Findings and Analysis
The easy reflects on “Database, Big Data and Business Intelligence” for analyzing their use in today’s world. The database is defined as an organized structure of indexed information that helps in managing, accessing as well as updating data easily. It is stated by Wu et al. (2014), that big data is a term that helps in describing large volumes of data both in structured as well as unstructured form, which inundates business on a daily basis. On the other hand, Kimball et al. (2015) opined that business intelligence is one of the technologies driven process that is utilized for analyzing as well as presenting information in order to make business related decisions.
The assignment reviews the literature on “Database, Big Data and Business Intelligence” for analyzing their applications as well as an impact on various business organizations. The assignment also discusses the case study “Business Intelligence and Big Data Analytics for Higher Education” for analyzing the problem. The paper helps in providing proper recommendations for improving as well as discussing the situations in some specific ways.
Database, Big data and Business Intelligence
According to Derbyshire et al. (2015), a database is a collection of data in the form of tables, schemas and queries. A DMMS is a computer application that interacts with the users and other entities in the table to exchange information and analysis of data. A database is not portable, but different database can be connected together with the help of SQL commands. The DBMS is sometimes called the ‘Database’. The database contains data in an organized form. DBMS can store data of any form and retrieval of data is very simple.
On the other hand, it is opined by Monroe et al. (2015), that Big Data is defined as a large volume of data that contain both structured and unstructured data. In big data, the amount of data is not important rather how any organization use these data to benefit is what matters. Big data can be analyzed in order to get a better decision and more precise strategic business moves (Mayer and Cukier 2013). Big Data can be characterized by the 3Vs- volume, variety and velocity. It can also be defined as the pool of huge data where exabytes of data are stored over time.
Chang (2014) stated that Business Intelligence is a technology, which is used by the managers, business executives so that they can make better decisions, which will benefit the company as well as help the company to grow. The data that has been collected may focus on a specific area or can represent the overall view of the company (Sauter 2014). It helps an organization to identify the most profitable customers, spotting the trouble within the organization. Although it is quite complex, costly and time-consuming but if it is used correctly, it can benefit the organization.
Data can be collected from anywhere such as business records, scientific data or even from the sensors of IoT. It is stated by Derbyshire et al. (2015) that the data can be raw or preprocessed before analytics are applied. Data can of various forms and of various types. In Big data, data gets collected at a speed of terabytes per second speed. Based on the analysis of these data, a data analyst can give an answer what will be good. The infrastructure of big data is also quite demanding. On the other hand, Davidson (2014) stated that with the help of Business Intelligence, better decisions could be made. It can also help in reducing the cost and increase revenues with proper planning. It also helps in increasing the competitive advantage as it gives an edge over the competitors. It also ensures employee satisfaction and increases their efficiency in work (Swan 2013). Business Intelligence also helps in gaining valuable insights of the customer’s behaviour. It helps in getting the actual cost of the company and helps in understanding the misdeeds that are going on in the organization.
Database, Big data and Business Intelligence
Information system plays an important role in making valid decisions in an organization by providing accurate information and by performing various analytical functions. The information system that is present in an organization helps in structuring the basic data, which is present from various operations as well as records of the company. According to Laudon and Laudon (2013), managers requires rapid access to information for making various types of decisions which are associated with the strategic, financial, operational as well as marketing issues of the organization (Govindan et al. 2015). The information system helps in simplifying as well as speeding the procedure of data retrieval by storing the data in a central location, which can be accessible with the help of the network.
It is stated by Urena et al. (2015) that information system helps in bringing together the data from inside as well as outside of the organization. It is analyzed that setting up proper as well as effective network for linking up the central database to various retail outlets, supply chains as well as distributors for collecting sales as well as production data helps in making proper decisions (Jager et al. 2016). Information system helps decision makers in understanding the implications of their decisions. Decision makers generally use the system for understanding the effect of various changes that are required in the organization.
An organization can take in order to keep their database secure against various types of insider threats. According to Blanco et al. (2016), the battle against the insider threats needs proactive as well as comprehensive database security strategy, and the strategies must address both the unintentional threats as well as intentional threats use cases. It is analyzed that a data repository that is misconfigured are vulnerable to number of attacks (Lazer et al. 2014). A vulnerability assessment must be conducted for comparing the configuration setting which is associated with data repository, and it would help in alerting the administrator about any miss configuration problem.
It is stated by Laudon and Laudon (2013), that monitoring various activities of a database in real time is very much critical in countering the various insider threats. A policy must be put in place when the administrator of the database gets information about any risks then proper as well as effective action is taken. On the other hand, it is opined by Urena et al. (2016) that anomaly detection can be considered as a critical step for countering various types of insider threats. Encryption is considered as one of the significant options that is utilized by system administrators for countering privileges. It is identified that additional database encryption is very much important for protecting accidental various sensitive data disclosure when some of the physical media is decommissioned.
It is believed by Kuh and Trowler that the development, as well as success of higher education, is mainly dependent on the involvement of the student in the course but not on the institution from where they have completed the course. In the year 2009, the University of Bedfordshire started visualizing the importance of big data analytics in managing engagement of the student. Therefore, both the university as well as the BI vendor developed an engagement tracking system for the student (Vincent 2016).The system is aimed at supporting various users including administrative staff, faculty managers as well as academic staff. The system helps in allowing staff to take proper actions for students who faces problem due to poor engagement. The main objective of the project is to benchmark the UoB’s BI maturity level for identifying various toolkit specifications for developing enhanced BI functionalities.
Usefulness of Information system in supporting decision making in an organization
The problem that is associated with the project is that data are fragmented and disrupted in the traditional storage system. The management of the organization faces problem in managing the various data that are associated with the administrative staff, faculty managers, students as well as academic staff of the institution. Manual reports are created by the different department of the organization, which involves risks like data redundancy (Govindan et al. 2015). The risks of errors increase in the manual reports, as it is very much difficult to make corrections and as a result, it is very much difficult to make some informed decisions that are associated with the engagement of the student.
The organization “University of Bedfordshire, ” develops a student engagement tracking system with the help of Big Data Analytics. For this project the organization, benchmark the BI maturity level by identifying the BI toolkit appropriately. The system was created by working jointly with the BI vendor. The project was mainly dependent on the evidence of different types of investigations as well as assessments. It develops an improved SET that is dependent on the requirement of the BI toolkit specification (Mayer and Cukier 2013). Big data that is in the form of student engagement are automatically collected as well as processed for reporting through an interactive dashboard. The dashboard helps in presenting different types of data visualization to users for allowing users to make some of the informed decisions, which are associated with the engagement of the student.
The improved system helps in providing users with the flexibility for customizing indexes as well as the default engagement-measuring index, which are dependent on the perspective of big data analytics. The interactive system helps in providing the ability of creating a recurring standard of the report with the help of interactive visualization. The reports are generally generated with the help of either end-user or with the help of request by using an automatic scheduler (Vincent 2016). This project also helps in demonstrating the value that is needed for creating both informed as well as the analytic based decision on various activities of student engagement. This improved SET is considered as one of the significant systems that is used for processing big data and for reporting various trends of student engagement. The system also helps in reducing student retention.
Conclusion
It is concluded from the overall assignment that DBMS, Big Data and Business Intelligence are very much advantageous for the business organizations. They are meant to provide data security as well as provide better decision making which could be beneficial for the company. Using these technologies, any company can easily know the market and store their data without any difficulty. The case study that is discussed in this assignment helps in illustrating the implementation of big data analytics based student engagement system, which aimed at supporting various users including administrative staff, faculty managers as well as academic staff. The system helps in allowing staff to take proper actions for students who faces problem due to poor engagement. Moreover, these technologies are meant to make the task of all the employees in an organization to make easy and increasing the productivity of the company. These also provide cloud-computing features, which are very much useful in today’s world, which ensures data backup so that the organization may not to suffer a huge loss.
Protection and safeguarding database in an organization
The recommendations that are provided below helps in proper utilization of big data, database as well as business intelligence in the various business organization. The recommendations include:
Use of big data in decision-making system of organization: The use of big data in organizations helps in supporting decision making in an organization. Big data assists in simplifying as well as speeding the procedure of data retrieval by storing the data in a central location, which can be accessible with the help of the network.
Use of proper password for protecting data: Proper passwords must be provided in order to secure the sensitive data of the organization properly. All accounts, as well as resources, must be protected by utilizing proper passwords for providing proper authentication.
Organization need to improve the operation of Business Intelligence: Organizations must improve their operation of business Intelligence by utilizing proper as well as effective approaches, which include event processing, report as well as the use of dashboard against operational data, and embedding various operational processes.
References
Blanco, C., Clement, R., Derevensky, J.L., Goudriaan, A.E., Griffiths, M.D., Hodgins, D.C., van Holst, R.J., Liesching, M., Moersen, C., Molinaro, S. and Parke, A., 2016. Safeguarding the Protection of Minors and Players with Respect to Commercial Gambling in Germany–2.0.
Chang, V., 2014. The business intelligence as a service in the cloud. Future Generation Computer Systems, 37, pp.512-534.
Davidson, W.A., 2014. Creating, managing, evaluating, optimizing creating business partnership standards and creating reuseable knowledge and business intelligence for business partnerships and alliances. U.S. Patent 8,818,819.
Derbyshire, M.K., Gonzales, N.R., Lu, S., He, J., Marchler, G.H., Wang, Z. and Marchler-Bauer, A., 2015. Improving the consistency of domain annotation within the Conserved Domain Database. Database, 2015, p.bav012.
Govindan, K., Rajendran, S., Sarkis, J. and Murugesan, P., 2015. Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98, pp.66-83.
Jäger, H., Nguyen, D.K. and Karatzas, C., Uniscon Universal Identity Control Gmbh, 2016. Method and system for safeguarding database relations against unauthorized access. U.S. Patent Application 15/235,398.
Kimball, R., Ross, M., Becker, B., Thornthwaite, W. and Mundy, J., 2015. The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence Remastered Collection. John Wiley & Sons.
Laudon, K.C. and Laudon, J.P., 2013. Management Information Systems 13e.
Lazer, D., Kennedy, R., King, G. and Vespignani, A., 2014. The parable of Google Flu: traps in big data analysis. Science, 343(6176), pp.1203-1205.
Mayer-Schönberger, V. and Cukier, K., 2013. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
Monroe, B.L., Pan, J., Roberts, M.E., Sen, M. and Sinclair, B., 2015. No! Formal theory, causal inference, and big data are not contradictory trends in political science. PS: Political Science & Politics, 48(01), pp.71-74.
Sauter, V.L., 2014. Decision support systems for business intelligence. John Wiley & Sons.
Swan, M., 2013. The quantified self: Fundamental disruption in big data science and biological discovery. Big Data, 1(2), pp.85-99.
Ureña, R., Chiclana, F., Morente-Molinera, J.A. and Herrera-Viedma, E., 2015. Managing incomplete preference relations in decision making: a review and future trends. Information Sciences, 302, pp.14-32.
Vincent, K. (2016). Intelligence and Big Data Analytics for Higher Education: Cases from UK Higher Education Institutions. Vol.2, No.1, pp.65 – 75.
Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014. Data mining with big data. ieee transactions on knowledge and data engineering, 26(1), pp.97-107.
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