Jonah Kipkemboi Lagat

 [Title] [Student`s Name] [Institutional Affiliation] Full Title of Research Paper Abstract Table of Contents Abstract2 Chapter 1: Introduction and Overview5 1.1 Introduction5 1.2 Background7 1.3 Problem Statement8 1.4 Purpose and Scope of the Study9 1.5 Significance of Study to Management Practice and Scholarship9 1.6 Research Question(s)10 1.7 Discussion of Themes/Concepts10 1.8 Definitions/Terminology12 1.9 Chapter Summary13 1.10 Organization of Dissertation13 Chapter 2: Literature Review15 2.1 Introduction to Chapter and Review of Study Background15 2.2 Discussion of Theoretical Framework15 2.3 Literature Review15 2.3.1 Decision Making Theory15 2.3.2 Factors19 2.3.3 Performance Measurements27 2.4 Thesis statement34 2.5 Conceptual Framework and Narrative34 2.6 Chapter Summary35 Chapter 3: Methodology36 3.1 Introduction to the Chapter36 3.2 Evidence Based Research & Management36 3.3 Search Strategies40 3.4 Quality Appraisal of Literature44 3.5 Synthesis Methodology44 3.6 Expert Panel Review47 3.7 Chapter Summary48 Chapter 4: Findings: Analysis and Discussion49 4.1 Introduction49 4.2 Findings: Evidence for Research Question49 4.3 Discussion Based on Synthesis and Analysis76 4.4 Conceptual Model and Narrative77 4.5 Alternative Perspectives78 4.6 Chapter Summary79 Referrences82 Appendix87 Appendix A: Exclusion Reasoning87 Appendix B: Snowball95 Appendix C: Quality97 Chapter 1: Introduction and Overview 1.1 Introduction Leaders of an organization require quality decision making in order to compete effectively in the market. .The r Risk is an inevitable part of decisions. For most decisions that people make, the risks are small. On an organizational scale, the implications of decisions are much larger. Thus, the theory of decision- making strategies arose to understand these implications and minimize risk. Through the years the research literature has produced the human constraints on the ability to make the optimal decision. Herbert Simon (1987) states a case that humans face complex circumstances, limited amounts of time, and limited mental capacity to a state of what he calls “bounded rationality”. Daniel Kahneman (2011) developed a two-thought system of tthat identifies how each system arrives at different results based on the same input as well as factors that cause people to decide against their best interest. “We can be blind to the obvious, and we are also blind to our blindness” (p. 24). Scholars and practitioners alike have sought ways to achieve more optimal outcomes. Research into organizational behavior on decision-making is a practical approach to help organizations achieve better outcomes. The study of decision making is based on many disciplines such as sociology, psychology, economics, and mathematics. The integration of data with technology has improved organizational processes. An example of this is Dickey’s Barbecue Pit. Dickey’s operates 514 restaurants across the US and developed a data system they call Smoke Stack. The idea behind Smoke Stack is to improve all aspects of the organization including operations, marketing, training, branding and menu development. They examine the data multiple times an hour to enable immediate decisions in the real time. This means that operational behavior can be used to respond to issues. Dickey’s stated that “If a region or a store is above or below a KPI- whether it is labor or cost of goods- we can deploy resources to course correct, and we are reacting to those members every 12 to 24 hours instead at the end of every business week or, in some cases, using months- old data. To stay profitable, it is just not reasonable to do business that way anymore.” (Marr, 2016, p. 177). One of the main challenges has been the end-user adoption. The solution was a dashboard that made it easy for the spectrum of end users, such as corporate all the way down to the people in the stores interacting with customers, to access and understand the data. The fact that Dickeys has found a ways to integrate Smoke Stack into easy to use the platform, makes it easier to adopt into everyday operations. Everyone has access to the data and that has helped Dickeys improve performance. “This flexibility has been key to user adoption and given us valuable insights. Smoke Stack has bridged the gap for us from data that is merely accessible to data that is valuable, timely, manageable and actionable.” (Marr, 2016, pp. 179- 180). Data visualization tools are just one example of the many organizations that have adopted data and implemented technology as an important factor in making- decisions and managing risk. Executives have understood the importance of aligning goals with action but monitoring performance in real time relative to these goals were difficult before data integrated with technology. Platforms, such as Smoke Stack, can provide complex information in an easy to use and visually engaging format to decision-makers at all levels to assist in the decision-making process. Lohr (2015) points out that data and technology must be used correctly to be effective. “All successful technologies raise alarms and involve trade-offs and risks. In ancient times, the fire could cook your food and keep it warm, but, out of control, could burn down your hut. The outlook for the technology of big data is not fundamentally different.” (p.213). Technology alone is not enough. Organizations understand the importance of data and technology but now they are seeking to leverage this and adopt data-driven processes of decision making that can improve the organizational performance. This means that organizations can now measure outputs that are beyond profits in ways they never could before, thanks to data and technology. This research is explored from a scholar-practitioner perspective and is aimed at understanding the impact of data- driven decision-making on the organizational performance. 1.2 Background Chester Barnard (1938) brought the description of decision-maker into organizations as a function of the executives. This changed the ways in how leaders thought about what they did. Later theorist such as Herbert Simon, Victor Vroom, and Daniel Kahneman laid the foundation on the theories surrounding decision-making. This thought of who makes decisions and how has been questions that organizations seek to answer in a rapidly changing economy. Research into the organizational behavior and risk of decisions is performed to help achieve better outcomes. In history, the change in technology has challenged traditional ways of how organizations function. It is this sophistication of understanding human behavior, looking at advances in technology that has improved the decision-making process. Through the years, theories have revealed constraints on making decisions. Simon (1957) suggests that decision-makers are bounded by rationality. Kahneman (2011) identified systems that cause people to decide against their interests in situations that they know better. Theorists of decision-making research ways to achieve effective outcomes. Other theorists joined the research ranks of organizational behavior of decision-making to use technology to model human cognition and to aid in the process. Simon along with Allen Newell came up with artificial intelligences that are is intended to automate this process (Newell & Simon, 1976). Supporting decision-making is complex and new systems are necessary to exploit data streams (Power, p. 2016, 348). Literature about decision making and performance measurements connect through data. Data is a powerful tool that has its limits. Much of the performance outcome depends on the context of what is being measured and how it is measured (Lohr, 2014, p. 8). Performance measures allow organizations to draw insights to conditions that result in success and failures. Some ways organizations measure performance is through measurement systems like Kaplan & Norton (1996) balanced scorecard. Yet, even when organizations have measureds, it is imperative they review the effectiveness in the changing economic world to make sure the measures relate to the objectives. When considering this, the leaders can push organizations towards data- driven decision making in order to meet the strategic objective and increase performance. 1.3 Problem Statement   Organizational leaders need to gain the knowledge needed to make informed decisions and assess performance. When organizations fail to implement processes that enables measurement of progress towards the objective, it is difficult to evaluate the performance. Performance measurements are a way to understand how decisions impact the organization. Organizations are attempting to combine their unique knowledge with resources available and transform these into better tools to make decisions. Data and technology is are offering an updated decision-making process. As more investments are being made into producing better decisions, there is a need for practitioners and scholars to understand the factors contributing to or organizational decision-making and their outcomes. Organizations are seeing rapid changes and increased competition. As organizational competition has become more complex, effective decision-making has also intensified. The proposed research is meant to link data and technology to decision-making efforts in order to see what kind of an impact this has on organizational performance. 1.4 Purpose and Scope of the Study The purpose of this study is to identify the transformational impacts that data- driven decision making is having on organizational performance. The theoretical models are based on the factors of data and technology that are changing the way organizations make decisions and how that is being processed as an outcome of performance measurements. Data has become a common topic in practitioner literature. With increased data collections and the use of big data, this research is timely and necessary for scholar-practitioners wanting to see the impact. 1.5 Significance of Study to Management Practice and Scholarship The significance of this study is that businesses are investing in data with hope to enhance their performance. This dissertation can contribute to the research on the impact of data- driven decision making on the organization. Through the examination of the factors contributing to the use of data on decision-making, scholar-practitioners can gain a better understanding of how data can impact the organization performance. It is important to point out that this topic is fairly new in the management field and therefore it is in the exploratory stage. Data- driven decision-making focuses on adoption, drawing insights, implementation, making better decisions, and critical factors that might lead to automation. Jain & Sharma (2014) point out that the data- driven leader is committed to learning about the customer and all the dynamics to drive growth and innovation and these leaders inherently believe that data will help drive superior business decisions and ultimately outcomes (p. 144). This topic is important to organizations and management. Data- driven is a philosophy that leaders of organizations must adaopt with the goal of providing quality data as an input into the decision-making process in order to positively impact the output of the performance. Academic researchers, such as myself, are finding this area of research of interests due to the rapid rate in which organizations are adopting a data- driven approach. Since quality decision-making is imperative for organizational success, this study will contribute to understanding the relationship of data drovedriven decision-making and organizational performance. 1.6 Research Question(s) In order to examine the use of data in the decision-making process and the relationship to organizational performance, a research question was constructed. How does data- driven decision-making impact organizational performance? 1.7 Discussion of Themes/Concepts When blending decision-making with the use of data technologies, it is an intersection of human art and technological sciences. It is the use of data to inform human decisions to understand what people want and how organizations should operate. The art-science form is to understand a social world through the information found in data. This paper examines the concepts of the balance between the art and the science and how organizations use this to impact their performance. To explain, the following is an example the major themes and concepts. A game is being created by D. Science called “The Art of Data”. In this game, there are 10,000 characters each with 1000 parameters making each one very unique. These characters are ‘data’, in the game which is the ‘data set’. In constructing the game, D. Science formulates different relationships of each character with the main character whose name is Organization based on their unique parameters. In order to do this, D. Science looked through the set of characters using a technique called data mining and generated new plots of information. In order to do this, research was carried out to determine the possibilities of outcomes that the characters could produce which is predictive analysis. Once all of the possibilities were created and the story line created “The Art of Data” was produced and sold. To keep people interested, D. Science had the game reviewed based on feedback on what interested the customers the most. This feedback went back into changing the game to keep the customers coming back for more to see how the story will end. D. Science is a story telling the artist who uses data to create a compelling visualization that represents the value of the story being told. As a good story goes, everyone likes a hero. Therefore, D. Science is constantly looking for indicators and metrics that will improve the relationship of all of the characters to Organization to ensure that Organization remains on par to be the hero in every story known as the performance outcome. The art of storytelling provokes insights that would not have been understood previously. Data tells intricate stories that need to be brought to life. Recently, data was uncovered as a reservoir of currencies for organizations to use. Organizations see the potential of being powerful in shaping their performance but sometimes the data can be overwhelming. There is a new appreciation of the skills of how art and science can come together to understand relationships. People are not aggregates and we all experience the world as individuals. Declaring something will be good for the population, isn’t entirely persuasive. What is needed is the storytelling, the machines giving simplified accounts of how they work. What is also needed is time for people living with decision-making machines to reach a level of comfort (Lohr, 2015, p. 149). Analytics is thought of in scientific terms with a technical understanding. However, technology has provided visualizations tools in which analytics can be articulated and communicated. Organizations are looking towards data to tell the story about humans. People are hard to predict and processing data is about using the knowledge that data affords organizations to understand their behavior. The art of data science is becoming the future. Albert Einstein stated, “The greatest artists are scientists as well”. These concepts show that data- driven decision-making can discover complex insights to aim to improve organizational performance. 1.8 Definitions/Terminology Analytics- applying a method to solve a business problem using data to drive impact Analysis- an assessment performed by an organization to provide a basis for effective decision-making Big Data- data that is based on volume, variety, velocity Business Intelligence-the process of turning data into information and then turning it into knowledge (Tank, 2015, p. 43). Data- drovedriven- using data to drive better decisions Data scientist- individual with the ability to do advanced analytics using tools, people who wield their math and computing smarts to make sense of data Decision Support Systems applications that are paramount in decision making an analysis. KPI- key performance indicator is a measure of success. Margins and revenue are used at the organizational level. Leadership- the process of motivating others to work together to accomplish great things (Vroom & Jago, 2007, p. 23). Measure- a unit that is either quantitative or qualitative which expresses the amount, size, or degree Metric- the standard of measurement Predictive- using historical and current data and a variety of statistical techniques to predict future outcomes to find relationships in data (Janssen et al., 2017, p. 339) 1.9 Chapter Summary Understanding the value of data is more than analyzing. The data needs to be accessible to decision-makers because there is no use for data unless a decision is made. Organizational leaders need tangible results. This research reviews the rise in the interest in data and attempts to discover more about this emerging trend. 1.10 Organization of Dissertation This research follows a five-chapter format. Chapter One- Introduces the topic which is compromised of the problem states, the purpose and scope of the study, the purpose and scope of the study, the significance of the study to management practice and scholarship, the research question, a discussion of the themes and concepts, definitions and provides an summary and organization of this research. Chapter 2- Is a review of the literature and a review of the background. It includes a discussion of the theoretical framework, the literature review, a thesis statement, and a conceptual framework with a narrative. This is the theoretical lens that is used to analyze and interpret the theoretical works. Chapter 3- Is the Methodology section which is the evidence-based research for management. It discusses the methods used such as the search strategies, the quality appraisal of the literature, and a synthesis of the methodology. Also, it discusses the use of the expert panel. Chapter 4- This is the Findings section which presents the results of the systematic review based on synthesis and analysis. It presents a conceptual model which provides a visual analysis of the evidence found in the research. Lastly, Chapter 4 gives alternative perspectives. Chapter 5- Concludes the research and emerging trends. It is overall conclusions based on the researcher’s view as well as implications, recommendations, limitations, and future research. Chapter 2: Literature Review 2.1 Introduction to Chapter and Review of Study Background Organizational performance historically has been measured based on profits. The profit achievement has been the benchmark for the top managers and stakeholders to recognize the efforts of the organization’s employees. Within those profits, managers have to make decisions by designing processes that achieve the objectives. Management needs information prior to the decisions being made but they might not be the best decision which can be reflected in the P&L account. Thus, the purpose of the literature review is to establish the evolution of the decision-making process and integrating data to gain a foundation of how decisions are made by managers in organizations in order to impact organizational performance. 2.2 Discussion of Theoretical Framework In trying to understand the theory development that supports the research question, three theoretical viewpoints were used to conduct the literature review. The three lenses are: enabling factors of data- driven, decision making, and organizational performance. This literature review is to explore seminal works and current approaches that center on the research question. These theories set the stage for understanding data- driven decision making and the outcome of the organizational performance. Also, the literature review brings to light some indicators and metrics that organizations might use in measuring organizational performance. 2.3 Literature Review 2.3.1 Decision Making Theory Chester Barnard author of The Functions of the Executive (1938) stated: “The fine art of executive decision consists in not deciding questions that are not now pertinent, in not deciding prematurely, in not making a decision that cannot be made effective, and in is not making decisions that others should make.” (p. 194). This concept changed how organizations thought about the decision-making process. It highlights that the core of the executive function within an organization is decision-making which makes it important to understand this human behavior. Vroom (2000) agreed with Barnard of the importance of leaders to make decisions and introduced a framework that helps leaders identify leadership styles based on the situation. It is thought that managers behave situationally and adapt based on the situations they face (Vroom, 2000, p. 92). These are important factors for organizations when considering their overall strategy. It is from the work of Barnard that Herbert Simon built upon the decision-making concept and derived his ideas on the topic. Simon (1957) said that “capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problems whose solution is required for objectively rational behavior in the real world — or even for a reasonable approximation to such objective rationality” (p. 198). This is the concept of what is known as bounded rationality. To put it simply, Simon noted that decision-making is a very large task for the mind of humans because the rationality is limited and bounded to the thinking capacity of humans and their experiences. Basel & Bruhl (2013) summarize Simon’s theory by stating “a perfect and ideal solution might exist for our problems, but because of our bounded mind, we are not able to conduct the necessary cognitive steps to reach this goal. Paying respect to this fact, we use satisficing decisions and systematically deviate from ideals of unbounded rationality. With this strategy, we can reach satisfactory but not perfect outcomes” (p. 747). It is not enough to just make decisions but leaders must formulate a plan of action in order to produce the desired outcome from the decision being made. Cyert and March presented a behavioral theory of organizations and provided mechanisms for implementing the decisions. “In order to understand contemporary economic decision making, we need to study; the development of goals, the formation of expectations, and the execution of choices." (Cyert & March, 1963, p. 1). Their objective was to bridge the behavior of the firm with the economics. Cyert and March (1963) outline that one must consider the cognitive limits of humans with the uncertainty and the decision-making involves multiple people at different levels (p. 3). This theory of thought guides leaders in an implementation process to put the decision into action to produce an outcome. In the 1960’s, Victor Vroom joined the ranks at Carnegie Mellon among the colleagues previously mentioned such as Simon. Vroom established a contingency framework in the 1972 that was based on situational leadership styles. This framework was designed to help organizational leaders go through the decision-making process based on their situation. This model was expanded to become the ‘Vroom, Yetton, Jago’ model in 1988 to expand on the complexity of the decision-making process. These can be related on a sliding scale and provides a decision tree. This tree gives leaders a leadership style to perform based on the situation and the contingency variables. They state that the “research waswere supportive of the concept of intrapersonal variance in leadership style and helped to identify some of the situational factors influencing leaders' choices of decision processes (Vroom, Jago, 1974, p. 334). Additionally, the authors emphasized on the decision-making model and the applicability on an effective decision-making process. There are different models of decision making and these models and theories are a tool for studying behaviors of complex decision processes. Vroom (1973) views decision making as a social process and Simon 1987 takes a more individual approach because he focused on how the decision-making process influences decisions. Vroom’s partnership with Jago built a model for leaders to go through based on a situation. A criticism of this is that situations arise that put a constraint time and the ability to go through the process to arrive at a style for the decision. It was noted that “it would be a mistake to conclude that the managers’ behavior was identical to the model” (p. 333). Kahneman and Tversky built upon Simon’s bounded rationality theory. Their idea is that there are automatic operations of perception based in intuition and then very logical operations of reasoning. This became known as the dual process of System 1 and System 2. Kahneman (2003) states: “The operations of System 1 are typically fast, automatic, effortless, associative, implicit (not available to introspection), and often emotionally charged; they are also governed by habit and are therefore difficult to control or modify. The operations of System 2 are slower, serial, effortful, more likely to be consciously monitored and deliberately controlled; they are also relatively flexible and potentially rule- governed” (p. 698). Kahneman is focused on the human brain which is not a computer able to work with unbounded rationality and perfect logic. Thus, as Basel and Bruhl (2013) point out, the brain is an easy victim to all kinds of lapses and biases (p. 746). The research of Kahneman displayed that decision-makers also deal with cognitive biases. Another example was the framing effect which displays that how people react depending on how something is presented. Decision makers are looking to avoid risk when a problem is framed positively but seek risk when it is negative (Kahneman, 2003). Much of the work of Kahneman and Tversky was to suggest that the human judgement can be hindered by cognitive illusions. This identifies ways in which the intuitive thought is different from deliberate reasoning and decision-makers can be misguided by their systems (Kahneman, 2003, p. 717). Humans are subject to their cognitive functions and as Simon pointed out they are bounded by their rationality. Kahneman takes this a step further by outlining the ways in which humans and be deceived by their perceptions of the situations and identifies the way in which intuitive thought differs from deliberate meaning (Kahneman, 2003, p. 717). 2.3.2 Factors Decision-making theory was focused on components that are within the organization and those components that directly impact the decision-making process. However, the theory on decision-making has noted the limitations of the cognitive ability. Leaders assume that rationality is possible as long as they know all of the information available and can identify the options and outcomes. These options and outcomes cannot be known and as the literature is pointed out it falls into bounded rationality. In order to broaden their perspectives, decision-makers should collaborate with other resources. This section focuses on three components that might be able to aid organizations. In order to be more effective, enabling factors in the literature have been identified as potentially being able to assist in the decision-making process in order to become data-driven. This new wave of organizations migrating towards being data-driven is based on innovation. When looking at innovation and what is needed to adopt this approach, a framework has been identified. This connection to innovation is based on the technology-organization-environment (TOE) Framework from Tornatzky and Fleisher’s (1990) The Processes of Technological Innovation book that describes the implementation of innovation into organizations. The TOE framework represents the context influences of the adoption and implementations of technological innovation. This is an organizational level theory that explains the three elements of an organization to influence adoption decisions. For purposes of this study, the environment being evaluated is a data-driven environment. Therefore, the factor of data will replace the environment and the resulting framework is: data, technology, and organizations to be discussed as a lens for this research. Technology and data are growing exponentially but from the discussion on decision-making, it appears that human brains are wired for a more linear world. Take for example that teachers once taught kids the reason they had to learn math was because a calculator would never be at our fingertips at all times. Those teachers were proven wrong with the technology of smartt phones. This is just an example of how technology is changing the way humans function. Data science is not the science of data but instead, it refers to the efforts to develop a sophisticated and systematic analysis (Power, 2016, p. 349). The goal of data and technology is to combine the types of thinking along with humans. There are some problems that humans may not be able to solve alone but the idea would be to work alongside data and technology to solve problems. Individually, data, technology, and organizations and do not mean much, however, organizations are realizing that together they are changing the way organizations do business. The data-driven culture that organizations are looking to create, is built upon the value of obtaining, processing, sharing and using information in decision making (Power, 2016). This takes an adoption mindset to adopt the culture of being data- driven. Tank (2015) talks about the implementation phase which is taking that solution and putting it into action (p. 44). In this case, it is organizations that are analyzing data by way of technology and turning it into something that can help drive decisions in order to impact performance. Today we can take a routine job performed by humans put it into code read by a machine and replicate it. This section is an attempt to understand the main challenge of how organizations integrate the technology and data to work together in order to change the outcomes. Data There has become a rapid adoption of organizations using data to drive the decision-making process. Organizations see potential in the combination of analytics and data to manage operations of organizations (Power, 2016, p. 346). However, the right data is needed to be helpful in the process. Data must be analyzed to inform decision-making which is complex (Power, 2016, p. 347). This is the task of a data scientist. They support the decision-makers in a semi-structured decision situation which has become challenging because of the multiple data streams (Power, 2016, p. 350). Data is based on facts and numbers which is often generated and used to detect patterns and the results are prepared for use by decision-makers (Janssen et al., 2017, p. 340). The data sources can originate both internal and external to an organization. Data is often captured to collect as much data as possible because the value of the data is not fully known (Janssen et al., 2017,p. 340). The key aspect is taking the data and finding the value in it to make better decisions. Tank (2015) defines business intelligence as the process of turning data into information and then turning it into knowledge which is a process that involves getting data in and getting it out (p. 43). Boisot and Canals (2004) agree with the turning data into knowledge. Their research makes the case for distinctions between the data, information, and knowledge. People often use these terms interchangeably. However, the research indicates the need for distinction. Therefore the terms are defined by Boisot and Canals (2004) as follows: Data can be treated as originating in discernible differences in physical states of-- the-world – that is, states describable in terms of space, time, and energy (p. 46). Information constitutes those significant regularities residing in the data that agents attempt to extract from it (p. 47). Finally, knowledge is a set of expectations held by agents and modified by the arrival of information (p. 47) The iInformation contains facts based on the analysis of the data. Taking information as an input into decision-making, the authors identify the lack of information about the future and objective information which are key issues they wish to address (p. 46). Knowledge is processed by individuals and is related to facts of the data and interpretation of the informations and includes experience expereience and other observations. Now all information can be knownledge based on if an individual can understand the contextual meaning from the information (Boisot & Canals, 2004, p. 44). Figure X: Hierarchy of data, information, and knowledge Figure X represents the way in which the literature outlines data, information, and knowledge. Added is the organizational level in which they are categorized into. This can be seen as a hierarchy model with knowledge at the top as seen in Figure X. It is important for organizations that are looking to become data-driven in their approach to understand the difference in each level. To understand this concept, imagine the following example of receiving an encrypted message which you possess the key. The following information is extracted “the cat is in the bag”. Unless you have the contextual background, you will not understand the message. Kahneman (2003) illustrates this using another similar example but states “The ambiguity is suppressed in perception” (p. 701). If you understand the context as referring to a partner capturing a suspect, you are in a position to react adaptively. To understand the phrase is not the same as understanding the message, otherwise one might think of the literal meaning of a cat being in a bag. Only prior knowledge allows contextual understanding of the message. The message carries information that modifies the knowledge (Boisot & Canals, 2004, p. 47). This hierarchy represents how organizations can turn data into knowledge in order to become data-driven in their decision-making. Once you have the knowledge, you can more clearly define what action needs to be taken. Since data is the foundation of the hierarchy, it is important that leaders focus on good data to come up with accurate decisions to minimize the complexity of the decision-making process (Tank, 2015, p. 43). Organizations are focused on the use of data and turning that to knowledge. Tank (2015) states that leaders must focus on good data to come up with accurate decisions to minimize the complexity of the decision-making process (p. 43). More recently organizations are adopting the concept of “big data” due to the complexity of the data. To achieve timely and effective decisions, strategic approaches to decision-making need to be incorporated into organizations. Based on the literature, the term big data has evolved over the years. Power (2016) explaines explains that big data is defined as the 3 Vs: volume, velocity, and variety as seen in Figure X. Figure X: Definition of big data The idea is that data, big or small, can be a solution to organizations taking their data to knowledge in order to support the decision-making process. These characteristics are based on the overall rate of the data expansion for organizations and society as a whole (Power, 2016, p. 346). Data is changing the way in which organizations must compete. However, the concept of more data, does not always lead to better decisions and strategies. Data is expanding in volume, velocity, and velocity. Improving the tools and data provide part of the role of how organizations can help in problem- solving and enhance the decision-making process for leaders (Power, 2016, p. 354; Tank, 2015, p. 47)). Converting the data into knowledge is an important aspect and the literature acknowledges the significance of data in ensuring efficiency and effectiveness. Technology The process by which organizations adopt and implement technological innovations is important (Tornatzky & Fleisher, 1990). Allen Newell formed a partnership with Simon and together they developed information processes theories which later became the ‘thinkable machine’ also known as Artificial Intelligence (AI). This was to simulate human processes by using computers to thick, problem solves, and for use in concept fining, using computers for the means of testing theories by comparing human behavior with behavior predicted by theory and simulated by computers (Newell & Simon, 1965, p. 111). The increased demand for evidence-based decisions and the AI technology has improved the development of data- driven decision making. “Contemplating decision-making support options forces managers and technologists to examine issues of rationality, information culture, and decision support and analytics design and deployment” (Power, 2016, p.345). Organizations are implementing technology as a means to link data with the decision-making process. Simon built upon this theory by stating that there are decision aids for managers that are interactive with knowledge and intelligence being shared between humans and automated systems (Simon, 1987, p. 61) and that managers need to analyze situations systematically with the aid of modern tools when appropriate (Simon, 1987, p. 63). Simon (1987) explained that both intuition and analytical processes that complement each other in effective decision making. Intuition is based on judgmentement and experience with “no awareness of how the judgementjudgement is evoked” (p. 59). The manager with experience has knowledge that is gained and organized in terms of recognizable chunks and associated information (Simon, 1987, p. 61). The recognizable patterns are what offer predictability and can use technology to assist in identifying these patterns. “Increasingly, we will see decision aids for managers that will be highly interactive with both knowledge and intelligence being shared between the human and the automated components of the system” (Simon, 1987, p. 61). It is through this that Simon expressed that more needs to be explored on how to improve the decision-making process. In order to make the decisions, managers need to analyze problems systematically with the aid of modern analytical tools and having the skills to apply them when appropriate (Simon, 1987, p. 63). Historically, technical experts have made decisions for the customer (Kusiak, 2009, p. 444). Meaning that organizations have been designing products and services that the customer was expected to accept. Technology is changing that and allowing organizations to focus on data- driven decision making by looking at what the customers are seeking by understanding the customer experience. “The value of improving the decision processes is reducing time spent retrieving information and increasing response time to customers, which is beneficial to the company in terms of reduced costs and more satisfied customers.” (Tank, 2015, p. 48). Customer data can add customer centered value in order to create actionable decisions by managers that can be measured and refined as a continuous feedback process that leads to improvement and the development of new products or services. It was the introduction of technology that changed the way decisions were made and Newell and Simon recognized this in the 1960s. This review of the literature lays a foundation of why many organizational leaders should explore the value of utilizing technology and data to aid in the strategic decision-making process. This concept will be expanded upon for practitioners to gain a deeper understanding of the impact external data sources can have on the success of the organization. Organizations Earlier in the literature review on decision-making, much of the focus was on the individual making the decision. “An understanding of the decision-making process is critical not only for the explanation of individual behavior but also for the behavior of complex organizations” (Vroom & Yetton, 1973, p. 4). Chester Barnard laid the foundation for the advances that support the cognitive process and the impact that this has on the organization through the executive functioning. It is important to understand the role that organizations play as a factor in the process. The behavioral theorists of decision-making wanted to take a look at how leaders make decisions in the context of the organization. Simon collaborated with March and wanted to keep things simple and they concluded that organizations are a hierarchy that imposes pressures on the cognitive ability of those within (March & Simon, 1958). It can, therefore, be argued that the processes of decision-making when carried out by organizations are different from the same processes carried out by individuals in that organizational decision-making involves cognitive and social processes (Vroom & Yetton, 1973, p. 4). Therefore, it is important to understand how leadership is reflected in the social processes involving the organization. There isare a wide range of factors that influence decision-making and this makes the process complex and hard to manage. In situations where several influences are involved, it is hard for organizational leaders to oversee the entire process offrom adopting data analysis, implementing technology, and execution once a decision is made. 2.3.3 Performance Measurements Organizations are invested in making better decisions because the overall performance is critical for sustainability. However, measuring organizational performance can be difficult. There can be objective measures such as accounting data and subjective measures such as a manager’s perceptions involved in the performance measurements. Measuring performance is a complex process so the literature review will evaluate different performance frameworks. Grigor et al. (2010) defined performance as “a state of competitiveness of the organization, reached through a level of efficiency and productivity which ensures a sustainable market presence” Performance measurement represents results quantification of activities undertaken within an organization over a period of time (p. 951). The important part about measurements is they should accurately produce appropriate data to enable feedback for informed decisions. A measurement is a set of metrics through mechanisms of data collection and used to support an organization. In the 1980s the organization was viewed as belonging to shareholders. Shareholder theory uses the shareholder return to measure overall organizational performance (Hubbard, 2006, p.178). In the 1990s, the organizations were seen as having responsibilities that span to a wider set of people seen as the stakeholders. These included groups such as employees, customers, suppliers, governments, industry bodies, local communities and the like (Hubbard, 2006, p.178). This is an assessment of the relationship between organizations and the performance. Data alone does not provide an effective basis for action. It is the information that the data provides to decision-makers that produce insights. These insights produce metrics that organizations can use to monitor and analyze the performance and then take action and make decisions. Thus, organizations use this analysis of data to provide insights to their leaders. Measures can be organized into two categories which are lagging and leading. Lagging measures (outcomes) tell organizations what has happened; leading measure (performance drivers) predict what will happen (Evans, 2004, p. 222). Leading measures are more difficult to determine. An example to that Evans (2004) used is that a customer survey that produces results about recent transactions might be a leading indicator for retaining customers. Meaning- based on the data they think they will retain 5 of the customers. However, the customer retention actual results and numbers are lagging measures. Meaning they thought they would retain 5 but maybe they actually retained 6. Kaplan & Norton (1996) created multiple measures that incorporate the complex set of cause-and- effect relationships among critical variables including the leading and lagging measures as well was a feedback loop that describes the trajectory of the strategy (pp. 64-65). Along with those measurements, there is the concept of key performance indicators (KPIs) for non-financial results. These key performance indicators measure the performance of a specific process, and whether it is done right. Indicators that can be measured would be things like user acceptance, employee satisfaction, quality assurance, and testing. IF measurements are linked to a specific activity within the organization, this will help in understanding what changes should be made (Tank, 2015, p. 47). Organizations must decide what they want to measure and how they will measure it using these concepts. In order to figure out what and how to measure, an organization must have a strategy. A strategy is a set of hypotheses about the cause-and-effect which is a sequence of if-then statements (Kaplan & Norton, 1996, p. 65). Actions depend on these understandings of a cause and effect relationship (Evans, 2004, p. 220). This is where the analysis plays a role. Organizations conduct an analysis in order to gain an understanding of performance. Example include: 1) improvement trends in key operational performance indicators 2) cost trends relative to competitors 3) how product and service quality improvement correlates with the key customer (Evans, 2004, p. 220). Figure X represents what this feedback process might look like when organizations are trying to assess the relationships by measuring performance outcomes. Figure X: Feedback process From the view of the practitioner, they point to the need for better approaches to analyzing performance outcomes and the need for incorporating statistical techniques, competitive comparisons, and benchmarking in the review process (Evans, 2004, p. 230). The design of a measurement system is paramount to aligning the organizational operations with the overall strategy. A performance measurement framework where organizations seek to measure the right things based on the operations and strategy is the triple bottom line. This is based ion stakeholder theory but has a wider perspective. It is based on the idea that a firm should measure performance in relation to stakeholders beyond with whom it has direct relationships. The triple bottom line adds social and environmental measures of performance. However, this is not straightforward. While things like market share might be easy to quantify and measures that are developed by one organization can transfer to others, the social and environmental performance are more unique and can be difficult to quantify (Hubbard, 2006, p.180). Figure X represents the three components. Social and environmental performance isare unique to organizations or industries and they are difficult to quantify. It is known to be a complex system of measurement for organizations since sustainability meets the needs of the present without compromising the ability of future generations to meet their needs (Hubbard, 2006, p.180). However, those that seek sustainability as an organizational strategy, this performance measurement tool can be very helpful. Figure X: Triple Bottom Line The Balanced Scorecard is a measurement system by Robert Kaplan and David Norton and is also based oin stakeholder theory (Hubbard, 2009, p. 179). The Scorecard has four perspectives; financial, customer, internal business processes and learning and growth (Kaplan & Norton, 1996, p. 53). Many people think of measurement as a way to control the behavior and to evaluate performance. The balance means considering internal and external perspectives, long and short-term objectives, and financial and nonfinancial measures. The balanced scorecard is unique for each organization but isare based on four perspectives: 1. Financial- defines long- run objectives 2. Customer-customer and market segments in which organizations will compete 3. Internal business process- enable the organization to deliver on value propositions to customers and satisfy shareholders expectations of financial returns 4. Learning & Growth- comes from three sources: people systems and organizational procedures for long- term growth and improvement (Kaplan & Norton, 1996, pp. 54-63). The measures on a Balanced Scorecard is meant to articulate the strategy of the organization and to help align individual, organizational and cross- departmental initiatives to achieve the common goal (Kaplan & Norton, 1996, p. 53). Grigore et al. (2010) add to this by stating that the Balanced Scorecard offers the opportunity for the organization to clarify the vision and strategy and to turn that into action. This provides feedback on internal processes and external achievements to improve the performance (p. 952). It is intended to find additional measurement beyond financial methods. The example in Table X shows some possible measurements that would be used on a Balanced Scorecard according to the literature: The organizational performance is the core of organizational survival. The aim of the research is centered on how organizational performance can be enhanced, shaped and sustained to help the organization improve profitability and long-term survival (Singh, Darwish & Potocnik, 2016, p. 214). The concepts of sustainability change the way organizations think and are forcing them to re-evaluate their approach to measuring organizational performance Organizations are looking for ways to measure sustainability which means that organizations find ways to meet their needs without compromising the ability of the future. (Hubbard, 2006, p. 180). These measurements are incorporated through data and technology can be implemented into the decision-making process and its part of the learning process. Hubbard (2006) used the triple bottom line and the balanced scorecard to develop the Sustainable Balanced Scorecard. This method takes the four components of Kaplan & Norton’s Balanced Scorecard and added the two components of the Triple Bottom Line (p. 187). The idea is to rate the performance of each element against the expectations which imply improvement against past performance, best practice or the industry average. What is noted as important is to pick indicators that capture the essence of the organization based on the strategy thus aggregating the measures with the possibility of applying different weights (Hubbard, 2006, p. 188). What is important is that performance measurement can be easily understood by leaders and analysts. Thus, it will be more likely to be used as a performance measurement tool and that is what the Sustainable Balance Scorecard aims to do since it is based on existing recognizable performance measures. The measurements are a way to articulate and communicate the strategy of the business and to align all of the parts of an organization to a common goal. Organizational feedback happens through the measuring of performance outcomes and is linked objectives and measures that are reinforcing through a cause and effect. Kaplan and Norton (1996) point out that it is the translation of the organizational strategy to a linked set of measures that define long-term strategic objectives, as well as feedback on those objectives (p. 68). This feedback facilitates organizational learning with the ability to adjust their processes to develop a better organizational strategy. It is from this literature we can measure the impact of data- driven decision-making on organizational performance by looking at key indicators outlined. 2.4 Thesis statement Data driven decision-making impacts organizational performance positively. 2.5 Conceptual Framework and Narrative The Conceptual Framework of Data-d Droiveen Decision Making and Organizational Performance Figure X, illustrates the relationship between the key factors of data- droiven decision-making and organizational performance as identified in the literature. The relationship of the factors has been identified from a scholar- practitioner- based research approach. There are three components that must be used in data- driven decision making which are the data, technology and the organization. In order for the analysis of data to take place, the overall concept needs to be adopted. This adoption flows into the implementation of technology. Implementation takes the adoption a step further by actually putting concepts of adopting the data mindset into action. These flows into the organizational decision-making concept. The literature shows that traditionally decision-making has been analyzed from a human standpoint which can be full of misconceptions and mistakes. Organizations are now trying to come together with the use of data and technology in order to drive decision-making in hopes that they can impact the organizational performance positively. However, this requires making a decision and executing it in order to see the outcomes. This performance outcomes can be measured by indicators and metrics that then feed back into the organization and is used to communicate the strategy. 2.6 Chapter Summary Chapter 2 discussed the literature in decision-making, enabling factors that aid in the process of data driven and organizational performance. From the literature, a conceptual framework was developed. The aim of the literature review is to provide a theoretical basis of the research being studied in an attempt to answer the research question. There are many factors that influence decision-making that makes the process hard to manage. Organizations can utilize technology as a tool to aid in data- driven decision-making to include the customer experience in the innovation processes. Data can offer organizations the possibility of obtaining unparalleled insights. This review of the literature lays a foundation of why many organizational leaders should explore the value of utilizing data. This paper, in future chapters, will aim to identify the mechanisms of the data- driven decision-making process based on the executional outcomes and understand what impact this will have on organizational performance. Chapter 3: Methodology 3.1 Introduction to the Chapter The purpose of this research is to examine the impact of data- driven decision-making on organizational performance. This research is based on the synthesis of concepts that are associated with data, decision-making, and organizational performance arising from multiple areas of studies. Evidence- based research (EBR) brings together prevailing prior research literature in a systematic way using rigor and transparency (Gough et al., 2012; Petticrew & Roberts, 2006). This chapter is divided into seven sections. The first section introduces the emergence of research designs on evidence-based management. The next section includes a discussion of how Context, Intervention, Mechanism(s), and Outcomes (CIMO-logic) apply to this research design. The third section of this chapter outlines the common stages of a systematic review which is followed by three sections discussed in order of the literature search, inclusion and exclusion criteria, and the quality assessment process. The seventh section is a discussion on the synthesis methodology. This will be followed by an expert panel discussion which will explain the selection process. The final section will review the chapter highlighting the important components and summarizing the chapter contents. 3.2 Evidence Based Research & Management There are a number of literatures from various research fields on the subject of data- driven decision-making and organizational performance, however, there are no known attempts to synthesize the literature into a scholar-practitioner model that provides actionable outcomes to be used. Evidence-based research can be used to inform decisions by systematically selecting studies that are synthesized into a single report. This does not provide answers, but it relays what is known and not known about the research question based on the best available evidence (Briner, Denyer, & Rousseau, 2009, p. 27). This study follows the principles of evidence-based research through a systematic search of the academic literature. Utilizing processes to document, code, and synthesize the research, a systematic review process was selected. Systematic literature reviews are used to make sense of large amounts of information and a means to contribute answers to questions (Petticrew & Roberts, 2006, p. 2). Using the methodology presented in this research, a scholar-practitioner can mean the demands of the scholar-practitioner paradigm. Rousseau (2006) defined evidence-based management as translating principles based on evidence into practice for organizations. This is derived from research evidence and translates them into practice that can solve an organizational problem (p. 256). Briner et al. (2009) provide a framework that conceptualizes the evidence-based management. Figure X represents the four key elements into a diagram that reflects the evidence-based management process (p. 22). This offers a practice for a science-based approach to management. The overlapping relationships between external evidence, stakeholder preferences and values, practitioners experience and judgement, and the organizational actions and circumstances meets the needs of decision making. Figure X: The Evidence-Based Management Framework Used in Decision-Making. Adapted from “Evidence-Based Management: Concept Cleanup Time?” by R. B. Briner, D. Denyer, and D. M. Rousseau, 2009, Academy of Management Perspectives, 23(4), p. 22. Copyright [2009] by Academy of Management. Denyer and Tranfield (2009) reformulated a medical science approach called the PICO and adapted it to the social science called the CIMO which is context, intervention, mechanisms, and outcomes which focuses on the mechanisms by which change occurs. An example of these components they formulated into a question would be “Under what conditions (C) does leadership style (I) influence the performance of project teams (O), and what mechanisms operate in the influence of leadership style (I) on project team performance (O)?” (p. 682). Using the CIMO-logic for this study, the context (C) refers to organizational environments, interventions (I) refers to the decision-making, mechanisms (M) refers to the factors of the data-driven approach that will be generated through the research literature, and the outcomes (O) is the measurement of organizational performance. Using these elements, the contextual approach this research is formulating the problem as under the organizational environment of decision making, the attempt of this research is in identifying the mechanisms of data- driven decision making which impacts the organizational performance. This research follows the common stages Figure X in a systematic review proposed by Gough, Oliver, and Thomas (2012) which consists of “review initiation; review question & methodology; search strategy; description of study characteristics; quality and relevance assessment; synthesis and using reviews” (p. 8). Figure X. Common stages in a systematic review. Adapted from An Introduction to Systematic Reviews (p. 8), edited by D. Gough, S. Oliver, and J. Thomas, 2012, London, England: Sage. Copyright 2012 by SAGE Publication Ltd. The results of the methodology process will inform an update for the data- driven decision-making framework that addresses factors of organizational performance. Given the importance of the decision-making process and the increasing number of organizations interested in understanding the impact that becoming data- driven has on organizational performance, using existing literature on this topic could be beneficial to see what impact it is having on organizations. This research uses evidence-based management to assess the research assumptions of data- driven decision-making and attempts to understand the impact it has on organizational performance. 3.3 Search Strategies In order to follow an evidence-based research approach, the literature search started with the identification of search words that are framed by the research question. The identification of key words was conducted with assistance from an expert librarian with experience in searching academic databases and the following search string was developed: "data- driven decision making" AND (firm* OR organization* OR business* OR company* OR corporate*) AND (perform* OR effective* OR success*) In order to gain a comprehensive view of the available literature, the following databases were identified: Science Direct, ABI/Inform, Business Source Complete, and Computer and Applied Science Complete. The keyword search string was entered into each of the databases and the articles were retrieved based upon the criteria of being in a scholarly peer- reviewed journals that were of the English language with the dates from January 2010-May 2018. The titles and abstracts were scanned for the three components of: (1) data, (2) decision making, and an (3) outcome. After the initial title and abstract scan, the remaining articles were reviewed for a full- text review. The articles had to meet the inclusion criteria outlined in Table X. Any of the research that did not meet the inclusion criteria and fell under the exclusion criteria outlined in Table X, was excluded from the study with reason identified in Appendix X. Inclusion Criteria Is the focus of the organization to use data in the process? Does it include the data in the decision-making? Is there a performance outcome? Yes Yes Yes Exclusion Criteria Category Type Document Type Theses/Dissertations, editorials, news articles, magazine articles, advertisements, wire feeds, trade journals, lecture notes Language Non-English Primary Population Non-Generalizable Secondary Population Individual Outcomes Table X below illustrates the search results of the articles retrieved from each database as well as the number of articles excluded. Database Articles Retrieved Duplicates Removed Excluded on Title & Abstract Analysis Excluded Full Text Included Science Direct- ABI/Inform- Business Source Complete 15 9 4 1 1 Computer & Applied 15 3 7 3 2 After the database search was complete, there were a total of 24 articles. Those articles references were scanned for additional articles that might relate based on the titles. In order for the articles to be used, the requirement was that they appear as a reference in 2 or more of the articles and the inclusion and exclusion criteria wereas applied in the same way as the articles found using the search method. Using the snowball approach, 10 more articles were added to the research and the references appear in Appendix X. A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) represents the references retrieved in the review (Gough et al., 2012, p. 85). The final PRISMA is in Figure X and represents the number of relevant studies. This process resulted in a total of 34 articles being included for quality assessment for the relevance and rigor of each article. Figure X. Flow Diagram of the Systematic Review Process to Determine Inclusion and Exclusion of Studies. From “Preferred Reporting Items for Systematic Reviews and Meta- Analyses: The PRISMA Statement,” by D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, and The PRISMA Group, 2009, Public Library of Science, 6(6), p. e-. Copyright [2009]. American College of Physicians. 3.4 Quality Appraisal of Literature As a part of ensuring that the most appropriate studies were used, the articles went through a quality assessment (Gough et al., 2012). The quality appraisal process was created to ensure that the articles selected met the standards of scholarship based on the following criteria adapted from two quality assessment measures of CASP and TAPUPAS: (1) transparency (2) utility (3) accessibility (4) methodology (5) generalizable (6) specificity (Pawson Boaz, Grayson, Long, & Barnes, 2003; CASP, 2018). Each article received a score of 1-4 for each of the criteria that areis outlined. 1= low quality, 2=average quality, 3=good quality, 4= excellent quality. The results of the quality assessment can be seen in Appendix X. 3.5 Synthesis Methodology Once the articles were collected, the data needed to be extracted for results. At first, the basic information was recorded and organized into a database with the following list in Table X of data to be extracted. Author(s) Year Country Industry Title Database Journal Method of data collection Methodology Purpose/Focus/Aim Thematic synthesis is a technique that the reviewer conducts a synthesis that is systematically grounded in the studies it contains (Gough et al., 2012, p. 193). For this research, the thematic synthesis process created by Cruzes & Dyba, (2011) was used to develop a multi-step coding process to create high-level themes as seen in Figure X. Figure X. Thematic synthesis process. Adapted from: Recommended Steps for Thematic Synthesis in Software Engineering (p. 8), by D. S. Cruzes and T. Dybå, 2011, Proc. Empirical Software Engineering and Measurement (ESEM), 2011 International Symposium. The first step in the data extraction used in this research was to label the text into codes based on the ideas presented in the conceptual model presented in the earlier chapter. This descriptive text was then categorized into themes. In order to create a higher order, the themes were integrated into analytical constructs established to create a model that relates to the research question. These themes build a common metric to translate the findings. These constructs use primary data and the analysis of the author in the coding process (Gough et al., 2012, p. 145). Table X presents the codes and their association with the themes. Chapter 4 will provide the updated conceptual framework which is the graphical depiction of the findings found in this research. Keywords- Initial Codes Themes Analytical Adoption/Implementation     Correlation (AI-C) Relation Techniques Drivers (AI-D) Motivators Decision Making     Data-Driven (DM-DD) Data-Driven Application Tactical (DM-T) Hierarchy Operational (DM-O) Strategic (DM-S) Enabling Factors-Data     Micro (EF-D-Mi) Spectrum Collection Meso (EF-D-Me) Macro (EF-D-Ma) Descriptive (EF-D-D) Analytics Predictive (EF-D-Pd) Prescriptive (EF-D-Ps) External (EF-D-E) Sources Internal (EF-D-I) Raw (EF-D-T) Types Processed (EF-D-P) Enabling Factors-Organization     Process (EF-O-P) Framework Strategy Structure (EF-O-Su) Knowledge (EF-O-K) Resources Leadership (EF-O-L) Enabling Factors-Technology     Architecture (EF-T-A) Investment Infrastructure Software (EF-T-S) Functions (EF-T-F) Capabilities Execution     Action (E-A) Action Practice Feedback     Mechanisms (F-M) Assessment Change Performance     Correlation (P-C) Association Outcomes Dependence Variables (P-DV) Measure (P-M) Metrics 3.6 Expert Panel Review To ensure that perspectives of both academic scholars and practitioners were present, an expert panel was constructed to review the dissertation and provide independent feedback. These subject matter experts were identified based on their positions held within an organization with the requirement being that their focus was data- driven decision making. Using subject matter experts, their focus was to offer feedback into the assessment and impact of the question being asked and the implications it may have for practitioners (Briner et al., 2009 p. 23). The feedback was reviewed for the inclusion of the suggestions provided by the subject matter experts. The following experts provided comments and suggestions: INSERT PANEL NAME/BACKGROUND Suzanne Goebel Beth Partridge- Founder/CEO/Chief Data Scientist Beth brings nearly 30 years of executive-level experience in manufacturing, product engineering, quality control, technical support, and operations. Her formal training includes a BS in Electrical Engineering, and a Master of Information and Data Science from UC Berkeley. Beth is that rare executive with a powerhouse combination of natural leadership, deep technical experience, and impeccable execution skills. milk+honey is born from her hard-won understanding that success in business now requires a deep commitment to data-driven decision making, and development of a culture of experimentation and innovation at all levels of an organization. She is filled with excitement about the data revolution and the profound transformation that’s upon us. Justin Sloan Besa Bauta 3.7 Chapter Summary The methodology described in this section is used to identify the evidence. The search process identified over 500 articles but with the application of inclusion and exclusion criteria, the results were narrowed to 34 articles. The core terms identified in the original conceptual framework were the foundation of the coding process producing an output of high-order themes. Chapter 4: Findings: Analysis and Discussion 4.1 Introduction 4.2 Findings: Evidence for Research Question Based on the analysis of the articles this research presents the following requirements for successful data- driven decision making as an eight-step process which will serve as the outline for the findings section of the research. Then this paper will take the data- drivenn decision- making process and see what kind of impact it has on the performance of organizations in which the researchers studied. The steps of data- driven decision making areis as follows: 1. problem identification, 2. planning, 3. data analysis, 4. decision making, 5. action, 6. measure, 7. assessment 8. feedback (Akter et al., 2016; Dutta & Bose, 2015; Lee & Yoo, 2012; Long, 2018; Pedersen & Wilkinson, 2018). 1. Problem Identification A sound understanding of the business problem is absolutely critical in order to understand the possible improvements that can be realized from the implementation data and the insights generated from it (Dutta & Bose, 2015, p.294). Data assists in the decision-making process through the analyzing of current problems creating predictive models to forecast future threats and opportunities and analyzing business processes based on past and present data in order to enhance organizational performance (Sun et al., 2017). In defining the problem, the researchers identified multiple issues that data can help to solve as well as positive impacts data can make on an organization: 2. Planning- strategy, design, resources, and knowledge The key concept of the planning phase is that organizational practices are reflected by developing a strategy and a policy to guide the data environment and designing the structure and processes to enable the data processing. The data strategy is developed to guide the use of the data by relevant organizational structure and processes (Cao et al., 2015). Key factors suggested by Holsapple et al., (2014) are “awareness and commitment to the organization’s vision, mission, and strategy; an analytics-friendly culture; a management philosophy that understands and supports the use of business analytics” (p. 139). The research states that the larger amount of synchronization between data analytic capabilities and business strategies increases the collaboration among different functional units and positively impacts organizational performance. (Atker et al., 2016, p. 120). The organizational design includes the designing of the organizational structure. The structure determines what information will be provided to who and thus controls the organizational activities. This structure refers to what Cao and Duan (2017) refer to as the horizontal and vertical differentiation, mechanisms of coordination and control, formalization and centralization of power (p. 876). This structure facilitates information processing to enable decision makers to process more data and inform decision making. Organizations that embrace the data- driven approach find that they have to change the design of the organization. This is because data shifts the power away from leadership and gives it to whoever has access to the data with the means to analyze it to make the decisions (van Rijmenam et al., 2018, p. 2). Comprehending this takes the understanding of knowledge and how the knowledge is gained and transferred in the organization. The knowledge can be defined a set of concepts, including product descriptions, business processes, operational plans, production technologies, techniques and standards, personnel structure and experiences, enterprise regulations and culture, and environment dynamics (Long, 2018, p. 59). The research states that organizations that provide more people with access to knowledge through data that the power is distributed more equally and it empowers the organization (van Rijmenam et al., 2018, p. 4). Long (2018) refers to this as knowledge flow as being the possession, flow, interchange, and utilization of knowledge among personnel (p. 59). The research suggests that the most valuable way to transfer data knowledge is to use data visualization techniques (Atker et al., 2016; Bayrak, 2015; Cao, & Duan, 2017; Chang et al., 2014; Chen et al., 2012; Delen & Zolbanin, 2018; Dutta & Bose, 2015; Gunasekaran et al., 2017; Gupta & George, 2016; Holsapple et al., 2014; Khatri, 2016; Lavalle, 2011; Roßmann et al., 2018; Saggi & Jain, 2018; Sharma et al., 2014; Sun et al., 2017; Wang & Byrd, 2017; van Rijmenam et al., 2018; Weiner, 2015; Zhong et al., 2016). Knowledge is a powerful resource that plays a key role in the success of data- driven decision making. It is important to align the organizational resources with short- and long-term business strategies during the planning process. Resources refers to the tangible and intangible assets and the ‘capabilities’ are subsets of the firm’s resources which are non-transferable and aim to enhance the productivity of other resources (Atker et al., 2016 p.115). Tangible resources can be sold or bought in market meaning things like financial debt or equity and physical assets such as equipment and facilities (Gupta & George, 2016). Intangible resources would be things such as information and knowledge (Gunasekaran, et al., 2017). Tangible resources are readily available to all firms however they will not provide an advantage on their own but they are required to create capabilities. This research draws from the literature that organizations must build capabilities through the orchestration of their resources. (Gupta & George, 2016). The resources are valuable when it generates something of value to customers that competitors cannot achieve (Erevelles et al., 2016). This is a part of the strategy of organizations that are looking towards data. The literature lays out a clear association between resources as an independent variable and the firm’s performance as a dependent variable (Gupta & George, 2016). Thus, the research argues that the firm performance in a is enhanced only when the capabilities are valuable, rare, imperfectly imitable and when the organization exploits the potential of resources (Atker et al., 2016, p. 115). The absorptive capacity or the organization’s ability to learn, should be a high priority of organizations to improve the knowledge flow by utilizing resources. The absorptive capacity serves as a complement to technological resources in creating value and emphasizing that obtaining capabilities from the use of technology to increase organizational performance cannot be guaranteed unless organizations have sufficient capacity to identify, absorb, transform and exploit the knowledge that is generated (Wang & Byrd, 2017, p. 526). Organizations have to make considerable effort to acquire and internalize new knowledge from technology. This is the key in the planning process to integrate with existing knowledge and improve knowledge flow for its further sharing to achieve better performance in a turbulent environment (Wang & Byrd, 2017, p. 524). Based throughout the research, the following organizational traits in Figure X are correlated to being successful for organizations that implement data into their decision making and therefore should be highly considered during the planning process: 3. Data Analysis- generating insights the advances in information technology (IT) have enabled businesses to develop innovative ways to collect data from both internal and external sources (Cao et al., 2015, p. 2). These data sources Saggi & Jain (2018) categorize into three classifications and seen in Figure X: Machine-Generated Data: The machine-generated data comes from several computer networks, sensors, satellite, audio, video streaming, mobile phone applications, and the prediction of security breaches. Human-Generated Data: It can be collected by people, for example: identification details having their name, address, age, occupation, salary, qualification etc. Whereas, real streaming data can be generated by various files, documents, log files, research, emails, and social media websites such as Facebook, Twitter, YouTube, LinkedIn. Business-Generated Data: The volume of business data of all companies across worldwide is estimated to double every 1.2 years such as transactional data, corporate data, and government agencies data. When Business intelligence of analytics is discussed, it means: value (does the data contain any valuable information for my business needs?), visibility (focus of insight and foresight of a problem and an adequate solution associated with it) and verdict (potential for decision-makers based on problem, computational capacity and resources) within the business intelligent domain (p. 768) Figure X: Data sources Data-driven decision-making methodology includes a complex system research perspective, which derives macro knowledge and rules from the micro operational data to support decision making (Long, 2018, p. 57). Chang et al., (2014) points out that depending on the question or the problem an organization is trying to solve, data granularity is important (p. 72). Technology has expanded the data availability and this newly available data has permitted a more detailed analysis. This data granularity is a reference for mapping analysis to position decision problems for correlation analaysis to adjust critical parameters for operational performance improvement (Long, 2018, p. 58). This granularity is categorized into three levels of data: Micro-data The least aggregated level of data in very large data sets, resulting from the capture of technology-mediated human, social, machine and physical settings; in this category, atomic data reflect the least aggregated relevant level for data capture, such as tweets in Twitter analytics for an individual person, channel changes in digital set-top box viewership pattern tracking for a viewer, blog posts by individuals, sensed activities by digital sensors in the so-called “Internet of Things”, or clickstreams in Internet session tracking for a user, etc. Meso-data The mid-level of data aggregation in very large data sets, resulting from the collection of data that are a level-up from micro-data in terms of the kinds of information that is captured. So, for example, while capturing the specifics of a user's tweets may constitute micro-data, examining the extent of their tweeting and re-tweeting behavior over time, and in response to specific issues or the tweets of other users will produce different kinds of information. The data are still very large in the sense of potentially being society-wide in their coverage. Macro-data The most aggregated level of data in large data sets describes: the market, regional or geographic area; the industry or economy sector; or the national and international levels. Macro-data on patterns of electricity use among the cities and towns of a country, or of the long-distance phone calling patterns of people in different regional area codes, and so on are aggregated at that level. (Chang et al., 2014, p. 72) Figure X: Data spectrum While organizations are primarily focused on the use of data to generate insights as discussed previously, the research proposes that they also need to understand the roles of design and storage and processes and people in data management. These three elements can be seen in Figure X which are; use, design, and storage, and processes and people, developing the data triad (Khatri, 2016). Based on the uses explained to this point, the design and storage refer to the technology that can be used to analyze the data during the lifecycle. Khatri (2016) identifies the five requirements to the design and storage of data to be an organizational asset. 1. Acquired and managed throughout its lifecycle; 2. Stored effectively; 3. Identified, defined, and understood; 4. Quality controlled; and 5. Consistently accessible even if the individual data stores may have been designed independently (p. 678). Figure X: Design and Storage of Data Data analytics processing is a matter of strategy for organizations. There are sophisticated solutions being developed to solve complex problems based on the capability to process large amounts of data. Analytics describes the application of statistics to past data, with the aim to identify patterns which eventually enable the forecasting of future behavior to some extent (Kache, 2017, p. 11). Following in Figure X are the three types of analytics- descriptive, predictive and prescriptive, that organizations can use to gain the insights they are looking for in order to answer the questions what, why, and how. Figure X. 3 Types of Business Analytics. Adapted from The analytics paradigm in business research Reviews (p. 189), Delen, D., & Zolbanin, H. M. (2018). Journal of Business Research. The following Table X follows up with the descriptive, predictive and prescriptive analytics by summarizing how the literature defines each one. Analytics has long been studied but the technicaltechnological aspects are rooted in the decision support systems and provide a powerful mechanism for transforming data through systems that help organizations improve their performance by finding solutions to their problems. Feedback mechanisms are an integrated element in data analytics’ algorithms and to make predictions more accurate over time, these are continuously compared with outcomes as close as possible to real- time (Pedersen & Wilkinson, 2018, p. 199). Definition Reference Descriptive Descriptive analytics, also called business reporting, addresses what happened, and what is happening? Sun et al., 2017 It does not provide recommendations on what to do moving forward; used to understand the environment and to discover patterns in customer behavior or market trends van Rijmenam et al., 2018 Drills down into data to uncover details such as the frequency of events, the cost of operations, and the root cause of failures Bayrak, 2015 Uses business reporting and web analytics to describe the context of and trending information on past or current events Cao & Duan, 2017 Examples: Standard reporting, ad-hoc reporting, dashboards, querying, and drilling down Saggi & Jain, 2018 Predictive forecasting trends by providing a business solution to what will happen, and why will it happen Sun et al., 2017 Uses a variety of models and techniques to predict future outcomes based on historical and current data Bayrak, 2015 Top-performing companies are more likely than bottom-performing companies to use predictive analytics more extensively Cao & Duan, 2017 Uses machine learning and algorithms to find patterns and capture relationships in multiple (un)structured data sources to create foresight van Rijmenam et al., 2018 Techniques include: regression, decision trees, neural net, and support vector machines Khatri, 2016 Prescriptive Offers recommendations on how to act upon predictions to take advantage of seized opportunities and, potentially, (re)align assets to transform businesses van Rijmenam et al., 2018 Offers optimal solutions or possible courses of action to help users decide what to do in the future; continually re-predicts and automatically improves prediction accuracy by importing and incorporating new data sets (a combination of structured and unstructured data and business rules) Want & Byrd, 2017 Mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance Bayrak, 2015 Optimization, model management, and interactive data visualization to prescribe one or more courses of action and shows the likely outcome of each decision, providing answers to what should we do Cao & Duan, 2017 Determine high-value alternative decisions–using optimization, scheduling, queueing, and like methods–given objectives and a constrained set of conditions and resources Khatri, 2016 4. Decision-Making- information processing Data can be used to surface important information and support decision making like never before. It is hard for people to change from making decisions based on experience or intuition to makeing them from data (LaValle et al., 2011). 19 of the 34 articles listed important factors that were required for the adoption of data- driven decision making into organizations. 16 of the 19 articles (84%) identified organization and/or technology. 14 of the 19 articles (74%) recognize them BOTH as being the important factors in the data- driven decision- making environment. The analytics will provide the insight while the environment ensures that the insight is used to support the decision making with maximum effect (Cao et al., 2015, p. 878). The data-driven environment will lead to the development of information processing capabilities, which will have a major impact on decision-making effectiveness (Bayrak, 2015, p. 3). It is hard for people to change from making decisions on personal experience to making them from data. However, when everyone sees how it can contribute to the goal, a significant effort is easier to justify and people across all levels are better able to support it (Lavalle et al., 2011, p. 25). Decision making is done on three levels as seen in Figure X: strategic, operational, and tactical (Bayrak, 2015, p. 230). Figure X: Hierarchy of Decision Making 5. Implementation- acting on insights Insights alone are not enough. Actionable insights are what isare needed. These insights provide new thought processes that drive decisions and promote positive action. However, execution can be met with some obstacles that make it hard for organizations to overcome. All organizations face challenges when it comes to data- driven decision making. However, the success of an organization depends on the response to these challenges. Based on the articles, the following Table X lists the challenges of data driven decision making into three main categories of data, technology, and the organization. Challenge Reference Data The Complexity of big data involves dealing with data that is high in volume, variety, and velocity it also includes the complex types, structure, and pattern (Bayrak, 2015 Cao et al., 2015; Delen & Zolbanin, 2018; LaValle et al., 201; Saggi & Jain, 2018) The Noise level in real- world environments such as irrelevant data (Chang et al., 2014; Schildkamp & Kuiper, 2010) Multiple sources of data (Chang et al., 2014) Information overload (Schildkamp & Kuiper, 2010) Access to relevant data which is valid, accurate and timely that coincides with the needs (Schildkamp & Kuiper, 2010; Kache & Seuring, 2017; Fosso, 2015) Collection (Saggi & Jain, 2018) Integration of internal and external data (Gupta & George, 2016) Technology Capabilities and infrastructure required to process large amounts of data (Kache & Seuring, 2017; Fosso et al., 2015) Integrating (Bayrak, 2015) Processing of real- time information (Kache & Seuring, 2017) Advances by significantly accelerating the speed by which data is generated making it hard to keep up (Roßmann et al., 2018) Organization Culture (Bayrak, 2015; LaValle et al., 2011) Talent management and human resources in finding analytical talent and lack of skills or expertise (Akter et al., 2016, 2016; Bayrak, 2015; Kache & Seuring, 2017) Trustworthy and understandable to employees (Fosso et al., 2015; Schildkamp & Kuiper, 2010) Aligning with the capabilities and emerging tactics (Akter et al., 2016, 2016) Collaboration and involvement of employees (Schildkamp & Kuiper, 2010) Training (Schildkamp & Kuiper, 2010) Strategy and objectives that include a clear vision, norms, and goals (Kache & Seuring, 2017; Schildkamp & Kuiper, 2010) Linking to the knowledge economy (Sheng et al., 2017) Leadership lack of understanding, data sharing managerial support (Gupta & George, 2016; Saggi & Jain, 2018; Schildkamp & Kuiper, 2010) Development of business processes in the initial phases (Fosso, 2015) 6. Measure-metrics Data can be a powerful tool but it has limits. So much depends on the context of what is measured and how it is being measured. Insights are used to develop measures of key performance indicators (Erevelles et al., 2016). These key performance indicators are extracted from the reporting services (Gupta & George, 2016). The most commonly used measure of performance in the literature, is multifactor productivity. computed by relating a measure of firm output such as Sales or Value Added, to firm inputs such as capital, labor, and information technology capital or labor (Brynjolfsson et al., 2011). Weiner etr al., (2015) case study showed that because the employees had access to their performance metrics that related to their responsibilities, accountability was increased. This was because they got in the habit of monitoring their performance on dashboards, and they began hitting targets on the metrics that pertained to them (p. 327). Brynjolfsson et al., (2011) pointed out that accounting measures such as return on assets, return on equity, and return on sales have some weaknesses in capturing firm performance: 1) they typically only reflect past information and are not forward- looking; 2) they are not adjusted for risk; 3) they are distorted by temporary disequilibrium effects, tax laws, and accounting conventions; 4) they do not capture the value of intangible assets; 5) they are insensitive to time lags necessary for realizing the potential of organizational change (p. 7). 7. Assessment Executives want better ways to communicate complex insights so they can quickly absorb the meaning of the data and act (Lavalle et al., 2011, p.23) Data visualization is a perfect way to cure the blindness. It converts the raw data into meaningful information through visual presentations (Zhong et al., 2016, p. 584). This is often employed for the continuous monitoring of the business performance and measured by the metrics or key performance indicators. These metrics are based on a scorecard. These dashboards that are used to monitor the strategy are performance management systems (Khatri, 2016, p. 685). Two mechanisms are often used to interpret the data: - visualization and modeling. data visualization method is concerned with the design of a graphical representation in the form of a table, images, diagrams, and spontaneous display ways to understand the data. Visual analytics has potentially brought the new federation of data mining and machine learning tools. Visual perception, design, data quality, missing data, end-user visual analytics are future trends of visualization (Saggi & Jain, 2018, p. 775) 20 of the 34 articles (59%) identified the visualization as being the fundamental avenue for presenting information to decision makers. The goal of data should be to convert data into actionable insight for more timely and accurate decision support (Delen & Zolbanin, 2018, p. 187). The research articles presented an association to data- driven decision making when assessing the firm performance. Atker et al., 2016 Big data analytic capabilities are an enabler of improved organizational performance The Sstructural model confirmed big data analytic capabilities is a predictor of organizational performance Suggest that big data firms should consider the higher order of big data analytic capabilities AND analytics capability–business strategy alignment as important strategic qualifications to influence organizational performance Big data analytic capabilities areis the perfect starting point for identifying and solving emerging big data challenges Brynjolfsson et al., 2011 IT is significantly correlated with ROA and Asset Utilization but not ROE Data- driven decision is correlated with ROE and Asset Utilization Organizations that invest in data- driven decisions have an additional value per employee for each standard deviation above the mean Organizations that adopt data- driven decisions have a higher market value that is most closely related to their level of IT capital Cao et al., 2015 The Positive relationship between inter-organization information processing capabilities and supply chain company performances Cao & Duan, 2017 Top-performing companies are more likely than bottom-performing companies to have a better data-driven environment, to use descriptive analytics more extensively Organizational design associated with data applications are essential to match information requirements and processing to inform decision making thereby to improve organizational performance Dutta & Bose, 2015 Using data from Google maps, the company successfully captured 20–30% market share in under developed markets by appropriately identifying them and taking a proactive marketing plan to improve sales at those locations. Fosso et al., 2015 The research shows that retailers can achieve up to 15-20% increase in ROI by putting big data into analytics Gunasekaran et al., 2017 Information sharing under the mediation effect of top management commitment help big data predictive analytics capability, which impacts on supply chain performance and organizational performance Gupta & George, 2016 This study has empirically validated the relationship between big data analytics capability and firm performance Lavalle et al., 2011 Top-performing organizations use analytics five times more than lower performers. Organizations that strongly agreed that the use of business information and analytics differentiates them within their industry were twice as likely to be top performers as lower performers. Top performers put analytics to use in the widest possible range of decisions and were twice as likely to use analytics to guide future strategies, and twice as likely to use insights to guide day-to-day operations Liberatore et al., 2017 Research has found that those companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors Long, 2018 The study shows that the more data-driven a firm is, the more productive (a 4–6% increase) it is Data -driven decision isare also correlated with a higher return on assets, return on equity, asset utilization, and market value, and the relationship seems to be causal. Pajouh, 2013 The case study estimates that the higher margin sales generate over $300,000 per year due to changes in data -driven decision making The case study experience thus far with implementation estimates it saves $200,00 per year by reducing yield loss The case study estimates that by automating the quote process based on data, the labor-saving cost isare $85,000 per year Provost & Fawcett, 2013 They show statistically that the more data-driven a firm is, the more productive it is—even controlling for a wide range of possible confounding factors One standard deviation higher on the data -driven decision making scale is associated with a 4–6% increase in productivity Data- driven decision making also is correlated with a higher return on assets, return on equity, asset utilization, and market value, and the relationship seems to be causal Schildkamp & Kuiper, 2010 Only one out of the two teachers interviewed indicated that he used data to base decisions on and to reflect on his own performance Data did not lead to any concrete measures to improve the curriculum, teacher performance or school performance because the use of data was limited Sheng et al., 2017 Organizational performance in a competitive environment is significantly correlated to user-generated content Social media metrics significantly indicate firm equity value with stronger and faster predictive relations than traditional online behavioral metrics Weiner et al., 2015 A systems mentality began to permeate the hospital, as people started to feel peer pressure to enhance their performance, as well as that of their units. The dashboard system and the administrative commitment driving its implementation began to shape the behaviors of people across the hospital. 8. Feedback- learning process The evaluation is using data to systematically to analyze the existing data sources leading to results that produce data the go back into the process to be analyzed through a feedback loop. That then leads organizations to implement improvement actions that can be taken (Schildkamp & Kuiper, 2010). This can create a reliable feedback mechanism that generates a permanent circular learning process (Pedersen & Wilkinson, 2018). This helps organizations to understand the market and the customer needs in the way of products or services (Saggi & Jain, 2018). 4.3 Discussion Based on Synthesis and Analysis The based on the synthesis and analysis the key findings are as follows: 1. Problem identification is critical in order to understand the possible improvements that can be realized from the implementation data and the insights generated from it. 2. Performance in a data economy is enhanced by the organization’s ability to learn and exploits the potential of resources by leveraging the capabilities that are valuable and rare, imperfectly imitable. 3. Analytic capabilities are an enabler of improved organizational performance. The analytics capability and business strategy alignment as important strategic qualification to influence organizational performance. 4. Data- driven decision is correlated with a higher return on assets, return on equity, asset utilization, and market value, and it has been shown to have a 4-6% increase in productivity. The relationship seems to be causal. 5. Top-performing companies are more likely than bottom-performing companies to have a better data-driven environment, use analytics five times more than lower performers, and to use descriptive analytics more extensively. 6. Organizational performance in a competitive environment is significantly correlated to user-generated content 7. A systems mentality, such as using visualization systems like dashboards, cause people to feel peer pressure this shapes the behaviors of people across the organization to enhance their performance. 4.4 Conceptual Model and Narrative The revised conceptual framework is based on the thematic synthesis process that was outlined in Chapter 3. This revised framework associates the data-driven environment and the decision-making hierarchy process that impact the performance outcomes to be measured and the assessment of this is input back into the process to create real-time changing solutions. The framework proposes that in the data- driven environment three components are necessary which are the overall organizational strategy, the generation of data both internally and externally, and the technological infrastructure to process the data. It also addresses the three levels of the decision-making hierarchy as being strategic, operational, and tactical and decisions can be made at any level to impact the performance outcomes. The implementation of adaptable data- driven decision making may result from a better understanding of these relationships and provide a framework to inform leadership that is dependent upon understanding mechanisms that are associated with positively impacting organizational performance. 4.5 Alternative Perspectives 4.6 Chapter Summary Khatri (2016) states that data- driven decision making requires the following: employing of new data sources, identifying ways through analysis of generating insights, and using the insights for decision making (p. 686). That is supported by Chang et al. (2014) that defines analytics as a tool that is used to surface useful information from raw data generated by technology and isare used in supporting decision making (p. 69). However, analytics is more than just analytical methodologies. It is a process of transforming data into action through insights in the context of problem-m solving (Liberatore et al., 2017). Problem -solving starts with the proper planning process which identifies opportunities and how data can improve performance (Akter et al., 2016). These insights are used to develop measures of key performance indicators (Erevelles et al., 2016) that allow continuous monitoring through user interfaces (Khatri, 2016). Feedback mechanisms are integrated into the process so that outcomes can be compared in as close to real time as possible (Pedersen & Wilkinson, 2018). The learning process enables decision- makers to gain a holistic view of the business and customers to improve the operational efficiency by moving through a data- driven environment and using the data to gain insights that will deliver critical solutions to the organization (Bayrak, 2015). An important aspect of this process is the alignment between the planning and the implementation at the different levels within an organization (Weiner et al., 2015). Planning is done at the strategic level and the implementation is done through the operations. This is how data supports decision makers and empowers them to make strategic, tactical, and operational decisions. (Bayrak, 2015). Data granularity at these decision-making levels consists of: 1. Strategic- being rougher, more uncertain and more unstructured 2. Operational- being finer, more accurate and more structured 3. Tactical- lies somewhere between strategic and operational (Long, 2018, p. 58). In order to process data on all levels, organizations can design a structure that will meet the information processing requirements at each level. The quality and performance of decision making areis based on the organization’s capability to meet the information processing requirements by an adequate level of information processing capacity (Roßmann et al., 2018, p. 136). A data- driven environment ensures that the insights are used to support decision making with maximum effort (Cao & Duan, 2017). However, data processing does not guarantee sufficient decisions will be made. There are many challenges that can hinder the extraction of valuable insights. To capitalize on the value of the data an analytical strategy can be developed to guide the use of the data (Cao & Duan, 2017). Harnessing the power of statistical and mathematical models by using historical and real- time data, organizations can monitor key metrics that are relevant to the strategy of the organization (Bayrak, 2015). This enables the decision maker to identify a finer subset of possible outcomes to reduce the noise in information (Brynjolfsson et al., 2011). The research indicates that when an organization has complete and accurate information about the relationship between choices and outcomes, it will be more likely to improve the decision-making effectiveness (Cao et al., 2015, p. 8). This involves comprehensive as well as multi-dimensional analytics without any constraints or assumptions and the resulting decisions will be unbiased and objective (Long, 2018) This sometimes allows decisions to be made automatically on a massive scale (Provost & Fawcett, 2013) By leveraging these capabilities, decision -makers can consider any number of the drivers of the adoption and implementation and develop value- based strategies that can impact organizational performance positively (Bayrak, 2015). 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Integrating strategic and operational decision making using data-driven dashboards: The case of St. Joseph mercy Oakland hospital. Journal of Healthcare Management, 60(5), 319–330 Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101, 572–591. Appendix Appendix A: Exclusion Reasoning Reference Reason Database Ahmad, Y. (2017). Recreating Intimacy With Connected Consumers. GfK Marketing Intelligence Review, 9(2), 49–54. https://doi.org/http://dx.doi.org.ezproxy.umuc.edu/10.1515 Interview format with no supporting evidence ABI Inform Arunachalam, D., & Kumar, N. (2018). Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems With Applications. Retrieved from http://-/j.eswa- Focuses on cluster performance ScienceDirect Babiceanu, R. F., & Seker, R. (2016). Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Computers in Industry, 81, 128–137. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.compind- No performance outcome ScienceDirect Ballou, B., Heitger, D. L., & Stoel, D. (2018). Data-driven decision-making and its impact on accounting undergraduate curriculum. Journal of Accounting Education. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.jaccedu- Focused on academic curriculum performance ScienceDirect Batarseh, F. A., & Latif, E. A. (2016). Assessing the Quality of Service Using Big Data Analytics: With Application to Healthcare. Big Data Research, 4, 13–24. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.bdr- No performance outcome ScienceDirect Bohanec, M., Borštnar, M. K., & Robnik-Šikonja, M. (2017). Explaining machine learning models in sales predictions. Expert Systems with Applications, 71, 416–428. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.eswa- Focus is on machine learning ScienceDirect Bohanec, M., Robnik-Sikonja, M., & Borstnar, M. K. (2017). Decision-making framework with double-loop learning through interpretable black-box machine learning models. Industrial Management & Data Systems, 117(7),-. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 Focus is on machine learning ABI Inform Bohler, J., Krishnamoorthy, A., & Larson, B. (2017). The Financial and Non-financial Aspects of Developing a Data-Driven Decision-Making Mindset in an Undergraduate Business Curriculum. The e - Journal of Business Education & Scholarship of Teaching, 11(1), 85–96. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 Performance focused on academic curriculum ABI Inform Browne, L., & Rayner, S. (2015). Managing leadership in university reform: Data-led decision-making, the cost of learning and déjà vu? Educational Management Administration & Leadership, 43(2), 290–307. https://doi.org/10.1177/- Focuses on student performance BSC Das, D. (2017). Development and validation of a scale for measuring Sustainable Supply Chain Management practices and performance. Journal of Cleaner Production, 164,-. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.jclepro- Not data driven focused in decision making ScienceDirect Datnow, A., Park, V., & Kennedy-Lewis, B. (2013). Affordances and constraints in the context of teacher collaboration for the purpose of data use. Journal of Educational Administration, 51(3), 341–362. https://doi.org/http://dx.doi.org.ezproxy.umuc.edu/10.1108/- Focuses on individual performance ABI Inform Dong, F., Zhang, G., Lu, J., & Li, K. (2018). Fuzzy competence model drift detection for data-driven decision support systems. Knowledge-Based Systems, 143, 284–294. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.knosys- No performance outcome ScienceDirect Dupin-Bryant, P. A., & Olsen, D. H. (2014). Business Intelligence, Analytics And Data Visualization: A Heat Map Project Tutorial. International Journal of Management & Information Systems (Online), 18(3), 185. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 No performance outcome ABI Inform Deutsch, R., Aia, & Leed, A. P. (2015). Leveraging data Across the Building Lifecycle. Procedia Engineering, 118, 260–267. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.proeng- No performance outcome ScienceDirect Fitzgerald, M. (2015). When Health Care Gets a Healthy Dose of Data. MIT Sloan Management Review, 57(1), n/a. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 Focused on individual performance ABI Inform Flath, C. M., & Stein, N. (2018). Towards a data science toolbox for industrial analytics applications. Computers in Industry, 94, 16–25. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.compind- Focused on model predictive performance ScienceDirect Hubbard, L., Datnow, A., & Pruyn, L. (2014). Multiple initiatives, multiple challenges: The promise and pitfalls of implementing data. Studies in Educational Evaluation, 42, 54–62. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.stueduc- No performance outcome ScienceDirect Ju, J., Liu, L., & Feng, Y. (2018). Citizen-centered big data analysis-driven governance intelligence framework for smart cities. Telecommunications Policy. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.telpol- Not generalizable to Organizations ScienceDirect Kolomvatsos, K., Anagnostopoulos, C., & Hadjiefthymiades, S. (2015). An Efficient Time Optimized Scheme for Progressive Analytics in Big Data. Big Data Research, 2(4), 155–165. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.bdr- Focused on model performance ScienceDirect Kowalczyk, M., & Buxmann, P. (2015). An ambidextrous perspective on business intelligence and analytics support in decision processes: Insights from a multiple case study. Decision Support Systems, 80, 1–13. Retrieved from http://-/j.dss- Focused on ambidexterity and decision quality ScienceDirect Liang, T.-P., & Liu, Y.-H. (2018). Research Landscape of Business Intelligence and Big Data analytics: A bibliometrics study. Expert Systems with Applications. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.eswa- No performance outcome ScienceDirect Lyu, Z.-J., Lu, Q., Song, Y., Xiang, Q., & Yang, G. (2018). Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study. Advances in Civil Engineering, 1–14. https://doi.org/10.1155/2018/- Focused on mechanical performance CASC Massis, B. E. (2017). The business of the library is service. Information and Learning Science, 118(7/8), 447–450. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 No performance outcome ABI Inform Nabati, E. G., & Thoben, K.-D. (2017). Data Driven Decision Making in Planning the Maintenance Activities of Off-shore Wind Energy. Procedia CIRP, 59, 160–165. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.procir- No performance outcome ScienceDirect Phillips-Wren, G., & Hoskisson, A. (2015). An analytical journey towards big data. Journal of Decision Systems, 24(1), 87–102. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 Focuses on CRM performance ABI Inform Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37–58. Retrieved from http://-/j.accinf- Not data driven focused in decision making ScienceDirect Ryan, G. W., Bloom, E. W., Lowsky, D. J., Linthicum, M. T., Juday, T., Rosenblatt, L., … Sayles, J. N. (2014). INNOVATION PROFILE: Data-Driven Decision-Making Tools To Improve Public Resource Allocation For Care And Prevention Of HIV/AIDS. Health Affairs, 33(3), 410–417. Retrieved from http://ezproxy.umuc.edu/login?url=https://search.proquest.com/docview/-?accountid=14580 Focuses on Individual performance ABI Inform Sadati, N., Chinnam, R. B., & Nezhad, M. Z. (2018). Observational data-driven modeling and optimization of manufacturing processes. Expert Systems with Applications, 93, 456–464. https://doi.org/10.1016/j.eswa- Focused on model performance CASC Schroeder, H. M. (2015). Knowledge, learning and development for success in the new business environment: an art and science approach. Development and Learning in Organizations, 29(5), 10–12. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 Other variables (trust and social media) ABI Inform Sherrod D., McKesson T., & Mumford M. (2010). Are you prepared for data-driven decision making? Nursing Management, 41(5), 51. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 Focussed on performance targets ABI Inform Shrivastava, S., Nagdev, K., & Rajesh, A. (2018). Redefining HR using people analytics: the case of Google. Human Resource Management International Digest, 26(2), 3–6. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 Focused on team and personal performance ABI Inform Stein, N., Meller, J., & Flath, C. M. (2018). Big data on the shop-floor: sensor-based decision-support for manual processes. Zeitschrift Für Betriebswirtschaft, 1–24. https://doi.org/http://dx.doi.org.ezproxy.umuc.edu/10.1007/s- No performance outcome ABI Inform Stein, A. D., Smith, M. F., & Lancioni, R. A. (2013). The development and diffusion of customer relationship management (CRM) intelligence in business-to-business environments. Industrial Marketing Management, 42(6), 855–861. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.indmarman- No performance outcome ScienceDirect Visinescu, L. L., Jones, M. C., & Sidorova, A. (2016). Improving Decision Quality: The Role of Business Intelligence. The Journal of Computer Information Systems, 57(1), 58–66. https://doi.org/http://dx.doi.org.ezproxy.umuc.edu/10.1080/- Focuses on decision maker performance ABI Inform Walker, S. C., Bumbarger, B. K., & Phillippi, S. W. (2015). Achieving successful evidence-based practice implementation in juvenile justice: The importance of diagnostic and evaluative capacity. Evaluation and Program Planning, 52, 189–197. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.evalprogplan- No performance outcome ScienceDirect Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems. https://doi.org/https://doi-org.ezproxy.umuc.edu/10.1016/j.jmsy- No decision making CASC Wang, A., Mahfouf, M., Mills, G. H., Panoutsos, G., Linkens, D. A., Goode, K., Denaï, M. (2010). Intelligent model-based advisory system for the management of ventilated intensive care patients. Part II: Advisory system design and evaluation. Computer Methods & Programs in Biomedicine, 99(2), 208–217. https://doi.org/10.1016/j.cmpb- Focused on model performance ScienceDirect Williamson K., & Kretschman R. (2010). Outcome measurement matters. Nursing Management, 41(8), 13. Retrieved from http://ezproxy.umuc.edu/login?url=https://search-proquest-com.ezproxy.umuc.edu/docview/-?accountid=14580 Focused on patient outcomes ABI Inform Xavier, M. J., Srinivasan, A., & Thamizhvanan, A. (2011). Use of analytics in Indian enterprises: an exploratory study. Journal of Indian Business Research, 3(3), 168–179. https://doi.org/http://dx.doi.org.ezproxy.umuc.edu/10.1108/- Not data driven focused in decision making ABI Inform Young, C., McNamara, G., Brown, M., & O’Hara, J. (2018). Adopting and adapting: school leaders in the age of data-informed decision making. Educational Assessment, Evaluation and Accountability, 30(2), 133–158. https://doi.org/http://dx.doi.org.ezproxy.umuc.edu/10.1007/s- Focused on student performance ABI Inform Appendix B: Snowball Included Articles Snowball Article Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018), Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017) Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R., Childe, S.J., (2016). How to improve firm performance using big data analytics capability and business strategy alignment? Int. J. Prod. Econ. 182, 113–131. Brynjolfsson, E., & McElheran, K. (2016) Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014), Long, Q. (2018) Brynjolfsson, E., Hitt, L.M. and Kim, H.H. (2011), “Strength in numbers: how does data-driven decisionmaking affect firm performance?”, April 22, available at: https://ssrn.com/abstract=-; http://dx.doi.org/10.2139/ssrn- Cao, G., Duan, Y., & Li, G. (2015), Cao, G., & Duan, Y. (2017), Kache, F., & Seuring, S. (2017), Wang, Y., & Byrd, T. A. (2017). Chen, H., Chiang, R.H.L. and Storey, V.C. (2012), “Business intelligence and analytics: from big data to big impact”, MIS Quarterly, Vol. 36 No. 4, pp-. Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017), Wang, Y., & Byrd, T. A. (2017) Erevelles, S., Fukawa, N., Swayne, L., 2016. Big Data consumer analytics and the transformation of marketing. J. Bus. Res. 69 (2), 897–904. Kache, F., & Seuring, S. (2017), Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018), Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017), Wang, Y., & Byrd, T. A. (2017) Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D. (2015), “How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study”, International Journal of Production Economics, Vol. 165, pp. 234-246. Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018), Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017) Gunasekaran, A., Papadopoulos, T., Dubey, R., Fosso Wamba, S., Childe, S.J., Hazen, B., Akter, S., (2017). Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. 70, 308–317. Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018), Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017) Gupta, M., George, J.F., 2016. Toward the development of a big data analytics capability. Inf. Manag. 53,-. Cao, G., Duan, Y., & Li, G. (2015), Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014), Delen, D., & Zolbanin, H. M. (2018), Dutta, D., & Bose, I. (2015), Wang, Y., & Byrd, T. A. (2017) LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21. Long, Q. (2018), Pedersen, J. S., & Wilkinson, A. (2018), Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017) Provost, F., Fawcett, T. (2013) Data science and its relationship to big data and data–driven decision making, Big Data 1 (1), 51–59 . Cao, G., & Duan, Y. (2017), Flath, C. M., & Stein, N. (2018), Kowalczyk, M., & Buxmann, P. (2015), Wang, Y., & Byrd, T. A. (2017). Sharma, R., Mithas, S. and Kankanhalli, A. (2014), “Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations”, European Journal of Information Systems, Vol. 23 No. 4, pp. 433-441. Appendix C: Quality Source Transparency- Access to Evidence Utility- Answers to the question Accessibility- Understandable Methodology-Research design appropriate Generalizable- applied to population at large Specificity- Quality standards in source domain Total Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R., Childe, S.J.,- Bayrak, T. - Brynjolfsson, E., Hitt, L.M. and Kim, H.H- Brynjolfsson, E., & McElheran, K. - Cao, G., Duan, Y., & Li, G. - Cao, G., & Duan, Y. - Chang, R. M., Kauffman, R. J., & Kwon, Y. - Chen, H., Chiang, R.H.L. and Storey, V.C. - Delen, D., & Zolbanin, H. M. - Dutta, D., & Bose, I. - Erevelles, S., Fukawa, N., Swayne, L., - Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D. - Gunasekaran, A., Papadopoulos, T., Dubey, R., Fosso Wamba, S., Childe, S.J., Hazen, B., Akter, S.,- Gupta, M., George, J.F- Holsapple, C., Lee-Post, A., & Pakath, R. - Kache, F., & Seuring, S. - Khatri, V. - LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. - Lee, S., & Yoo, S. - Liberatore, M. J., Pollack-Johnson, B., & Clain, S. H. - Long, Q. - Pajouh, F. M., Xing, D., Zhou, Y., Hariharan, S., Balasundaram, B., Liu, T., & Sharda, R. - Pedersen, J. S., & Wilkinson, A. - Provost, F., Fawcett, T. - Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. - Saggi, M. K., & Jain, S. - Schildkamp, K., & Kuiper, W. - Sharma, R., Mithas, S. and Kankanhalli, A- Sheng, J., Amankwah-Amoah, J., & Wang, X. - Sun, Z., Strang, K., & Firmin, S. - van Rijmenam, M., Erekhinskaya, T., Schweitzer, J., & Williams, M.-A. - Wang, Y., & Byrd, T. A. - Weiner, J., Balijepally, V., Tanniru, M. -