Prasad Jagtap

Individual Report From Analytics to Action Big Data Analytics : A key to success Author: Prasad Jagtap (s200109) T ECHNICAL U NIVERSITY O F D ENMARK Hand in date: 10th May, 2020 Big Data Analytics : A key to success Introduction to Big Data & Analytics Big Data refers to the data and information which cannot be handled or processed through the legacy software systems. It is large set of data that needs to be processed by advanced analytics and visualization techniques to uncover hidden patterns and find correlations to improve the decision-making[1]. Many organizations have a huge volume of data, but they are unable to utilize it because it is raw & unstructured in format that is difficult to analyze with the mainstream tech-stack. The evolution in technology has enabled organizations to drive innovation, discover valuable insights, optimize processes & efficient decision making[7]. Technology creates value for the associations, which they acknowledge and leverage accordingly in order to solve their complex Business problems. Identifying the dimensions Big data analytics has grown its roots into industries like finance, sports, infrastructure, agriculture, etc. One such example of implementing SAP Match Insights helped German National Football Team win the FIFA World Cup in 2014 (Figure 1 in Appendix). The unsung hero in their 2014 campaign was none other than big data & predictive analytics. The report helps look back at how the German team worked as a cohesive unit and employed data analytics to create history in the World of football. The DFB (German Football Association) & SAP in combine faced definite challenges and managed to implement the solutions to the big data problem, creating a value based on improvements and ultimately winning the World Cup. The challenges hereby, can be identified in the form of analytics, and can be described in the dimension of data from diamond framework (Figure 2 in Appendix). After certainly working on the solutions, SAP proposed DFB to leverage the insights from the platform, a certain value was generated while processing an outcome, which is why another dimension of the report focuses on Value Creation (Figure 2 in Appendix). Challenges The DFB were in possession of huge data but were not able to figure out what value can data add to their situation. A major problem arose after Germany getting knocked out of the 2012 European Championship. In October 2013, the German football team manager & SAP brand ambassador Mr. Oliver Bierhoff, approached the enterprise software company SAP, to look for innovative ways to improve the team’s performance[6] and decided to work on a streamlined product which could help them overcome the challenges[8]. The main challenges the Football team identified were analyzing the opponent’s positioning, their speed, and the time in possession of the ball. Supporting the cause, a lot of data was made available in order to understand the opponents. Even though, the challenges from the footballing perspective were identified it was of a huge deal for SAP to analyze the data and create a new product for the national team. 2 Big Data Analytics : A key to success It involved a lot of risk factors for them to work on creating such innovative platforms. SAP identified challenges in figuring out what data they exactly wanted to use, as a result, a lot of data was generated, figuring out the technique to carry out analytics using the data and last but not the least, figuring out the medium to showcase the data analysis and is important that the Manager, players, fitness coach & the supporting teams understand the value the newly introduced platform may create. Value proposed solutions In order to overcome these challenges from the perspective of data, SAP came up with the Match Insights platform. It helped the team analyze matches by processing a vast amount of data to help players improve their performance. Players’ movements and passes were recorded on the SAP HANA in-memory database platform. Video data was captured from 8 on-field cameras & it was observed that 10 minutes of data for 10 players with three balls can produce over 7 million data points, which SAP HANA can process in realtime, putting the data in context for coaches and players. This enabled coaches to analyze performance metrics, such as player speed, positioning, and possession time. SAP providing such interactive platform helped Coaches to provide feedback to the players individually (Figure 3 in Appendix) and create value in order to improve their game[2]. There are some areas where the Match Insights tool empowered the win[3]: Speed: The team was able to analyze stats about average possession time and cut it down from 3.4 seconds to about 1.1 seconds. Positioning: The tool reveals virtually defensive shadows that help understand how much area a player can protect or take advantage of the opponent’s weak links. Training: Tool allowed the coaches to adjust the speed, positioning or possession time & accordingly, stats would be sent to the player’s device to look at performance data. Analyzing competition: A lot of qualitative data was available in a bid to predict which countries would advance in the tournament. The key statistics included goals, time of possession, progress in championship and rankings. Conclusion In recent times, there are some great assessments of real-time & spatial analytics in the arena of sports analytics. So, as a football enthusiast, it is of great opportunity to actually channelize sports passion into a proprietary analytics application. After analyzing the situation of the German football team and SAP altogether with an idea of leveraging analytics with the functioning of lean management along with optimized solutions to the player’s situation & team’s response. It can be said that Big Data analytics stand out as 12th man for German football team helping them blitz past their opponents in the 2014 World Cup[9]. 3 Big Data Analytics : A key to success A Appendix Figure 1: Germany lifting World Cup 2014 | FIFA [4] Figure 2: Diamond framework [10] Figure 3: Descriptive analysis of a player [5] 4 Big Data Analytics : A key to success References References [1] Big Data Explained. 2020. explained. URL: https : / / www . mongodb . com / big - data - [2] Irena Bojanova. “IT Enhances Football at World Cup 2014”. In: IT Professional 16 (July 2014), pp. 12–17. DOI: 10.1109/MITP.2014.54. [3] Sangeeta Deogawanka. How Analytics helped Germany win the 2014 FIFA World Cup! Aug. 2014. URL: http://ivyproschool.com/blog/2014/08/09/howanalytics-helped-germany-win-the-2014-fifa-world-cup/. [4] FIFA World Cup 2014. URL: https://www.britannica.com/topic/FIFAWorld-Cup-. [5] Germany’s World Cup Win Stems From Data Analysis. URL: https://itpeernetwork. intel.com/germanys-world-cup-win-stems-from-data-analysis/#gs. 62p5a2. [6] Augustine Hong. SAP ANALYTICS HELPED GERMANY WIN THE 2014 WORLD CUP. 2014. URL: https://www.mediabuzz.com.sg/research- july- 14/ sap-analytics-helped-germany-win-the-2014-world-cup-so-when-isyour-turn-to-get-your-trophy. [7] Y. Liu. “Big Data and Data Analytics”. In: Journal of Business Forecasting (Winter-), pp. 18–21. [8] Brian McKenna. SAP helps Germany lift the World Cup. 2014. URL: https:// www.computerweekly.com/news/-/SAP-helps-Germany-liftthe-World-Cup. [9] Steven Norton. Germany’s 12th Man at the World Cup: Big Data. July 2014. URL: https://blogs.wsj.com/cio/2014/07/10/germanys-12th-man-at-theworld-cup-big-data/. [10] S. Shaw R. Vidgen and D. B. Grant. “Management challenges in creating value from business analytics”. In: European Journal of Operational Research (2017). 1