Business Intelligence & Data Warehousing 

Executive Summary of Data Analytics

The exponential increase in large datasets and its availablity for analysis has affected many aspects of the world. This report examines how Big Data, data analytics influence different aspects like health-care, sports, Cybersecurity, and how companies, through business intelligence create business value. It critically explores specific ways business intelligence has been used in sports, health-care, and cybersecurity. Big data analytics, big data, artificial intelligence are disruptive innovations which are changing how research is conducted; and there is need for wider reflection on the implications of data analytics, big data to transform data into insights that inform an organization's business decisions through business intelligence.

Introduction to Data Analytics

Data is transforming into a key aspect of our economy, industry, business development, and health-care. Business intelligence utilizes data warehousing, analysis, and database management to create insightful information to decision-makers. Business intelligence should provide actionable information at the right time, right location, and presented in the right format to assist in decision making. Its impact is to improve the timeliness and quality of inputs to the management team in the decision making process. Business intelligence is made up of big data, data warehousing, data mining, automated learning, refinement and information visualization.

Business intelligence is therefore used by organizations to assist in operational decision making. In general, business intelligence can be utilized in corporate performance management, monitoring business activity, decision support, optimizing production, improving customer relations, operational excellence, and management reporting. Data analysis is utilized to create forecasts based on performance, historical data, and to generate future estimates(Chen, Chiang & Storey, 2012).

Big Data Analytics

Big data are large data volumes that can be analyzed to gain an insight on patterns, and trends. These trends relate especially to human behavior and interactions, business interactions, and communications. Big data analytics depends on innovative technological forms to capture, store, distribute, manage, and processes the data to create insightful information critical in decision making while still being cost-effective (Ohlhorst, 2013).

New technology, and artificial intelligence are being implemented to analyze semi-structured, structured, and unstructured data. Structured data makes up 6% of all the data generated. Data analysis should determine which data to be acted upon and analyzed. Devices like smart-phones, and IoT have led to an increased rate of data creation thus creating the need for real-time analytics and evidence-based planning. Retailers like Amazon and Walmart are processing over one million transactions per hour. Data generated from smart-phones, mobile applications generate information that is used to provide real-time, personalized offers for each customer. The business intelligence tools capture geospatial location data and demographic data which is analyzed in real-time to create customer value and gain competitive advantage (Chen, Chiang & Storey, 2012).

Impact in Healthcare

Big Data in health-care comes with a lot of risks and challenges despite the risks, data analytics has been used in biomedical discovery and specialized patient medical care and therapy by utilizing different health, genome, environmental and disease knowledge bases. It has improved the delivery of quality health-care practices, cut costs associated with the health-care industry and well-being.

Tracking Infectious Diseases

Data analysis has been used to manage the spread and containment of infectious diseases. This is done through real-time data analysis to provide insightful information to hospitals, health-care organizations, and governments. Predictive analysis can determine the underlying social and clinical impacts of infectious diseases thus providing governments and health-care organizations to provide the most effective plans to contain the spread and provide appropriate care to infected persons.

The spread of infectious diseases like the SARS outbreak in 2003 and the global spread of corona-virus from 2019 create a lot of challenges in containment and stopping its spread. In 2003, health officials in Hong Kong utilized data analytics to identify hotspots and areas where the disease was likely to spread next. This created a new platform on how to better fight emergencies including the recent Corona-virus pandemic. Similar approaches are being utilized to fight and stop the spread of the virus (Roy, 2016).

World organizations like WHO and health organizations in various countries are leveraging on the different data sources regarding Covid-19 to document new infections and predict its spread. When new cases of Covid-19 are reported, the data is shared across organizations and governments to generate reports which give statistical evidence of how the disease is spreading and predict where the virus is likely to spread next. WHO is providing real-time GIS data of all documented cases of Covid-19. Health care providers using data analysis to trace persons who might have come into contact with a patient.

From the data above the number of confirmed cases in the world since 31st Dec 2019 is 4,223,047 and the total deaths are reported to be 291,519. The first reported case in the COVID-19 outbreak was reported in Wuhan, China On 31 December 2019. The first case outside of China was reported in Thailand on 13 January 2020. Since then, the virus has now spread to more than 213 countries on all continents (Worldometer, 2020).

Impact in Sports

Sports science is used by the major sporting competitions, leagues, and teams to improve performance and overcome the competition and achieve better player performance and generate more revenue volumes. This has been made possible by advances in technology, professionalism, and utilization of large and complex data sets, game statistics, and individual players' performance data. Sports science investigates players and sports performance in training and competitive matches. Sports like football and rugby are using analysis of video data during games to assist in officiating matches.

Football leagues around the world are utilizing video analysis referee assistance technologies to determine and penalize fouls committed, goals scored, and assist in making the correct decision during match days. This has improved the overall credibility of sports leagues and has also removed bias in match officiating (Vinué & Epifanio, 2017).

Player Performance Analysis

Data analysis can be used to determine the best performing players which influence team selection and identifying the best talent to recruit which influences player transfers and transfer cost. The basketball league NBA and football leagues around Europe collect data sets such as minutes played, goals scored and assists, distance covered by players, injury history, and player health data sets. These data sets helps in-game decision making and thus improving overall team performance. The data also helps teams to knows a player's longevity of peak performance by monitoring several aspects of a player's health like heart rate, speed of the athletes. Data analytics have impacted the price of players since they can pinpoint and predict the worth of a player's contract (Vinué & Epifanio, 2017).

Sports analysis tools like SportVU utilized in the NBA league has not only changed how a player is scouted but can also efficiently analyze how a player is efficient in the first quarter as well as the fourth quarter. The tools track and analyze a player running history for data points such as maximum speed, distance covered, and how much energy a player has expended during a game. Tracking cameras also track the efficiency of players in different areas of the court. Moreover, it analyzes other elements of the sport such as ticket purchase, fan engagements in sports viewership which is important for teams to improving customer, fan match-day experience and increase fan retention.

Impact on Cybersecurity

Advancement in technology bring greater benefits but also presents opportunities for threats in cybercrime, cybersecurity, and cyber attacks. Concerns range from unauthorized access and attacks of private data, data manipulation, sharing of personally identifiable information, and identity theft. Cybersecurity should be part of the design of every business intelligence implementation, database, data warehousing architecture, and data transfer or communication involving data analysis. Cyber-attacks on critical systems are on the rise with objectives ranging from destroying and stealing data. Cyber threats and attacks are becoming more sophisticated as they can go for many months without being detected and are targeting critical information in big organizations (Kamenov, 2018).

Big Data analysis is influencing the way corporations are fighting the threat of Cybercrime. It is being used for cyber-attack detection and defense. It gives organizations a clear picture of potential threats, their sources making them able to prevent attacks from happening. Data analysis can become a better tool for the analysis of a particular cyberattack by mining large amounts of data an organization has collected from cybersecurity events like phishing attempts and malware from the past.

Big Data analysis is changing the way the data collected by traditional cyber-attack prevention systems such as antivirus software, host and network IPSIDS, network logging tool are being analyzed. Data points from these systems can be time-consuming and difficult to analyze thus making many organizations miss out on some key cybercrime events. Implementation of Big Data analysis can sift through these data very quickly giving an insight into how these cyber-attack detection systems can be made more effective and efficient. Such systems can reduce the number of employees tasked with monitoring cyber-attack events. Organizations can achieve these by making the current system more intelligent so that the more dangerous attacks are detected and isolated. Data analysis of cyber attacks can also be sourced from internal network sources and external sources from an online presence and online activities providing isolated search criteria for organizations (Kamenov, 2018).

Conclusion on Data Analytics

The developments in Big Data and analysis will impact on the various ways economic, health-care decisions are made . It has enabled new methods of data generation and analysis making it possible for businesses to come up with answers in deriving business value for its customers. It provides mechanisms of generating and analyzing huge, dynamic, and varied datasets. With the current developments, Big Data is becoming an intergral part of businesses as new data analytics tools become advanced. This presents another way of gaining knowledge apart from the established knowledge-driven scientific methods being used in health-care, Cybersecurity, and sports science. Big Data, therefore, presents new opportunities for social scientists, humanities scholars, health-care providers, and sports analysts through the large amounts of very rich social, cultural, economic, health, political and historical data. Thus generating business value, informing an organization's business decisions, improve performance and come up with better ways to cure and contain diseases.

References for Data Analytics

European Center for Disease Prevention. (2020). COVID-19. Retrieved 14 May 2020, from https://qap.ecdc.europa.eu/public/extensions/COVID-19/COVID-19.html

Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics:

From big data to extraversion impact. MIS Quarterly, 36(4), 1165–1188.

Kamenov, D. (2018). Intelligent Methods for Big Data Analytics and Cyber Security. Information & Security: An International Journal, 39(3), 255-262. DOI: 10.11610/isij.3921

Kaspersky. (2020). Phishing Prevention Tips. Retrieved 14 May 2020, from https://www.kaspersky.com/resource-center/infographics/web-worries-dangers-of-the-internet-infographic

Ohlhorst, F. (2013). Big data analytics. Hoboken, N.J.: John Wiley & Sons.

OptaStats. (2020). Basketball. Retrieved 14 May 2020, from https://www.optasports.com/sports/basketball/

Roy, A. (2016). Impact of Big Data Analytics on Healthcare and Society. Journal Of Biometrics & Biostatistics, 7(3). DOI: 10.4172/2155-6180.1000300

Vinué, G., & Epifanio, I. (2017). Archetypoid analysis for sports analytics. Data Mining And Knowledge Discovery, 31(6), 1643-1677. DOI: 10.1007/s10618-017-0514-1

Worldometer. (2020). Coronavirus Update (Live): 4,445,562 Cases and 298,439 Deaths from COVID-19 Virus Pandemic - Worldometer. Retrieved 14 May 2020, from https://www.worldometers.info/coronavirus/

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