Business Analytics Dissertation Help: Defining And Identifying A Research Agenda For An Evidence Based Management Framew

1. Business Analytics Dissertation Help: Defining and Identifying a Research Agenda for an Evidence Based Management Framework Dr. Nancy Agens, Head, Technical Operations, Phdassistance info@phdassistance.com In Brief You will find the best dissertation research areas/topics researchers enrolled in Engineering and Technology. In order to identify future research topics, we have reviewed the technical conventions. (recent peer- reviewed studies). Business analytics refers to the systematic use of data collected from a variety of sources, quantitative and statistical analysis, predictive and explanatory models and evidence-based management to direct actions and decisions towards the appropriate stakeholders. business analytics dissertations, it is important for the researchers to have strong knowledge approaches used and in the data science and machine learning which will help in writing business analytics dissertation. Keywords: Research Proposal, Academic Writing, Literature Literature review writing help, dissertation writing service. I. INTRODUCTION Organizations employ business analytics to make intelligent decisions, which can be made quicker and better in order to enhance the value of the business. Until today, academicians and industrialists have focused mainly on predictive analytics. However, prescriptive analytics is gaining huge interest in terms of research in the business analytics area as it is considered as the best course of action for future businesses. Therefore Prescriptive analytics is often considered as the next step in improving data analytics maturity, which further leads to augmented decision making improving business performance. Business analytics refers to the systematic useof data collected from a variety of sources, quantitative and statistical analysis, predictive models and evidence-based management to direct actions and decisions towards the appropriate stakeholders (Davenport & Harris, 2007; Soltanpoor & Sellis, 2016). Therefore, business analytics incorporates the use of approaches such as data science, operational research, machine learning and information (Mortenson, Doherty, & Robinson, 2015). In this context, business analytics deal not only with descriptive models but also with models that can offer valuable insights and support business performance decisions. To this end, business analysis has developed beyond a simple raw data analysis on large datasets with the goal of creating a competitive advantage for organizations (Mikalef, Pappas, Krogstie, & Giannakos, 2018; Vidgen, Shaw, & Grant, 2017). Business analytics is classified into three main categories with different levels of difficulty, value and intelligence (Akerkar, 2013; Krumeich, Werth, & Loos, 2016; Šikšnys & Pedersen, 2016) (Krumeich, Christ, Julian, & Kempa-Liehr, 2016): analytics, answering the questions “What has happened?”, “Why did it happen?”, but also “What is happening now?” (mainly in a streaming context); (ii) predictive analytics, answering the questions “What will happen?” and “Why will it happen?” in the future; (iii) prescriptive analytics, answering the questions “What should I do?”and “Why should I do it?” (Lepenioti, Bousdekis, Apostolou, & Mentzas, 2020). for future and explanatory systems fields Writing a related to the review help, writing, Dissertation (i) descriptive descriptive and Copyright © 2020 PhdAssistance. All rights reserved 1

2. Therefore, a data-driven model to evaluate an outcome and compare that outcome with alternative interventions. This concept of data-driven decision making is an integral part of the Business Analytics System (BAF) of Holsapple, Lee-Post and Pakath, in which evidence-based identification and solutions occur in a business context (Holsapple, Lee-Post, & Pakath, 2014). Decisions powered by data are evidence-based and companies step into evidence-based analytics to obtain a competitive advantage Comuzzi, & Yoo, 2017; Davenport, 2006; Wimmer, Yoon, & Sugumaran, 2016). DSS as a business analytics program, therefore, belongs to EBMgt's assessed by external evidence circle in Figure 1 (Scheibe, Nilakanta, Ragsdale, & Younie, 2019). writing a while business analytics dissertations, it is important for the researchers to have strong knowledge approaches used and in the data science and machine learning which will help in writing business analytics dissertation. II. FOR THE RESEARCH AGENDA AND CREATING AN EVIDENCE- BASED MANAGEMENT FRAMEWORK Association to Advance Collegiate Schools of Business (AACSB), 2018 states that “In today’s proliferating dynamic business environment, business schools must react to the business world’s changing needs by providing relevant knowledge and skills to the communities they serve” (Association to Advance Collegiate Schools of Business (AACSB), 2018). An Evidence-Based Management (EBMgt) methodology addresses highly important business issues using effective methodologies and (Pfeffer & Sutton, 2006). During the last two decades, the prevalence of EBMgt in research has increased. It was put forward as a way of bridging the gap between research and practice, EBMgt is a data and theory-driven approach making, arguing that while managers cannot have full knowledge in ever- changing environments, the consistency of decisions is usually enhanced by taking data-driven facts (Pfeffer & Sutton, 2006; Pfeffer, 2012). It is “about making decisions by the diligent, clear, and judicious use of four sources of knowledge: The classical concept of decision support systems (DSS) is a computer-based information system leveraging data and/or models to support decision-makers solve semi-structured or unstructured problems (Turban, Aronson, & Liang, 2004). The classical what-if analysis of DSS is to take a question, apply related to the problem (Cho, Song, The intersection of EBMgt and analytics is especially important for research on management science but is often only discussed in passing. In addition, this framework highlights the importance of data, models, stakeholders and context in implementing business analytics solutions through the four elements of EBMgt. Companies use analytics to succeed through advances in automated business processes and are enabled by predictive analytics (Prahalad & Krishnan, 1999). implementations developing and to decision- DSS research has lasted for almost half a century, but interest in DSS techniques, methods, and implementation has recently increased under the umbrella of "big data" and "business analytics" (Holsapple et al., 2014). Therefore, EBMgt is concerned with making decisions by using conscientiously, specifically and judiciously the best available data from various sources to maximize the likelihood of favourable outcomes. into consideration Copyright © 2020 PhdAssistance. All rights reserved 2

3. Decision Fig .1 Four Elements of EBMgt This can be accomplished through the six A’s: (1) Asking: transforming a real-world problem into an answerable question. (2) Acquiring: the systematic search and gathering of proof. (3) Appraising: to objectively evaluate the quality and validity of the evidence. (4) Aggregating: weighing proof and putting it all together. (5) Applying: Inclusion of evidence in the process of decision- making. (6) Assessing: Evaluation of the outcomes of the decision adopted. The intersection of EBMgt and analytics is particularly important for research in management sciences. Therefore, writing a dissertation in the field of business analytics can be challenging; however, it is important to have a thorough understanding of the business analytics and evidence-based management and their correlation. REFERENCES [1] Cho, M., Song, M., Comuzzi, M., & Yoo, S. (2017). Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques. Decision Support Systems, https://doi.org/10.1016/j.dss.2017.10.004 [2] Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98. Retrieved from http://www.impactline.net/%C0%DA%B7%E1% C3%B7%BA%CE%B9%B0/OLAPDW/Analytics HBR.pdf [3] Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics. The Winning.�Harvard Boston, MA., 2007. Google Scholar Google Scholar Digital Library Digital Library. Retrieved from https://lib.ugent.be/en/catalog/rug01:001210069 [4] Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, https://doi.org/10.1016/j.dss.2014.05.013 [5] Krumeich, Christ, M., Julian, & Kempa-Liehr, A. W. (2016). Integrating Predictive Analytics into Complex Event Processing by Using Conditional Density Estimations. International Enterprise Computing Workshop https://doi.org/10.1109/EDOCW.2016.7584363 [6] Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 104, 92–103. New Science School of Business Press, 64, 130–141. 2016 Distributed (EDOCW), IEEE 20th Object 1–8. 57–70. Copyright © 2020 PhdAssistance. All rights reserved 3

4. https://doi.org/10.1016/j.ijinfomgt.2019.04.003 [7] Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and E-Business Management, 16(3), 547–578. https://doi.org/10.1007/s10257- 017-0362-y [8] Mortenson, M. J., Doherty, N. F., & Robinson, S. (2015). Operational research from Taylorism to Terabytes: A research agenda for the analytics age. European Journal of Operational Research, 241(3), https://doi.org/10.1016/j.ejor.2014.08.029 [9] Pfeffer, J. (2012). Evidence-based management for entrepreneurial environments: Faster and better decisions with less risk. In Chance and Intent (pp. 71–82). Retrieved https://www.taylorfrancis.com/books/e/978020312 6677/chapters/10.4324/9780203126677-11 [10] Pfeffer, J., & Sutton, R. I. (2006). Management half- truths and nonsense: How to practice evidence- based management. California Management Review, 48(3), 77–100. https://journals.sagepub.com/doi/pdf/10.1177/000 812560604800301 [11] Prahalad, C. K., & Krishnan, M. S. (1999). The new meaning of quality in the information age. Harvard Business Review, 77(5), 109. Retrieved from https://go.gale.com/ps/i.do?id=GALE%7CA55739 484&sid=googleScholar&v=2.1&it=r&linkaccess =abs&issn=00178012&p=AONE&sw=w [12] Scheibe, K. P., Nilakanta, S., Ragsdale, C. T., & Younie, B. (2019). management framework for business analytics. Journal of Business Analytics, 2(1), 47–62. https://doi.org/10.1080/2573234X.2019.1609341 [13] Soltanpoor, R., & Sellis, T. (2016). Prescriptive analytics for big data. Australasian Database Conference, 245–256. https://link.springer.com/chapter/10.1007/978-3- 319-46922-5_19 [14] Turban, E., Aronson, J. E., & Liang, T.-P. (2004). No Title. Decision Support Systems and Intelligent Systems. Retrieved https://dl.acm.org/doi/book/10.5555/994103 [15] Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. Operational Research, 261(2), 626–639. Retrieved from https://www.sciencedirect.com/science/article/pii/ S0377221717301455 [16] Wimmer, H., Yoon, V. Y., & Sugumaran, V. (2016). A multi-agent system to support evidence based medicine and clinical decision making via data sharing and data privacy. Decision Support Systems, https://doi.org/10.1016/j.dss.2016.05.008 583–595. from Retrieved from An evidence-based Retrieved from from European Journal of 88, 51–66. Copyright © 2020 PhdAssistance. All rights reserved 4