This document outlines the key issues surrounding the management of Big Data in the 2014 Football World Cup and its subsequent effects on FIFA as an organizing body. It also analyzes the effect of big data on the quality of the game played, player health, revenue earned, fan predictions and the overall performance of county teams. As the influence of data analytics grows globally, the paper analyzes the role that proper big data management plays in one of the most anticipated events globally; Football World Cup which takes place every four years. Analyze planning, analytics, the influence of big data, and appropriate HR policies and data management tools used. Furthermore, the paper seeks to find out the challenges faced and solutions used to mitigate the event and the country's national teams suffering losses. Finally, a final report is provided on the structure and composition of the human capital factor and on how the management of big data in football helps in the correct management of the football team. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay The importance of big data management in the 2014 World Cup is a vast topic and this document does not claim to cover all the issues involved in that said event. Instead, it shares and analyzes the key issues that have affected and are currently affecting FIFA using scholarly research, current facts and figures, and most importantly data analysis from the actual 2014 event, including fan reaction and the meaning of fans' emotions fans. It is necessary to conduct further research on the same topic and on several occasions, including the next Football World Cup after 2014, which used big data to fully grasp its role in the correct management of the organization's resources and in improving the performance of the different teams. Introduction The 2014 Football World Cup, also known as the 2014 FIFA World Cup, held in Brazil ended with the German team as the victor after 64 matches played. The event, which took place between June 12 and July 13, saw a surge in global viewership as citizens of the country tuned in to cheer on their favorite teams (FIFA World Cup Brazil 2014). It has also been marked by an increase in contributions, opinions and criticism from the public across social media platforms such as Face Book, Twitter and LinkedIn. Big Data is an event that occurs when there is a lot of large and complex data that needs to be analyzed. analyzed but which conventional data analysis and management are unable to handle (Raheem et.al 2013). Big data if not analyzed correctly can be annoying but if analyzed correctly it has value. Data is very important during the football world cup not only for the management and players but also for the fans (Almeida et.al 2016). According to Yang, 2015, during the 2014 World Cup, there was a clear correlation between using social media to release emotions and intentions for teams. Social media was used to criticize teams that were not performing as their supporters wanted, to communicate progress, and to publish apologies from players and teams. Rein & Memmert, 2016, state that the correct analysis of big data is a turning point in the world of football. This is because the tactical analysis of the teams was carried out by analyzing observational data during the game and practice time. On the contrary, this is not the caseenough to produce a comprehensive report on elite football. Even though data on team function, physiological ability, and technical ability are available, there was no way to incorporate all of this data to achieve one result (Ohmann et.al 2015). Indeed it was great data, but during the 2014 Football World Cup, this complex data was analyzed and better results were observed. There are several ways in which big data makes it easier to collect, analyze and interpret the large amount of data as discussed below; According to Yang, 2015, during the one-month period in which the World Cup was taking place, there would be a flow of approximately 1.5 Petabytes of data each day from fans in terms of merchandise purchases, tweets and analytics. Facebook. This data was too much to handle and manage. It is necessary to measure this data in order to manage and interpret it to obtain better results (Balagué & Torrents 2005). There was also an increasing need to improve the tactical skills of different teams, meaning that all factors influencing players had to be analyzed and their correlation established. Big data managers have proven to be useful as all this data has been linked, analyzed and managed properly to get the best outcome. 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 joy and anticipation on the part of American fans . There has been an increase in the number of tweets congratulating the US team and a consequent increase in online purchases of team merchandise. After the match, the American team was defeated by the Portuguese team; there was expression of anger and frustration in tweets and a sharp decrease in merchandise purchased. According to disposition theory (Provost & Fawcett, 2013) tweets can be used to analyze fans' emotions and whether these will translate into profits for participating teams and the 2014 FIFA World Cup. When it comes to data and speed of information is more significant than volume. This is why people will tune in to watch the games live. During the 2014 World Cup, television broadcasters that had the right to broadcast the matches live as they took place in Brazil had a competitive advantage over their other counterparts (Fujimura & Sugihara, 2005). This would in turn lead to an increase in revenue from increased advertising. There's also an advantage to being a minute faster in live streaming; every fan wants to see everything as it happens, without delays. 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 was played, there was a reduction in the price of tickets to attend the launch, this led to an increase in the speed of purchase and they were purchased. It was later interpreted that most people would like to watch the matches live but due to high costs cannot; the price was slightly reduced and more tickets were purchased increasing the profits made by 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 huge data. Especially for global and significant sporting events such as the World Cup, huge amounts of data are always available, big data managers analyze the data and lead to conclusive assessments. The final evaluations lead to an improvement of theteam performance and explain why the team we least expect to win the Cup ends up winning it. Let's analyze the current World Champions; The German team started a bit weak in the first matches, especially when they drew against Ghana on June 21 (FIFA World Cup Brazil 2014). Before the matches began, the manager and coach, Joachim Low, were intent on creating a correct team dynamic at the start and start. They carried out detailed data collection and analysis which involved analyzing historical data, external factors influencing the team and finally the tactics of the team members (Junque, et.al 2016). Team members' data were analyzed in terms of technique, physiology, psychology and differences in individual parameters. This data was in terabytes and their analysis was still in progress. The team participated in the world cup. Variety allows for choice and understanding the different choices allows you to choose the most favorable one and the one with best and optimal results. Improved technology and ease of access to information via the Internet offer more variety than one might imagine (Shafizadehkenari et.al 2014). The data is usually one of two ways; structured or unstructured. Structured data is data that is stored specifically and can be easily searched. This is because data on specific information is in one file, so finding it is easier. Unstructured data constitutes 90% of human data because information is randomly placed in any folder and serious analysis needs to be undertaken to draw inferences from the same (Valter & Barry, 2006). Data from social media platforms is unstructured, and big data managers step in to help structure unstructured data and then analyze it. During the 2014 World Cup, large volumes of unstructured data were sent via social media and the Internet. If left unchecked, it would have been a burden to try to understand both fans and players, given that the event was global and the entire world could insert itself into the online conversation. The data first had to be structured and then understood and managed. According to Yang, in 2015, the IP address from which the tweets were sent was checked and these were labeled in files with the names of the countries. This made it easy to understand the emotional position of a population in a particular country. In his analysis, Yang states that after determining the emotional reaction of US fans before and after some games, he used disillusionment theory to develop the theory of disillusionment. reaction. This helped because it created a predictive model that 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 the referees would prepare adequately without getting caught by surprise. The veracity of big data analyzes the collection of unpredictable and uncertain data. Even with the volume, sometimes, a trend is missing that can lead to correct predictions of the results (Araújo et.al 2006). With the rise of betting platforms globally, the 2014 World Cup required predictions that allowed fans to earn money while having fun (Bialkowski et al 2014). Forecasts are also useful for tea managers as they will be able to strategically position and arm their teams with the tactics needed to defeat others. Truthfulness does not depend on volume or speed but on origin. The source must be reliable and known (Shull et.al 2014). Data covering global events such as the World Cup, team lineups and championshipthey cannot be based on hearsay. The information must therefore be verified before being analyzed. Although verification can sometimes lead to the discarding of valuable information due to lack of knowledge of its source, more irrelevant data that would not be useful to management is eliminated (Toga et.al 2015). As a result, you need to have clear and concise methods for analyzing data and information to ensure that vital details are not overlooked due to lack of authenticity. The decision-making process for 2014 football began before the matches even began; first of all it was necessary for FIFA to determine the host countries. It was preordained that the 2014 World Cup would take place in South America; countries were urged to apply. FIFA had to collect data on all the candidates and determine which one had the best infrastructure, standard stadiums, a good political environment and resorts where players could relax (FIFA World Cup Brazil 2014). The data analysis took more than a year to analyze and FIFA will have to identify a host country. Even after making the decision, Brazil was given ample 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 took place, it was necessary to comply with FIFA standards in terms of the team. The team must and should have all members as citizens of the country six months before qualification, the players must have adhered to anti-doping policies and the country has paid all required contributions to FIFA. This is another voluminous data set to analyze during the prequalification 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 flawless as possible, impartial and in line 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 connected to the referee's watch for that warning. This was intended to prevent the incident between England and Germany which occurred in 2010 and which led to England being denied a goal they had scored because the referee did not see the ball actually cross the goal line. This is followed by decisions by managers and coaches on which player to field, who to play in which role and who to exclude. All of this requires real-time data and analysis of facts and figures (Nevill et.al 2008). Talent management is a key factor in position allocation, and talent acquisition is a product of data analytics. With all these decisions to be made and deadlines usually set, it is necessary to have a mechanism where accuracy and accountability are ensured. Countries and FIFA need big data to make these decisions. Previously data analysis was carried out using traditional methods which did not take into consideration all the relevant information required (Cintia, et.al 2015). As a result, teams were not tactically armed with the information needed to improve the game and play better. Furthermore, FIFA always learned of rebellions and rule changes later than they should have, these incidents would give such teams a competitive advantage over all other teams. Traditional analysis was based only on the video captured, on what the eye sawnaked and on the information presented by the teams and players. To investigate this information, in case any doubts should arise, a committee was formed and it would take some time. But with the increase in technology, FIFA can even determine a player's age through the use of an MRI of the wrist (Lago, 2009). Big data analysis of the 2014 Football World Cup, part of the individual player analysis, is crucial to the success of the team. . This is because more data has been collected on the tactics used to evaluate players by coaches and training for different countries. The countries that reached the quarterfinals used the results of big data analytics to their advantage. A physiological demand on a player is linked to improving tactics 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 needs of a midfielder are not the physiological needs of a striker. A midfielder should have resilience and speed while a striker should be good at sprinting (Lees & Barton, 2003, Nakanishi et.al 2008). These player characteristics can only be identified by carefully analyzing their performance against the player's position requirements. Looking at the 2014 World Cup champions Germany, midfielders Draxler and Groetze are very fast and play for big BundesLiga teams in the same positions (2014 FIFA World Cup in Brazil). Ball possession is very important as it ultimately leads to scoring goals. . But what increases ball possession in every game are the player's passes. Long passes have a tendency to be inaccurate as to who will receive the ball, but short passes between players ensure accuracy. A score analysis carried out by Leser et al 2011, showed that ball possession in the score which ultimately led to the actual scores was a result of ball possession starting from the final third. He also noticed that the correct recovery was made after a ball on target was blocked. All of these analyzes are a moment-to-moment understanding of the match based on careful observation of many goal steals and scores. With this in mind players can easily be able to know the most opportune moment to score. Younger players whose age ranges between 20 and 26 years have a tendency to produce better defenders as they can easily deal with elongated ball movements, older players on the other hand have made better attackers as they fully understand the orientation of the goal and can easily coordinate their movements to score (Mesirov, 2010). All these observations made changed football training and this was evident during the 2014 World Cup. Teams that had more tactical ability than stamina made it to the quarter-finals, while those that had more stamina than tactical ability did not. In light of the above details and available data, national team managers have come up with several approaches to determining the best game. tactics to use and players' adaptability to changes during the match (Beetz et.al 2005)). All approaches require voluminous data about other teams, players on their own teams, and information about each player. One of the main approaches is the control space approach where the distance between all players is made to form a convex hull and each player's performance is analyzed within the hull. The defense team covers a surfacegreater than the attacking team, while old and mature players also cover more ground than young and new ones (Rein & Memmert, 2016). This results in older players being on defense more often. The other approach is the network approach which analyzes the passing of the ball by the players. It allows pairing where two or more players, particularly attackers and scorers, are paired to create a sequence (McAfee & Brynjolfsson, 2012). The couple functions as a single entity and is united by differences in ability. One player's weakness is the other player's strength. They complement each other and can easily achieve results by working together (Barton et.al 2006). All of these approaches used in football analytics were evident at the 2014 World Cup and are the result of proper big data analysis being used by teams to improve performance. On the other hand, big data analytics are used by fans in live betting and gambling. Using the previous history of the players and the team, the previous and current lineup, the previous successes and failures of the manager, and other prevailing external conditions, fans may be able to place their picks and win. Betting platforms globally have significantly increased the football fan base as it has gone from being a mere hobby to a money-making business for fans. Big data added value and quality to the 2014 Football World Cup by significantly improving the performances of many teams, especially those who made it to the quarter-finals. The dynamics and coherence of the teams were evident from the performances and seriousness that social media dedicated throughout the entire event (Barris & Button 2008). As a result, talent management was improved as players' physiological abilities were the ultimate factors in deciding the position played. The other advantage is that big data ensured the safety of players while performing their roles (Baro et.al 2015). This is because there was a reduction in player injuries during the event. Once players were assigned the positions they were physically capable of playing, they had more chances on the field without becoming too tired (Mohr et.al 2005, Baca 2008). Big Data also played an important role during training to ensure minimal damage to players and optimal use of their talents. Big data also allowed correct coverage of the event which lasted over a month. South America has a different time zone than many countries globally, especially in Europe, the Middle East, Asia and Africa, but thanks to the availability of big data, the fan base was maintained because they were able to follow the matches live . During the World Cup, it was easy to monitor the actions of players, coaches, teams and the entire event by FIFA as the data could be easily captured and structured in a convenient way. Big data has allowed FIFA to store large amounts of data for auditing and historical analysis. You can refer to this information if necessary and it will be available and easily accessible to the interested party. Due to the extension of big data into other World Cup related events and activities, its use increased the revenue earned and helped in the proper coordination of the event to make it successful (Bartlett, 2004). The revenue helped improve the support activities overseen by FIFA and their efficiency has since been improved. Big data has also increased the opportunity to improve the sport of football globally, regardless of the leagues played. One of..