bat, stumps, players shirts and others. These sensors record the statistics like run, home run, goal, wicket, etc. and send them to the RFID transceiver placed in the statistics room. The data collected in the transceiver is then recorded in the Big Data for future use and references (Hu et al. 2014). In baseball, Pitchf/x innovation from Sportvision has been introduced in every one of the 30 Major League Baseball Stadiums to track pitches amid diversions. These technologies have been extremely useful in different sports and more and more companies developed new technical instruments and developments for enhancing the match experience. Another company named Sportvision has a suite of different advancements for baseball, football and other popular sports. These companies have utilized existing advanced equipments like Google glass and fitness checker along with own inventions for enhancing the sports analytics process (Kim, Trimi and Chung 2014). Adidas has a f ramework called miCoach that works by having players append a wearable gadget to their shirts. Data from the gadget demonstrates the mentor who the best entertainers are and who needs rest. Another organization, SportVU provides six cameras in six corners of a NBA field that are utilized by the statisticians as well as the coaches to gather data on the developments on each player. From this data, the coaches can monitor the progress of the players. Storage System Previously, sports analytics data was stored in written documents. However, paper documents easily got destroyed or worn out and hence, the data could not be stored for a long amount of time. However, with the development of Big Data, this particular problem seems to be solved. After the development of information system, data could be stored in storage devices as well as virtual storage interface. However, the available storage space was not enough for storing data containing sports statistics of the last 100 years (Marr 2015). With the inception of Big Data, there was almost unlimited virtual storage space was made available and this was then utilized by sports statisticians. Not only Big Data provided the huge amount of storage space but also an efficient management system that did not provide any of the errors that would have been done by manual management. Big Data involves collection, use and management of huge amount of data from various sources and origins. With the gradual d evelopment and improvement of the management systems including analytics, everyday, huge amount of data is being created and transferred. Without Big Data, all these would not have been possible as the storage spaces in the physical drives and devices are limited. Big Data provides virtual storage space for almost unlimited amount of data. While physical storage devices like hard disk provides storage space of around 4 terabytes at maximum, Big Data can provide spaces more than hundreds of petabytes in the virtual interface (Rein and Memmert 2016). Hence, it can easily be said that the biggest advantage of Big Data is the unlimited space it provides as well as its capability manage different types of data with high efficiency. All the more particularly, the neighborhood government directions additionally affect on the Big Data administration process and in the end result in imposing of several rules and guidelines on the data usage and management inside Big Data. However, one main i ssue with the Big Data storage is its security. As the entire storage interface is hosted online, it is vulnerable to external attacks like malware infection, mass hacking attack and others. Although several measures have been taken to solve the issues and strengthen the immunity of the interface, this is a persistent problem as the unethical attackers are also upgrading themselves with the development of technology. For major Big Data related applications, for example, Google, Flicker, Facebook, and Walmart, an extensive number of server ranches are sent everywhere throughout the world to guarantee constant administrations and snappy reactions for neighborhood markets. Such self-ruling sources are the arrangements of the specialized outlines, as well as the aftereffects of the enactment and the direction runs in various nations/districts (Ellaway et al. 2014). For instance, Asian markets of Walmart are intrinsically not quite the same as its North American markets regarding occasio nal advancements, top offer things, and client practices. Data in Action Consumer-Centric Product Design Consumer-centric product is a product or a service that is mainly designed based on the requirements of the customer. Moreover, further modifications are also made based on the customer demands rather than just for profitability through design improvement. With the volumes and sorts of data being gathered, created, put away and utilized evolving day by day, it is not astonishing there is no single, acknowledged meaning of "Advanced Analytics" or simply "Big Data" (Mahmood and Takahashi 2015). The sorts and employments of data and investigation vary generally over all segments, and particularly the US Government: what is Big Data to the National Weather Service may not be to the NSA, the SEC or the IRS. In the meantime, any organization trying to fabricate a reasonable way to deal with enable Federal clients to characterize and address rising data and examination needs should first comprehend the scene. The likelihood of supplanting (or if nothing else lessening) vast quantities of ad ministrations contractual workers with bring down cost, robotized devices and procedures can be Big Data's most engaging perspective to a few clients. Despite the kind of test confronted, recall that arrangements must be customized to help the client's particular mission needs. To adequately serve a client requires a temporary worker to comprehend their main goal targets, characterize in detail the related data and investigation openings and difficulties (in a perfect world before the client does), and to know about and access to present and rising Big Data advances and apparatuses (Kim and Park 2015). This developmental movement is particularly debilitating to customary work serious plans of action of some present temporary workers. For occupants with both profound IT capabilities and extensive management skills, this introduces a troublesome choice on whether to grasp or fight improvements that may tear apart their current administrations organizations. It is assumed that the hard est piece of building up a viable Federal Big Data procedure is the hierarchical test. All these factors provide significant challenges to the development of consumer-centric Big Data applications in the organizations. In this chosen field i.e. sports analytics, the consumers are the sports team management as well as the fans who read and discuss the stats. The product i.e. the sports analytics databases are thus created based on the requirements of the consumers. There are several web based companies that have designed different websites where the consumers can check different players and teams statistics. Some of these websites are goal.com (soccer), cricbuzz.com (cricket), flashscore.com (baseball) and others. Recommendation System Recommendation system is a system that recommends a user a certain number of websites and others based on his search results. For instance, if a user types a certain word in YouTube, the in-built recommendation system provides the user with some videos that are most relevant with his searched keyword. The recommendation system works well with the Big Data. It is known that Big Data contains a huge amount of data and as a result, a user searching for a particular piece of data will find it very hard to find it. For instance, let there has been a soccer match between Argentina and Germany. A user missed the match and now wants to know who scored the goals in the match. However, in the database of the website, there will be records of thousands of soccer games played from its inception till now. Hence, the user will almost cannot find the information he needs without the recommendation system. Now, if there is a recommendation system, he can simply type the match name (Argentina vs. Ger many in this case) as well as the date in which the match has been played. First, the recommendation system will search the database and narrow down all the results to only Argentina vs. Germany soccer matches. After that, it will narrow it down to the selected year and then to the specific date. After it finds the statistics of the selected match, it will then return the results to the user. Business Continuity Since almost all of the online business requires a continuous internet connection, power outages and other internet-related disasters like mass hacking, malware attacks and others can significantly affect the business continuity of the organization. Power outages are out of human control and hence, support systems are to be designed in case of these incidents. The main objective of these support systems will be to continuously record any data operations that are going on in the main systems. When power outage occurs, this system will also shut down but the recorded data and information will be saved safely (Millington and Millington 2015). When the power is back, the saved data operations will be restored back in the main system. Again, there are other security challenges that may result in disastrous situations for the online business organization. In order to prevent these challenges that include malware attack, mass hacking and phishing, the organization should implement strong in ternet firewalls that will restrict the flow of data from the main systems. The main objective of these firewalls will be to restrict any random data from entering the system except only authorized data. All these steps must be taken to avoid disasters and maintain online business continuity. Conclusion In this report, the effects of Big Data on Sports Analytics and the related factors are analyzed and discussed. Big Data concerns substantial volume and developing data sets with different, self-sufficient sources. With the quick improvement of systems administration, data collection, and the data management limits, Big Data involves collection, use and management of huge amount of data from various sources and origins. With the gradual development and improvement of the management systems including analytics, everyday, huge amount of data is being created and transferred. Without Big Data, all these would not have been possible as the storage spaces in the physical drives and devices are limited. Big Data provides virtual storage space for almost unlimited amount of data. Hence, it can easily be said that the biggest advantage of Big Data is the unlimited space it provides as well as its capability manage different types of data with high efficiency. However, one main issue with the Big Data storage is its security. As the entire storage interface is hosted online, it is vulnerable to external attacks like malware infection, mass hacking attack and others. Although several measures have been taken to solve the issues and strengthen the immunity of the interface, this is a persistent problem as the unethical attackers are also upgrading themselves with the development of technology. Sports analytics have come a long way from that revolutionary year and is now used by coaches of different sports. Moreover, with the introduction of Big Data, sports analytics find more applicability and practicality in different sports. Previously, sports analytics data was stored in written documents. However, paper documents easily got destroyed or worn out and hence, the data could not be stored for a long amount of time. However, with the development of Big Data, this particular problem seems to be solved. After the development of information system, data could be stored in st orage devices as well as virtual storage interface. Currently, the main threat of the Big Data in sports analytics is internet security. Big Data is vulnerable to external attacks like malware attacks, mass hacking and others. Hence, proper security systems like firewalls must be developed in order to prevent any attacks on sports analytic database. Recommendations Based on the analysis provided, several recommendations can be made, which are as follows. Application Although Big Data is already implemented in Sports Analytics, it is still limited and the data managed is mostly manual within the virtual interface of the Big Data. A new system must be developed for further automating the data entry and management processes. Consumer-Centricity Maintenance of consumer-centricity is essential in most businesses including sports analytics. It is recommended for future developers that they must maintain consumer-centric designs for future sports analytics websites and databases. Security Currently, the main threat of the Big Data in sports analytics is internet security. Big Data is vulnerable to external attacks like malware attacks, mass hacking and others. Hence, proper security systems like firewalls must be developed in order to prevent any attacks on sports analytic database. References Abbasi, A., Sarker, S. and Chiang, R.H., 2016. Big Data Research in Information Systems: Toward an Inclusive Research Agenda.Journal of the Association for Information Systems,17(2). Agarwal, R. and Dhar, V., 2014. Big data, data science, and analytics: The opportunity and challenge for IS research. Baerg, A., 2017. Big data, sport, and the digital divide: theorizing how athletes might respond to big data monitoring.Journal of Sport and Social Issues,41(1), pp.3-20. Cao, M., Chychyla, R. and Stewart, T., 2015. Big Data analytics in financial statement audits.Accounting Horizons,29(2), pp.423-429. Choo, J. and Park, H., 2013. Customizing computational methods for visual analytics with big data.IEEE Computer Graphics and Applications,33(4), pp.22-28. Davenport, T., 2014.Big data at work: dispelling the myths, uncovering the opportunities. harvard Business review Press. Dubey, R. and Gunasekaran, A., 2015. Education and training for successful career in Big Data and Business Analytics.Industrial and Commercial Training,47(4), pp.174-181. Ellaway, R.H., Pusic, M.V., Galbraith, R.M. and Cameron, T., 2014. Developing the role of big data and analytics in health professional education.Medical teacher,36(3), pp.216-222. Hu, H., Wen, Y., Chua, T.S. and Li, X., 2014. Toward scalable systems for big data analytics: A technology tutorial.IEEE access,2, pp.652-687. Kim, G.H., Trimi, S. and Chung, J.H., 2014. Big-data applications in the government sector.Communications of the ACM,57(3), pp.78-85. Kim, N.J. and Park, J.K., 2015, November. Sports analytics risk monitoring based on hana platform: Sports related big data IoT trends by using HANA In-memory platform. InSoC Design Conference (ISOCC), 2015 International(pp. 221-222). IEEE. Macfadyen, L.P., Dawson, S., Pardo, A. and Gasevic, D., 2014. Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge.Research Practice in Assessment,9. Mahmood, K. and Takahashi, H., 2015, October. Cloud based sports analytics using semantic web tools and technologies. InConsumer Electronics (GCCE), 2015 IEEE 4th Global Conference on(pp. 431-433). IEEE. Marr, B., 2015.Big Data: Using SMART big data, analytics and metrics to make better decisions and improve performance. John Wiley Sons. Marr, B., 2016.Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. John Wiley Sons. Millington, B. and Millington, R., 2015. The Datafication of Everything: Toward a Sociology of Sport and Big Data.Sociology of Sport Journal,32(2), pp.140-160. Rein, R. and Memmert, D., 2016. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science.SpringerPlus,5(1), p.1410. Schrader, D., Gupta, A., Iyer, L., Schiller, S. and Sharda, R., 2016. Sports Analytics Research Collaborations: Connecting Business Schools with Athletic Departments.