This document describes the main issues surrounding the management of Big Data during the Football World Cup 2014 and its consequent effects on FIFA as an organizing body. It also analyzes the effect of big data on the quality of the game played, the health of the players, the revenue earned, the predictions of the fans and the overall performance of the teams in the country. With the growing influence of data analytics on a global scale, the article analyzes the role played by good Big Data management in one of the most anticipated events in the world: Football World Cup which held every four years. It analyzes the planning, analysis, influence of Big Data as well as the appropriate human resource policies and data management tools used. Furthermore, the paper seeks to uncover the challenges faced and solutions used to mitigate the losses of the event and the country's national teams. Finally, a conclusive report is presented on the structure and composition of the human capital factor and how big data management in football contributes to the successful management of football teams. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”? Get an original essay The importance of big data management in the 2014 FIFA World Cup is a vast topic and this article does not claim to cover all the questions involved in this one. aforementioned event. Conversely, it shares and analyzes key issues that have affected and currently affect FIFA using research by academics, current facts and figures and primarily data analysis from the actual 2014 event, including fan reaction and the importance of their emotions. Further research needs to be carried out on the same topic and on different occasions, including the upcoming Football World Cups that took place after 2014, which used Big Data in order to fully understand the role in good resource management of the organization and improving the performance of the organization. the different teams.IntroductionThe 2014 FIFA World Cup, also known as the 2014 FIFA World Cup, hosted in Brazil, ended with the German team winning after 64 matches played. Taking place between June 12 and July 13, the event attracted a growing global audience as citizens across the country tuned in to cheer on their teams of choice (2014 FIFA World Cup Brazil). It was also marked by an increase in public contributions, opinions and reviews via social media platforms like Face Book, Twitter and LinkedIn. Big Data is a phenomenon that occurs when there is a large number of large and complex data that needs to be analyzed. analyzed but which conventional data analysis and management cannot handle (Raheem et.al 2013). Big data, if not properly analyzed, can be a nuisance, but when properly analyzed, it has value. Data is very important during the football world cup, not only for the management team and players but also for the fans (Almeida et.al 2016). According to Yang, 2015, during the 2014 FIFA World Cup, there was a clear correlation between the use of social media to release emotions and the intentions of teams. Social media was used to criticize teams that were not performing as their fans wanted them to, to communicate progress, and to post results.apologies from the players and teams. Rein and Memmert, 2016, argue that proper big data analysis is a game changer in the world of football. Indeed, the tactical analysis of the teams was carried out by analyzing observation data during the match and training sessions. Conversely, this is not enough to provide a comprehensive report on elite football. Even though there is data on team functioning, physiological capabilities, and technical skills, there was simply no way to incorporate all of this data to arrive at a single outcome (Ohmann et.al 2015 ). This was indeed big data, but during the 2014 FIFA World Cup, this complex data was analyzed and better results were seen. There are several ways to use Big Data to facilitate the collection, analysis and interpretation of the large amount of data as shown below; according to Yang, 2015, during the month-long period when the World Cup was taking place, there would be a flow of approximately 1.5 petabytes of data each day. from daily fan data in terms of merchandise purchases to Facebook tweets and analytics. This data was too cumbersome to handle and manage. It is necessary to measure this data in order to manage and interpret it for better results (Balagué & Torrents 2005). There was also a growing need to improve the tactical skills of different teams, meaning that all factors affecting players had to be analyzed and their correlation established. Big Data managers came in handy because all this data was linked, analyzed and properly managed to get the best result. For example, on June 22, when the United States team played against the Portugal team (2014 FIFA World Cup Brazil), an analysis of the tweets and internet purchases showed the joy and the anticipation of the American fan base. There was an increase in the number of tweets congratulating Team USA and a consequent increase in online purchases of team merchandise. After the match, the American team was defeated by the Portuguese team; there was an expression of anger and frustration in the tweets and a sharp decrease in merchandise purchased. According to disposition theory (Provost & Fawcett, 2013), tweets can be used to analyze fans' emotions and determine whether this will translate into benefits for the participating teams and the 2014 FIFA World Cup. which is about data and the speed of information is more important than the volume. This is the reason why people tune in to watch the matches live. During the 2014 World Cup, television channels that had the rights to broadcast the matches live while they were taking place in Brazil had a competitive advantage over their counterparts (Fujimura & Sugihara, 2005). This in turn would lead to an increase in revenue generated from increased advertisements. There's even an advantage to being a minute faster in live streaming; every fan wants to see everything that happens without any delay. Big Data managers help increase the speed at which data reaches the recipient and the speed at which it is analyzed and translated (Olthof et.al 2015). According to Yang 215, before the first match, there was a reduction in the price of tickets to attend the launch, which led to an increase in the speed of purchase and they were purchased. This was later interpreted that most people would like to watch the matches live but due to high costs they cannot; the price was reduced slightly and more tickets were purchased, increasing the profit madeby FOFA and Brazil. According to Junque de Fortuny et al 2013, sometimes bigger is better, simply because some events cannot be analyzed completely and correctly without having big data. Big data is always available, especially for global and important sporting events like the World Cup, Big Data managers analyze the data and lead to conclusive evaluations. Successful evaluations lead to improved team performance and explain why the team least expected to win the Cup ends up winning it. Let's analyze the current World Cup champions; The German team started a little weakly in their first matches, notably in the draw against Ghana on June 21 (2014 FIFA World Cup Brazil). Before the matches started, the manager and coach, Joachim Low, aimed to create a real team dynamic from the start. They carried out the collection and fine analysis of data which involved the analysis of historical data, external factors affecting the team and finally the tactics of the team members (Junque, et.al 2016). Data from team members was analyzed in terms of technique, physiology, psychology and differences in individual parameters. This data was in terabytes and its analysis was still ongoing even as the team entered the World Cup. Variety allows choice and understanding the different choices allows you to choose the most favorable and the one with the best results. Improved technology and ease of access to information via the Internet provide more variety than one might imagine (Shafizadehkenari et.al 2014). Data is generally presented in two ways: structured or unstructured. Structured data is that which is stored in a specific way and can easily be searched. This is because data on specific information is in a single file, so it's easier to find. Unstructured data accounts for 90% of human data because information is randomly placed in any record and serious analysis must be undertaken in order to draw conclusions (Valter and Barry, 2006). Data on social media platforms is unstructured and big data managers step in to help them structure unstructured data and then analyze it. During the World Cup in 2014, large volumes of unstructured data were sent via social media and the Internet. If nothing was done, it would have been difficult to try to understand the fans and players, given that the event was global and the whole world could tune into an online conversation. The data had to be structured first, then understood and managed. According to Yang, 2015, the IP address from which the tweets were sent was verified and they were tagged in files with the name of the countries. This made it easy to understand the emotional position of a population in a particular country. In his analysis, Yang claims that after determining the emotional reaction of American fans before and after certain matches, he used the theory of disillusionment to propose the theory of disillusionment. reaction. This helped because it created a predictive model whereby in the event of the country winning, losing or drawing, the fan's emotion was predetermined and the resulting financial consequences were well understood and officials would prepare properly without being caught off guard. The veracity of big data analytics unpredictable and uncertain data collected. Even with the volume, there is sometimes a lack oftrend that can lead to correct predictions of results (Araújo et.al 2006). With the number of betting platforms increasing globally, the 2014 World Cup required predictions for fans to win money while having fun (Bialkowski et al 2014). The predictions are also good for tea managers as they will be able to strategically place and arm their teams with the tactics needed to defeat others. Veracity does not depend on volume or speed but on origin. The source must be reliable and known (Shull et.al 2014). Data that affects global events like the World Cup, team rosters and the championship cannot be based on hearsay. The information must therefore be verified before being analyzed. Although verification can sometimes lead to the elimination of valuable information due to a lack of knowledge about its source, more irrelevant data that would not be useful to management is eliminated (Toga et.al 2015). Therefore, it is necessary to have clear and concise methods for analyzing data and information to ensure that vital details are not overlooked due to lack of authenticity. Decision-making for 2014 football began before the games even began; FIFA first had to determine the host countries. The 2014 FIFA World Cup was expected to take place in South America; countries were invited to apply. FIFA had to collect data on all the candidates and determine which one had the best infrastructure, stadiums up to standard, a good political environment and resorts where players could relax (FIFA World Cup FIFA, Brazil 2014). The data took over a year to analyze and for FIFA to propose a host country. Even after the decision was made, Brazil had enough time to make several corrections to the established standards. There was also the decision on which teams would participate that year (Noor et.al 2015). After the World Cup qualifiers, FIFA standards had to be met in terms of the team. The team must and must have all its members citizens of the country six months before qualification, players must have adhered to anti-doping policies and the country has paid all its required contributions to FIFA. This is another big data set to be analyzed during the pre-qualification phase and requires precision and accuracy (Gama et.al 2014). Finally, the decisions made during live matches by referees, linesmen and FIFA analysts are very significant. They must be as perfect as possible, impartial and in compliance with FIFA rules and regulations. This is the only way the game can be considered free and fair (Kostkova et.al 2016). A good example is the introduction of technology that allowed referees to know when the ball crossed the goal line and a team scored. There is a magnetic field that is connected to the referees' watch for this alert. This was to prevent the incident between England and Germany in 2010 which led to England having a goal they had scored disallowed because the referee did not see the ball actually pass the goal line. This is followed by decisions from managers and coaches. on which player to be in the lineup, who to play in which position and who to be left out. All of this requires real-time data and analysis of facts and figures (Nevill et.al 2008). Talent management is a key factor in job allocation and talent acquisition is a product of talent analysis.data. With all these decisions to be made and the deadlines typically set, it is necessary to have a mechanism to ensure accuracy and accountability. Countries and FIFA need big data to make these decisions. Previously, data analysis was carried out through the use of traditional methods which did not take into account all the relevant information required (Cintia, et.al 2015). Therefore, teams tactically did not have the necessary information to improve the game and play better. Additionally, FIFA would always learn about violations and circumvented rules later than they should, these incidents would give these teams a competitive advantage over all other teams. Traditional analysis was based solely on captured video, what the naked eye saw, and information. presented by the teams and players. In order to investigate this information, if it raised any doubt, a committee was formed and this would take time. But with the advancement of technology, FIFA can even determine the age of a player using an MRI of the wrist (Lago, 2009). Big data analysis of the 2014 FIFA World Cup, as part of individual player analysis, is crucial for team success. . Indeed, more data has been collected on the tactics used to evaluate players by coaches and the composition of different countries. The countries that reached the quarter-finals used the results of big data analysis to their advantage. A physiological demand on a player is related to tactical improvement during the game. Physiological demand is the player's ability to implement the necessary action based on his position while playing against opponents. For example, the physiological demands of a midfielder are not those of a striker. A midfielder must demonstrate resilience and speed while a striker must be good at sprinting (Lees & Barton, 2003, Nakanishi et.al 2008). These player characteristics can only be identified by carefully analyzing their performance relative to the player's positional requirements. Looking at 2014 FIFA World Cup world champions Germany, midfielders Draxler and Groetze are very fast and play for big BundesLiga teams in the same positions (2014 FIFA World Cup Brazil ). Possession of the ball is very important because it ultimately leads to the goal. . But what increases ball possession in any match is the player's passes. Long passes tend to be inaccurate in who will receive the ball, but short passes between players ensure accuracy. A scoring zone analysis by Leser et.al 2011 proved that ball possession in the scoring zone that ultimately led to actual scores was the result of ball possession starting from the final third. He also noted that a proper recovery was made after a ball on target was blocked. All these analyzes are an instant understanding of the game based on careful observation of numerous recoveries and goal scores. With this in mind, players can easily know the most opportune time to score. Young players aged 20-26 tend to produce better defenders as they can easily handle extended ball movements, older players on the other hand. better attackers because they fully understand the direction of the goal and can easily coordinate their movements to score (Mesirov, 2010). All these observations changed the formation of football and this was manifested in the 2014 FIFA World Cup. The teams that hadmore tactical skills than endurance qualified for the quarterfinals, while those with more endurance than tactical skills did not. In light of the above details and available data, national team managers have proposed several approaches to determining the best match. tactics to use and adaptability of players to changes during the match (Beetz et.al 2005)). All approaches require big data about other teams, players on their own team, and information about each player. One of the main approaches is the control space approach in which the distance between all players is designed to form a convex hull and the performance of each player is analyzed inside the hull. The defensive team covers more area than the attacking team, while old and mature players also cover more ground than young and new players (Rein & Memmert, 2016). This results in older players being on defense more often. The other approach is the network approach which analyzes players' ball passes. It allows coupling where two or more players, notably attackers and scorers, are coupled in order to create a sequence (McAfee & Brynjolfsson, 2012). The couple works as one entity and is brought together by differences in abilities. One player's weakness is the other's strength. They complement each other and can easily score points by working together (Barton et.al 2006). All these approaches used in football analytics were evident during the 2014 FIFA World Cup and are the result of proper big data analysis used by teams to improve performance. On the other hand, Big Data analytics are used by fans in live betting and gaming. Using previous player and team history, previous roster and current roster, coach's previous successes and failures, and other prevailing external conditions, fans may be able to make their predictions and win . Betting platforms on a global scale have significantly increased the football fan base as football has evolved from just a pastime to a lucrative activity for fans. Big Data added value and quality to the 2014 FIFA World Cup by significantly improving the performance of many teams, especially those who reached the quarter-finals. The team dynamics and cohesiveness were evident through the performances and the seriousness given to social media throughout the event (Barris & Button 2008). Therefore, talent management was improved as the physiological abilities of the players were the ultimate factors in deciding the position occupied. The other advantage is that Big Data ensured the safety of the players while they performed their roles (Baro et.al 2015). Indeed, there was a reduction in player injuries during the event. Once players were assigned to positions that they were physically capable of occupying, they had more chances on the field without getting too tired (Mohr et.al 2005, Baca 2008). Big Data also played a big role during their training to ensure minimal damage to players and optimal management of their talents. Big data also made it possible to properly cover the event which took place over more than a month. South America has a different time zone than many countries around the world, including Europe, the Middle East, Asia and Africa, but thanks to the availability of Big Data, the fan base has been maintained because they were able to follow the matches live. During the World Cup it was easy to watchthe actions of players, coaches, teams and the entire event by FIFA, as the data could easily be captured and structured in a convenient way. Big Data has enabled FIFA to store large amounts of data for review and historical analysis. This information may be accessed if necessary and will be available and easily accessible to the relevant party. Due to the extension of big data in other events and activities related to the World Cup, its use increased the revenue earned and contributed to the proper coordination of the event to make it a success (Bartlett, 2004) . The revenue helped improve the support activities overseen by FIFA and their effectiveness has since been improved. Big Data has also increased the possibility of improving football on a global scale, regardless of the leagues played. One of the biggest challenges in big data is validation (Yin and Kaynak, 2015). Due to the high volume and variety of information collected, how will relevant information be separated from irrelevant data and implemented? The solution to this dilemma leads to increasing the data management approaches to be used. Conversely, it would be the only solution to the challenge. FIFA uses algorithms which are data-driven and data-driven monitoring processes. These ensure that information is validated before an analysis is carried out and inferences drawn (Valter and Barry, 2006). The 2014 FIFA World Cup, being a globally recognized and awaited event every four years, has attracted a high volume of data to analyze and analyze. well understood. According to Yang, 2015, he claims that terabyte-sized tweets would flood into the Twitter account every hour and needed to be analyzed in order to understand the aforementioned repercussions. This volume was too large and required a short analysis period, meaning that most of the data circulating every hour was not analyzed, discarded or used (Bauer & Schöllhorn 1997). The solution is to have social media managers. and data scientists on standby to analyze important information from fans who are customers of this company (Aguiar et.al 2015). This will ensure that the value of data and information is not distorted and is fully captured. This will also increase the efficiency of social media management. Managing big data is an expensive endeavor, due to the volume of data to be analyzed, it is necessary to purchase the necessary hardware and software to manage the data. It is necessary to have specialized experts on the work and the occurrence of the problem, who can easily translate the data and derive conclusive meaning from it (Appelboom et.al 2015). Even though the initial cost is high, the returns are worth it because teams can improve their performance, players will be safer when playing on the pitch, and FIFA Championships will be better in terms of quality. The solution is to make big data management a long-term plan whose results are not expected in the short term. This will allow the process to take its course. FIFA World Cups are events that take place every four years and each event has a larger and more aggressive fan base. Especially with the increase in the number of countries qualifying for the event, there is an increase in data to be received before and during the event. There should be a well-established big data management system that can receive and send data in the shortest and most convenient time frame. ConclusionBig Data is a phenomenon that is there to..