The aim of the Reading First grant program was to (a) increase quality and consistency of instruction in K-3 classrooms; (b) conduct timely and valid assessments of student reading growth in order to identify students experiencing reading difficulties; and (c) provide high quality, intensive interventions to help struggling readers catch up with their peers (Torgesen, 2002). In the State of Florida, school districts must incorporate the use of an assessment tool called the Dynamic Indicators of Basic Early Literacy Skills (DIBELS) to qualify for Reading First grant funding. Though DIBELS has been found to be a valid and reliable assessment for screening, monitoring, and evaluating student outcomes in early literacy skills, very little discussion or research has been conducted concerning teacher use and attitudes about DIBELS within a Reading First program. The present study involved a qualitative analysis of teachers' perceptions and use of the DIBELS within a Reading First context. Fourteen teachers (seven kindergarten and seven first grade teachers), Reading Coaches, non-teaching Specialists, and DIBELS experts participated in the present study. Results were aggregated for comparisons across multiple data sources. Results suggest teacher's perceptions may not be easily classified on a simple dichotomous range; rather their reported benefits and concerns on the use of the DIBELS were found to be varied and highly situational. Results were further interpreted in the context of research literature on data utilization and analysis in schools.

Most large contemporary mines already have considerable amounts of data, much of which goes largely unused. The key challenge in big data is increasing data utilization. Much of the data in the mine (not plant) come from a variety of systems, each with different databases and reporting environments. Standard technology deployments create a "silo-ification" of data leading to poor system usage. Through modern server monitoring, data utilization can quantifiably be measured. A host of other quantifiable, often automated approaches, to measuring data use and value can also be incorporated as a means of monitoring value generation. A data valuation tool is presented to measure the data assets at an operation. The Data Value Index (DVI) quantifies business intelligence best practices and user interaction considering managerial flexibility and data utilization rates. The DVI is built considering many case studies of data warehousing at various mining companies, some of which will be presented.

This study is using an inductive explorative approach to investigate how micro-enterprises within the marketing consulting industry collect and use data for the purpose of increasing sales. A micro-enterprise is considered to be a company that employs no more than 10 people and/or whose annual turnover is no more than 2 million EUR. The research applies the concept of social selling and development of emergent technologies to understand the methods used by examined salespeople within the marketing agencies examined, and to answer the research question: How is customer data from social selling used among micro-enterprises within the marketing consulting industry to build and retain customer relationships? The following paper suggests that social media is used by the seller with the aim of identifying common grounds between buyer and seller, in order to create a personal bond that sets a strong foundation for continuing the relationship long-term. However, it also indicates that social selling activities are used in various ways and in combination with traditional data collection. Even though usage of customer data has been a heated debate over the years, the results of this study point to the fact that the concerns of collecting and utilizing data in regard to customer privacy, are limited among micro-enterprises within B2B-sector. Additionally, the study addresses advantages and disadvantages with the size of being a micro-enterprise related to the methods of social selling.

Purpose - The objective of this study is to explore how Data-Intelligence Service-Providers (DISP) can adapt existing Business Model (BM) dimensions to leverage the potential value and mitigate the emerging challenges Open Data (OD) introduces. Method – By developing a multiple case study, we intend to qualitatively explore what BM practices DISPs employ when incorporating OD. Interviews are conducted in multiple phases with a total of 25 interviews and results generated using a thematic analysis. Findings – Through empirical investigation and analysis of DISPs actions and strategies, the study uncovers how these firms navigate challenges and opportunities presented by OD. By portraying the strategies across three BM dimensions—value creation, delivery, and capture—this study identifies six key practices that help DISPs competitively differentiate themselves in the OD environment. The identified practices include Use-case understanding and Data-driven Service Innovation for value creation, Enhanced Data Delivery and Collaborative Data Optimization for value delivery, and AdjustedRevenue Model and Market Expansion for value capture. Implications – In our contribution to existing literature, we present empirical evidence spanning across all dimensions of the BM, shedding light on the competitive advantages facilitated by OD. Additionally, through identifying key practices, this thesis uncovers several areas where there is a lack of understanding on ODs impact in a commercial context. Specifically, by solely focusing on the perspective of DISPs, we offer detailed insight into how these practices are practically unfolding. Furthermore, the thesis presents a framework categorizing practices based on priority and ecosystem dependency. This framework delineates certain practices that are considered fundamental when incorporating OD while also recognizing their intricate requirement of involving external parties, offering managers a visual overview of how to systematically adapt their BMs to incorporate OD into their services. In addition, we manage to address the common distortions about OD by offering a thorough theoretical foundation and defining it clearly within a commercial context, making this complex topic more accessible and better understood. Limitations and future research – As this study is limited to data-providers and DISPs, this thesis advocates for exploring end-user perspectives in future research deemed crucial for gathering a comprehensive understanding of their needs and interactions with OD solutions to solidify findings in this study. Additionally, it is encouraged that future research should investigate misalignments between data-providers and DISPs (e.g. regulatory and technical matters) which currently, are leading to massive inefficiencies in data supply chains. Understanding these issues and implementing strategies to address them can optimize OD resource utilization, thereby facilitating greater innovative potential for service-providers leveraging it.