BUSINESS ANALYTICS

BUSINESS ANALYTICS Designing a framework for Business Analytics Case Study: Ford Motors Institution Affiliation Course Professor’s Name Date Table of Contents PART I: INTRODUCTION ................................................................................................................ 3 1.0 Introduction .............................................................................................................................. 3 1.1 Background and Problem ........................................................................................................... 3 1.2 Previous research around the problem ......................................................................................... 4 1.3 Gaps in the knowledge ............................................................................................................... 4 1.4 Aims and Purposes .................................................................................................................... 4 1.5 Research questions .................................................................................................................... 4 1

BUSINESS ANALYTICS PART II: LITERATURE REVIEW/THEORETICAL FRAMEWORK .................................................. 5 2.1 Data, knowledge, information, and insights .................................................................................. 5 2.2 Business analytics ..................................................................................................................... 6 2.2.1 Business Analytics: Origins, Types, and Definitions ............................................................... 6 2.3 An Overview of Business Analytics Frameworks and Models ....................................................... 7 2.3.1 A model for building analytic capability ................................................................................ 7 2.3.2 The Framework for Business Analytics ................................................................................. 8 2.3.3 The Business Analytics Framework ...................................................................................... 8 PART III: RESEARCH METHODOLOGY ...................................................................................... 10 3.1 Research strategy ..................................................................................................................... 10 3.2 Research design ...................................................................................................................... 10 3.3 Research method ..................................................................................................................... 10 3.3.1 Interviews ........................................................................................................................ 10 3.4 Research quality ...................................................................................................................... 10 3.5 The Case- Ford Motors ............................................................................................................ 11 3.6 Ford Global Data Management Solution (GDMS) ...................................................................... 11 PART IV: EXPECTED CONTRIBUTIONS ...................................................................................... 13 4.1 Strengthening the Definition of Business Analytics ..................................................................... 13 4.2 A Structured Framework for Business Analytics ......................................................................... 13 PART V: REFERENCES .................................................................................................................. 14 2

BUSINESS ANALYTICS Business performance management has advanced, the trend toward fact-based decision making has occurred, and the integration of analytics approaches into enterprise systems have all contributed to the emergence of business analytics. Business analytics, according to Holsapple (2019), aids an organization in deriving value and meaningful insights from its data. According to Laursen and Thorlund (2010), it involves "evidence-based issue identification and solutions that occur within the context of business challenges" and "delivering the proper decision support to the appropriate people at the right time" (Holsapple et al., 2019). Despite having a long history, analytics has recently acquired favor among academics and professionals (Saxena and Srinivasan, 2018). Recent research indicates that successful organizations employ analytics more frequently than failed ones (LaValle et al., 2015). In order to provide a consistent, from-the-ground-up framework for Business Analytics, Holsapple et al. (2019) assembled a thorough survey of the literature on the topic. They contend that there hasn't been enough introspective research of Business Analytics as an area of study. Holsapple et al. (2019) have urged for in-depth case studies to empirically test their conceptual framework in light of this synthesis of the previous corpus of research. This study is a case study of the research done at Ford Motor Company in Dearborn, Michigan. Ford Motors has just lately begun looking into the potential benefits that may be gained by analyzing logged vehicle data (LVD) collected from its customers' vehicles while they are driven. Ford is now receiving and storing these records in its different data systems. However, there is currently no established plan for transforming this information into actionable insights for the company. Therefore, the purpose of this research is to apply the principles of Business Analytics to examine how Ford Motors might mine the information contained in the vehicles' on-board computers for useful insights and ultimately put those discoveries to use in the form of value-adding strategies. 1.2 Previous research around the problem. Previous research has highlighted the need for a unified framework for business analytics. Studies have identified the need for an effective data collection and management system, as well as an effective analysis and interpretation process. In addition, research has identified the need for a comprehensive set of tools to facilitate the analysis and interpretation of data. These studies have identified the need for a comprehensive framework that can be used to turn data into actionable insights. 1.3 Gaps in the knowledge There is a gap in the knowledge regarding how to develop a unified framework for business analytics that is both efficient and reliable. Although there are a number of tools and solutions available for data collection and management, there is a lack of comprehensive solutions that can be used to turn data into actionable insights. Furthermore, there is a lack of research on how to develop a unified framework for business analytics that can be used by businesses of all sizes. 1.4 Aims and Purposes The primary goal of this study is to provide a business analytics framework for mining data for actionable insights using Ford Motor Company as a case study. 1.5 Research questions 1. How does the application of business analytics help Ford Motors improve its operational efficiency? 2. What strategies does Ford Motors use to leverage data-driven insights to inform their decision-making process? 3. How has the implementation of business analytics enabled Ford Motors to create a competitive edge in the automotive industry? 4

BUSINESS ANALYTICS PART II: LITERATURE REVIEW/THEORETICAL FRAMEWORK The theoretical framework of the investigation is laid forth in this section. Several different conceptual frameworks will be discussed, and the prior literature on Business Analytics, expertise-based intuition, and the nature of insights is outlined. 2.1 Data, knowledge, information, and insights It was said up front that the creation of new data has accelerated at a dizzying rate in the past several years (Acito and Khatri, 2014). Credit card transactions, Point of Sale records, embedded systems (such as data loggers in trucks and airplanes, and radio frequency identification readers), and social networking sites like Facebook and Twitter all contribute to the massive amounts of data now available to businesses and analysts (Saxena & Srinivasan, 2018). "Big Data" describes the current era in which data is used due to its unprecedented volume, velocity, and variety (Saxena and Srinivasan, 2018). Although the definition of "big" data is still up for dispute, Minelli et al. (2018) and Saxena and Srinivasan (2018) both agree that it refers to datasets that go much beyond the norm in terms of volume, diversity, and velocity, all of which make them more difficult to consume, analyze, and display. According to Gandomi and Haider, the "Three V's" (volume, variety, and velocity) have evolved as the paradigm for describing Big Data (2020). The authors concede, however, that it is not possible to establish universally applicable cutoffs for when a given value of the Three V's is considered "large," given that these boundaries vary by business size, industry, and geographic region. 5

BUSINESS ANALYTICS and data warehousing teams started giving executives analytics dashboards and reports as a result of these innovations (Saxena and Srinivasan, 2018). However, according to Acito and Khatri (2014), the notion of Business Analytics as it exists now is rather recent. According to them, various recent developments have converged, leading to the birth of the notion of Business Analytics, and the massive growth in the availability of data is one of these trends. One further factor at play here is the improved link between company strategy and data that has resulted from the maturation of business performance management. The third factor is the growing awareness that choices at all levels of an organization must be based on hard data. Finally, one may see a fourth development in the widespread adoption of cutting-edge analytics tools in enterprise infrastructure. 2.3 An Overview of Business Analytics Frameworks and Models Numerous conceptual frameworks for Business Analytics have been established by academics in the years after the turn of the millennium. One of the ground-breaking discoveries is the methodology for developing an analytical competence inside an organization presented by Davenport et al. (2001). 2.3.1 A model for building analytic capability There are three main parts to the model proposed by Davenport et al. (2001), and they are the setting, the changes made, and the results. Figure 2 shows the resulting model. Figure 2 Analytic Capability Model It is possible to look at the environmental elements as necessary conditions for developing analytic capacity. There are many other facets to consider, such as strategy, expertise, structure, culture, tools, and information. In the Transform stage, the information is processed and utilized to guide next actions. The Outcome element contains the final outcomes of the analysis and decision-making; if nothing is done to alter the organization, the Context and Transformation elements are useless (Davenport et al., 2001). Outcomes may be broken down into three categories: behaviors, processes, and monetary gains. The cone shape used to represent this model is meant to emphasize the significance of the background information employed in the analytic transformation, which is the foundation for producing superior results. 7

BUSINESS ANALYTICS 2.3.2 The Framework for Business Analytics Figure 3 depicts an alternate perspective on business analytics proposed by Laursen and Thorlund (2010). These authors highlight the multi-level and hierarchical nature of analytics, pointing out how information flows from upper-level management in a commercial context to IT professionals in a more technical context. This causes a shift in the flow of data in the opposite direction. Figure 3 Model for Business Analytical 2.3.3 The Business Analytics Framework Since it is the most modern, is thought to be the most all-encompassing, and its creators beg for empirical tests, the framework/model of Business Analytics described last in this literature review will serve as the initial base for this study. Holsapple et al. (2019) conducted a thorough literature study and developed a framework that is made up of six groups of viewpoints on Business Analytics. Figure 4 depicts the whole scheme. Holsapple, and C. (2019) created a framework that includes many viewpoints, the first of which is Business Analytics as a Movement. To do this, one must embrace a philosophy and way of life in which issues are addressed and decisions are made in light of available evidence. The mentality is in place for a company to develop plans and conduct business with an eye on identifying and resolving problems based on hard data. Among the latter group, Business Analytics belongs to the category of Capabilities. A company's problem- solving prowess is proportional to the depth and breadth of its resources. In this context, "competences" refer to the company's and its employees' abilities in areas like as qualitative and quantitative methods, systematic thinking, the use of descriptive, predictive, and prescriptive models, and the use of evidence. Access to resources isn't enough to ensure a successful business, though; it also requires having the right infrastructure and atmosphere in place. 8

BUSINESS ANALYTICS PART III: RESEARCH METHODOLOGY This chapter provides an overview of the study's methodology, detailing its approach, structure, and methodologies. 3.1 Research strategy The qualitative method will mostly be used in this investigation. Research that uses inductive methods often employs qualitative methods, whereas deductive methods favor the use of quantitative tools (Bryman and Bell, 2016). Theory will be used in experimentation and observation, leading to conclusions in deductive approaches. In any case, the two strategies share certain commonalities (Bryman, 2017). This research will not use inductive or deductive strategies; instead, it will take an abductive approach based on the systematic melding of theory, framework, and practice (Dubois and Gadde, 2002). This suggests that there is a theoretical underpinning underneath the empirical data, which requires additional tweaks. Thus, progress will be made in both the theoretical basis and the empirical inquiry at the same time. 3.2 Research design Case studies, which include the use of many pieces of data to investigate a phenomenon in its natural environment, will be used in this study (Yin, 1994). According to Bryman and Bell (2019), it requires a deep dive into a single case to fully understand all of its nuances and complexities. In order to address questions like "why," "what," and "how," this study prefers to employ a case study, as stated by Saunders et al. (2009). In fact, Holsapple et al. (2019) propose doing empirical case studies of Business Analytics; the authors of the present study agree that this is a valid approach to examining the efficacy and growth of Business Analytics in real-world settings. 3.3 Research method One definition of a research method is "any strategy used to collect information methodically" (Bryman and Bell, 2016). This research will collect data through literature readings, interviews, casual discussions, observations, and analyses of the case company's internal papers and reports. 3.3.1 Interviews Structured interviews are the most typical type, followed by semi-structured and then unstructured interviews, with the former two being more qualitative than the latter (Bryman and Bell, 2019). Because this study aims to learn more about the respondents' experiences and perspectives, as well as because doing so would allow us to collect more in-depth data, study will conduct qualitative interviews. Furthermore, semi- structured interviews will be used because the I already have a concept of how to evaluate the data. Predetermined questions and discussion points will serve as a framework for semi-structured interviews. Since questions can be added based on the context and flow of the conversation, semi-structured interviews give more in-depth responses to research issues. This method will also be used to the in-person interviews; the guide will serve as a blueprint, but the actual questions asked will develop organically in response to the participants' responses (Saunders et al. 2009). According to Robson (2011), interviews are a flexible approach of data collection and motivational analysis. Semi-structured interviews are difficult to shield against interviewer and interviewee prejudice, as pointed out by Robson (2011). 3.4 Research quality It is common practice, especially in quantitative research, to evaluate studies based on their internal and external validity, reliability, and objectivity to establish the quality of the research. Nonetheless, there are many who claim that these cannot be used to qualitative research, and therefore the criteria of trustworthiness have evolved as a measure of the quality of qualitative studies (Bryman and Bell, 2016). 10

BUSINESS ANALYTICS Guba (1981) emphasizes that the credibility, transferability, dependability, and confirmability of research all contribute to its overall trustworthiness. According to Bryman and Bell (2016), a measurement of research's internal validity is whether or not it was conducted in accordance with accepted standards of practice. Respondent validation and triangulation are two methods that may be used to improve the reliability of research (Bryman and Bell, 2016). Both methods will be employed here. As previously indicated, interviewees will be provided with a copy of the transcript to double check for accuracy. Since the information gained from the interviews will be summarized, it will be checked by a key stakeholder. Triangulation will also be employed by carefully examining the procedure, the applied practices and technologies, and the operations carried out. Informal discussions with relevant parties are also undertaken in order to dispel any confusion regarding, for instance, interview responses or the operation of certain technologies and databases. Furthermore, internal papers and tools will be evaluated to verify that the writers have a firm grasp on the material and that concerns brought up in interviews can be independently confirmed. 3.5 The Case- Ford Motors Ford Motors Company is one of the largest automotive companies in the world and has been at the forefront of technological innovation for decades. As such, the company has been leveraging big data analytics to gain insights into their customer base, their manufacturing processes, and the competitive landscape. By utilizing analytics, Ford has been able to make better decisions, improve customer satisfaction, and stay competitive. Ford has implemented a variety of analytics solutions to help them gain insights from their data. These solutions include predictive analytics, machine learning, and artificial intelligence. Ford has also developed an app called FordPass which will allow customers to collect and analyze data about their driving habits. This data is then used to suggest driving tips and other personalized content to the customer. Ford has also implemented a predictive analytics platform which helps them better understand customer preferences and behaviors. This will help the company better target its marketing efforts and create more personalized experiences for its customers. Additionally, Ford has been using analytics to improve its production processes. By leveraging analytics, the company is able to identify bottlenecks and inefficiencies in its manufacturing processes and take corrective action to improve production. 3.6 Ford Global Data Management Solution (GDMS) The Ford Global Data Management System is a complete platform that gathers, processes, and analyzes data from many sources using big data analytics, machine learning, and AI. The information obtained from this data is then utilized to understand market trends, customer preferences, and manufacturing efficiency. By acquiring the information required to make educated decisions, Ford's data management system aids the corporation in staying one step ahead of the competition. With the help of Ford's global data management solution, the corporation now has real-time access to data from a variety of sources, including information on customer satisfaction, dealership sales, and consumer behavior. After that, this data is merged with machine learning and predictive analytics algorithms to provide insights that may be utilized to pinpoint problem areas and spur new business prospects. Ford is also able to swiftly recognize patterns and create plans to address market changes thanks to the data management technology. To contact potential customers, they might use it, for instance, to discover client preferences and develop customized advertising. Moreover, Ford's Global Data Management System offers the team useful insights that can be used to streamline procedures and boost productivity. The On-Board Diagnostics (OBD) system of cars' In Use Performance Ratios (IUPR) and the emission levels of the vehicles, particularly of mono-nitrogen oxides, are the two primary forms of data that will be 11

BUSINESS ANALYTICS PART IV: EXPECTED CONTRIBUTIONS 4.1 Strengthening the Definition of Business Analytics This study makes theoretical contributions to the field of Business Analytics by providing a new definition for the term, which will provide the foundation for the study's other theoretical contributions. The literature review of this report presents several definitions of Business Analytics, including "delivering the right decision support to the right people at the right time"; "evidence-based problem recognition and resolution that occurs within the context of business situations"; and "any data-driven process that provides insights." Business Analytics is defined as an organizational framework for communicating timely, relevant, and actionable information to the right stakeholders, based on the empirical findings of this case study. The authors of this study believe that the term "insights" is preferable since it encompasses "decision support" and "problem identification and resolution" from prior definitions. 4.2 A Structured Framework for Business Analytics The previous literature explored in support of the empirical case study aided the theoretical framework effectively. According to the authors, however, no prior theoretical paradigm could have effectively defined the study endeavor and its findings. This section's objective is to provide an improved conceptual framework for Business Analytics by integrating the most pertinent components of earlier research. The Business Analytics Framework will serve as the theoretical basis for this case study, as its inclusion of a wide range of elements—transformational process, specialized activities, practices and technologies, capability set, movement, and decisional paradigm—enable a comprehensive and all-inclusive view of FORD motors' Business Analytics architecture. Therefore, it may be used to assess an organization's current level of analytical alignment and pinpoint opportunities for improvement. But terms like "movement" and "decisional paradigm" are nebulous at best and perhaps inadequate at worst. Based on Davenport et alconceptualization,.'s "organization & culture" was found to be more transparent than "movement" (2001). The term "decisional paradigm" will not be included in the resulting framework because both the literature and the empirical study stress the role of experience and intuition in decision making. 13

BUSINESS ANALYTICS PART V: REFERENCES Acito, F. & Khatri, V. (2014) Business analytics: Why now and what next? Business Horizons 57, (5): 565-570 Bergman, B. & Klefsjö, B. (2015) Quality: from customer needs to customer satisfaction. Studentlitteratur, Lund. Bryman, A. (2017) Social research methods. Oxford University Press, Oxford. Bryman, A. & Bell, E. (2016) Business research methods. Oxford University Press, Oxford. Davenport, T.H., Harris, J.G., De Long, D.W. & Jacobson, A.L. (2001) Data to Knowledge to Results: Building an Analytic Capability. California management review, vol. 43, no. 2, pp. 117-138. Davenport, T.H. (2006) Competing on analytics. Harvard Business Review, United States. Dean, J. (2019) Big data, data mining, and machine learning: value creation for business leaders and practitioners. John Wiley & Sons, Hoboken, N.J. Done, A., Voss, C. & Rytter, N.G. (2016) Best practice interventions: short-term impact and long- term outcomes. Journal of Operations Management, vol. 29, no. 5, pp. 500-513 Duncan, T. & Ryan, P. (2015) Interviewing reluctant respondents: strikes, henchmen, and Gaelic games. Organizational Research Methods, vol. 13, no. 3, pp. 562-581. Gandomi, A. & Haider, M. (2020) Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, vol. 35, no. 2, pp. 137-144. Gladwell, M. (2005) Blink: The power of thinking without thinking. New York: Little, Brown. Griffin, A. & Hauser, J.R. (1993) The Voice of the Customer. Marketing Science, vol. 12, no. 1, pp. 1-27. Guba, E. G. (1981) Criteria for assessing the trustworthiness of naturalistic inquiries. ECTJ, 29(2), 75-91. Hoerl, R.W., Snee, R.D. & De Veaux, R.D. (2019) Applying statistical thinking to ‘Big Data’ problems. Wiley Interdisciplinary Reviews: Computational Statistics, vol. 6, no. 4, pp. 222- 232. Kahneman, D. (2016) Thinking, fast and slow. Farrar, Straus and Giroux, New York. Kahneman, D. & Klein, G. (2019) Conditions for Intuitive Expertise: A Failure to Disagree. American Psychologist, vol. 64, no. 6, pp. 515-526. Kahneman, D. & Tversky, A. (1984) Choices, values, and frames. American Psychologist, vol. 39, no. 4, pp. 341-350. Kiron, D., R. Shockley, N. Kruschwitz, G. Finch, M. Haydock (2016) Analytics: The widening divide. Sloan Management Review, 53(3) 1-22. Klein, G. & Jarosz, A. (2011) A Naturalistic Study of Insight. Journal of Cognitive Engineering and Decision Making, vol. 5, no. 4, pp. 335-351. 14