This non-experimental, census survey included the elementary, middle, and high school principals at the comprehensive schools within a large, suburban school division in Virginia. The focus of this study was the factors that influence building administrators in using data to make instructional decisions. The purpose was to discover if there is a difference in the perceptions of elementary, middle, and high school principals of data use to make instructional decisions within their buildings. McLeod’s (2006) Statewide Data-Driven Readiness Study: Principal Survey was used to assess the principals’ beliefs about the data-driven readiness of their individual schools. Each principal indicated the degree to which they agreed or disagreed with statements about acting upon data, data support systems, and the data school culture. Twenty-two items aligned with four constructs identified by White (2008) in her study of elementary school principals in Florida. These four constructs or factors were used to determine if there was a significant difference in principal beliefs concerning teacher use of data to improve student achievement, principal beliefs regarding a data-driven culture within their building, the existence of systems for supporting data-driven decision-making, and collaboration among teachers to make data-driven decisions. For each of the survey items a majority of the responses (≥62%) were in agreement with the statements, indicating the principals agreed slightly, agreed moderately, or agreed strongly that data-driven decision-making by teachers to improve student achievement was occurring within the building, a data-driven culture and data supporting systems exists, and teachers are collaborating and using data to make decisions. Multiple analyses of variance showed significant differences in the means. Some of these differences in means were based on the principals’ assignment levels. While both groups responded positively to the statement that collaboration among teachers to make data-driven decisions, the elementary principals agreed more strongly than the high school principals. When mediating variables were examined, significance was found in principals’ beliefs concerning teacher use of data to improve student achievement depending on the years of experience as a principal. Principals with six or more years of experience had a mean response for Construct 1 of 4.84 while those with five or less years of experience had a mean of 4.38, suggesting that on average those principals with more experience had a stronger belief that teachers are using data to improve student achievement. There is significance between the means of principals with three or fewer years versus those with more than three years in their current assignment on two of the constructs – a data-driven culture and collaboration among teachers. Principals with less time in their current position report a slightly higher agreement than their less experienced colleagues with statements about the data-driven culture within their school. Significant difference was also found between principals’ beliefs about teacher collaboration to improve student achievement and their beliefs regarding collaboration among teachers using data-driven decision-making and the school’s AYP status for 2008-2009. Principals assigned to schools that had made AYP for 2008-2009 moderately agreed that teachers were collaborating to make data-driven decisions. In comparison, principals assigned to schools that had not made AYP only slightly agreed that this level of collaboration was occurring in their schools.

This thesis explores the implementation and improvement of data-driven processes within a Swedish industrial organization, specifically focusing on long-term maintenance planning in the energy sector. Despite the recognized benefits of data-driven decision-making, many organizations, including those in the energy sector, struggle to fully adopt this approach due to challenges such as organizational culture, knowledge management, and lack of top management support. This study addresses these challenges by investigating how data is currently utilized within a Swedish energy producer, identifying barriers to effective data use, and exploring the role of knowledge sharing in enhancing data-driven practices. Employing theoretical models such as Nonaka’s SECI model and Gökalp et. al. (2020) data analytics capability process maturity level, the research highlights that while some data-driven strategies are in place, there is a need for more standardized processes and greater involvement from top management. The study reveals significant impacts of knowledge sharing on data utilization, identifying barriers such as lack of training, scheduling conflicts, and physical and informational silos due to high-security requirements. Furthermore, it examines the gap between data availability and its utilization, attributing it to factors like the complexity of information systems, perceived data quality issues, and insufficient involvement of knowledgeable personnel. The findings suggest that addressing these issues through improved training, streamlined data systems, and strategic management of high-security constraints can enhance the overall effectiveness of data-driven decision-making. By fostering a data-driven culture and enhancing knowledge sharing- practices, the organization can better leverage its data assets, ultimately improving maintenance planning and operational efficiency in a high-security, regulated environment. / Detta arbete undersöker implementeringen och förbättringen av datadrivna processer inom en svensk industriell organisation, med särskilt fokus på långsiktig underhållsplanering i energisektorn. Trots fördelarna med datadrivet beslutsfattande, kämpar många organisationer, inklusive de inom energisektorn, med att fullt ut anta detta tillvägagångssätt på grund av utmaningar såsom organisationskultur, kunskapshantering och brist på stöd från ledningen. Denna studie tar itu med dessa utmaningar genom att undersöka hur data för närvarande används inom en svensk energiproducent, identifiera hinder för effektiv dataanvändning och utforska kunskapsdelningens roll i att förbättra datadrivna metoder. Genom att använda teoretiska modeller som Nonakas SECI-modell och Gökalp et. al. (2020) mognadsnivå för dataanalytisk förmåga, belyser studien att även om vissa datadrivna strategier är på plats, finns ett behov av mer standardiserade processer och större engagemang från ledningen. Studien visar betydande effekter av kunskapsdelning på datanyttjande, och identifierar hinder som brist på utbildning, schemakonflikter samt fysiska och informationsmässiga silos på grund av höga säkerhetskrav. Vidare undersöker den gapet mellan tillgänglighet och utnyttjande av data, vilket tillskrivs faktorer som komplexiteten i informationssystem, upplevda datakvalitetsproblem och otillräckligt inkludering av kunnig personal. Resultaten tyder på att genom att ta itu med dessa problem genom förbättrad utbildning, strömlinjeformade datasystem och strategisk hantering av höga säkerhetskrav kan den övergripande effektiviteten av datadrivet beslutsfattande förbättras. Genom att främja en datadriven kultur och förbättra kunskapsdelningspraxis kan organisationen bättre utnyttja sina dataresurser, vilket i slutändan förbättrar underhållsplanering och operationell effektivitet i en högsäkerhetsmiljö.

The purpose of the study was to find out what challenges governments have encountered when implementing Artificial Intelligence.  The method used was qualitative and the interviews were conducted remotely. Four governments were interviewed where respondents were asked questions about what they had experienced as challenges in the implementation of AI. The results were analyzed against previous studies and compiled by picking out themes from the transcribed interviews.  The results of the survey identify a number of challenges linked to three main themes: the lack of knowledge, challenges around data and when challenges arise.

Datadriven kultur har flitigt nämnts i litteraturen som en tydlig framgångsfaktor för stora verksamheter för att skapa konkurrenskraft på marknaden.  Genom att verksamheter kan ta strategiska beslut baserat på stora mängder data förankrad i verkligheten undviks beslut som tas på magkänsla, således leder till optimering av verksamheter. Dock har små och medelstora företag (SMFs) halkat efter i utvecklingen då verksamheterna ofta saknar resurser och kompetens för att möjliggöra en datadriven kultur. Syftet med forskningen är att identifiera framgångsfaktorer speciellt inriktade på SMFs och skapa en sammanställning som SMF kan ta del av för att skapa en datadriven kultur. Den primära datainsamlingen genomfördes genom en kvalitativa ansats och fallstudie som forskningsmetod med semi-strukturerade intervjuer inriktade mot IT-branschen inom SMF som besatt på relevant kunskap inom ämnesområdet. Respondenternas svar har analyserats i jämförelse med tidigare litteratur för att generera framgångsfaktorer som möjliggör en datadriven kultur hos SMFs. Resultatet av forskningen har genererat en sammanställning på totalt fyra bekräftade huvudkategorier och sexton bekräftade underkategorier varav åtta berikande underkategorier är nya framgångsfaktorer som uppkommit från intervjuerna. De identifierade framgångsfaktorerna kan anammas av SMF för att möjliggöra den digitala transformationen mot en datadriven kultur. Resultatet av forskningen illustrerar att SMFs har stora möjligheter att öka sin konkurrenskraft, affärsvärde och produktivitet genom att tillämpa framgångsfaktorerna inom SMF och att en datadriven kultur inte är begränsade till stora verksamheter. / Data-driven culture has frequently been mentioned in the literature as a clear success factor for large enterprises (LEs) creating competitive advantages in the market. As enterprises can make strategic decisions based on large amounts of data anchored in reality, decisions are based on gut feeling, thus leading to optimization of enterprises. However, small and medium-sized enterprises (SMEs) have fallen behind in development as the enterprises often lack resources and knowledge to enable a data-driven culture. The purpose of the research is to identify success factors specifically focused on SMEs and create a compilation of which SMEs can adopt to create a data-driven culture. The primary data collection was conducted with a qualitative approach carrying out a case study with semi-structured interviews focused on the IT industry within SMEs that are obsessed with relevant knowledge in the subject area. The interviewees' responses have been analyzed in comparison with previous literature to generate success factors that enable a data-driven culture in SMEs. The results of the research have generated a compilation of a total of four confirmed main categories and sixteen confirmed subcategories, of which eight enriching subcategories are new success factors that have emerged from the interviews. The identified success factors can be adopted by SMEs to enable the digital transformation towards a data-driven culture. The results of the research illustrates that SMEs have great opportunities to increase in competitive advantages, business value and productivity by applying the success factors within SMEs and that the data-driven culture is not limited to LE.