Over the past years, the value of data and DDDM have increased significantly as technological advancements have made it possible to store and analyze large amounts of data at a reasonable cost. This has resulted in completely new business models that has disrupt whole industries. DDDM allows businesses to rely their decisions on data, as opposed to on gut feeling. Up until this point, literature is eligible to provide a general view of what are the major challenges corporations encounter when implementing a DDDM strategy. However, as the field is still rather new, the challenges identified are yet very general and many corporations, especially B2B MNCs selling consumer goods, seem to struggle with this implementation. Hence, a single-case study on such a corporation, named Alpha, was carried out with the purpose to explore what are their major challenges in this process. Semi-structured interviews revealed evidence of four major findings, whereas, execution and organizational culture were supported in existing literature, however, two additional findings associated with organizational structure and consumer behavior data were discovered in the case of Alpha. Based on this, the conclusions drawn were that B2B MNCs selling consumer goods encounter the challenges of identifying local markets as frontrunners for strategies such as the one to become more data-driven, as well as the need to find a way to retrieve consumer behavior data. With these two main challenges identified, it can provide a starting point for managers when implementing DDDM strategies in B2B MNCs selling consumer goods in the future.

Background: Data-driven decision-making (DDDM) has emerged as a primary approach to decision-making in many organizations. It uses data and analytics to guide decision-making processes and can lead to better business outcomes. Prior research has focused on DDDM in large corporations operating in large economies, and therefore this thesis will examine DDDM in small and medium enterprises in Sweden.  Purpose: The purpose of this research study is to examine the effect DDDM has on the financial performance of Swedish SMEs to investigate if the utilization of DDDM benefits companies financially and to understand the effect of managerial experience, technical skills, information quality, and firm size on the data-driven decision-making process. Method: This study is based on the positivism paradigm, following deductive reasoning and a quantitative approach of gathering data through digital surveys. The sample consisted of 55 Swedish SMEs gathered through simple random sampling. Further, the data was analyzed using Pearson correlation, Spearman rank correlation, and regression analysis to test hypotheses. Findings: The literature review identified a research gap on DDDM, factors that effect DDDM, and Financial Performance. Four hypotheses were developed to answer the research questions. The OLS regression found that DDDM had no significant effect on Financial Performance, the first hypothesis was not supported. The Information Quality variable had a significant positive effect on DDDM resulting in support for the second hypothesis. However, Managerial Experience and Technical Skills did not have a significant effect in the main regression model, hypotheses three and four were not supported. Conclusion: The thesis showed that DDDM did not have a significant effect on financial performance in Swedish SMEs. Additionally, managerial expertise and technical skills did not have an effect on DDDM. However, Information quality did have an effect on the DDDM process and was correlated with technical skills, which is in line with the theories used in the study: Organizational Information Processing Theory (OIPT) and Absorptive Capacity. This further supports that information quality is vital for the DDDM process and can explain why DDDM might not always lead to improvements in financial performance for Swedish SMEs.

Despite the importance an urban school district places on data-driven decision-making (DDDM) to drive instruction, implementation continues to remain a challenge. The purpose of this study was to investigate how support systems affected the implementation of DDDM to drive instructional practices in three urban schools that recently transitioned from priority or focus to good standing on the State Accountability Report. The study aligned with the organizational supports conceptual framework with an emphasis on data accessibility, collection methods, reliability and validity, the use of coaches and data teams, professional development, and data-driven leaders. Through the collection of qualitative data from one-on-one interviews, the research questions asked about the perspectives on data culture and data driven instructional practices of three school leaders and nine teachers. The data were triangulated to generate a thematic illustration of content that was coded and analyzed to identify solid patterns and themes. Findings suggest that leaders create a data-driven school culture by establishing a school-wide vision, developing a DDDM cycle, creating a collaborative DDDM support system, communicating data as a school community, and changing the way technology is used in DDDM initiatives. Based on the findings, a project in the form of a white paper was developed, using research to support that when data is regularly used to hone student skills, a positive shift in overall teacher practices occurs. This shift provides the potential for positive social change when students have opportunities to attain academic goals, resulting in increased student achievement and higher graduation rates.

Data-driven approaches to decision making are today applied far and wide. With origins in the field of judgment and decision making (JDM), data-driven decision making (DDDM) has become an emergent topic within I-O psychology, especially within the fields of people analytics and human resource analytics. In light of the current AI revolution, it is evident that the next steps in JDM research include data- driven approaches. The purpose of this Master’s thesis was to compile the research on data-driven decision making conducted across disciplines into a comprehensive overview. Main research questions: based on systematic reviews and scoping reviews about implementations of DDDM affecting individuals, groups, or organizations, what areas of research can be identified? How and to what extent are they linked? To address these questions, this thesis utilizes a scoping meta-review design and bibliometrics. After rigorous search and screening processes, the final sample consisted of n = 1,008 systematic and scoping reviews. The results indicated that there are research areas within the included reviews that are isolated to a varying extent. Based on a multiple correspondence analysis (MCA), five areas of research were identified: business intelligence; learning analytics/education; mHealth/telemedicine; general decision making/decision support; and clinical decision support/diagnosis/healthcare. As a scoping meta-review encompassing a large number of scientific fields and methodologies, this thesis contributes to the progression of DDDM research at large. The results highlight the scattered nature of current research practices within DDDM and identify an opportunity for scientific advancement through interdisciplinary research.

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