The 21st century saw the rise of internet and with it, the digitalization of our world. Today, many companies rely on technology to run their businesses and Big Data is one of the latest phenomenon that arose from technological evolution. As the amount of data is constantly increasing, ranging from business intelligence to personal information, Big Data has become a major source of competitive advantage for companies who are able to implement it efficiently. However, as with every new technology, challenges and issues arise. What’s more, the learning curve is steep, and companies need to adapt quickly, so as to follow the pace of innovation and develop the skill-set of their employees to remain competitive in their respective industries. This paper investigates how Big Data is impacting companies, the main challenges they are facing within its implementation and looks to determine if these challenges originate from a lack of skills from the current workforce. A qualitative study has been conducted, interviewing nine respondents over eight interviews of 54 minutes on average. Three main ideas have been outlined through the interviews conducted by the authors. The first is the impact of Big Data in companies with mainly the benefits, challenges, regulations as well as the cohabitation of human beings and technology. The second and third are the optimal profile of a decision-maker and the ideal profile of the employee in companies working with Big Data. The profiles of the decision-maker and employee are composed of characteristics, skills and experience. The decision-maker, in this paper, was defined as a key actor in the success or failure of a company and of great influence on the profile of the employee. His skills, such as strategic, basic, analytical, communication and decision-making were developed, and their correlation was demonstrated. Ultimately, the lack of skills in companies today, often regarded as a challenge by numerous scholars, was shown to be the origin for many of the challenges companies are facing, mainly through bad decision-making and lack of communication. The authors finally outlined steps for a successful implementation of Big Data in companies and future trends such as regulations and increased technological evolution to carefully and actively pursue for people and businesses alike.

Made available in DSpace on 2016-06-02T19:52:10Z (GMT). No. of bitstreams: 1 6712.pdf: 2095829 bytes, checksum: 0fcab607bc1d879d07e91b41e95f55c5 (MD5) Previous issue date: 2015-02-12 / Financiadora de Estudos e Projetos / Big data is associated with large amounts of data of different types that come from different sources in a very fast way, able to add value to business and with veracity. Nowadays, many companies are looking for ways to extract useful information from this huge amount of data. This can be attained applying analytical techniques. The application of these techniques to big data is denominated big data analytics. It can influence how managers make their decisions and manage the company businesses. This influences the use of performance measurement systems (PMSs). These systems are composed by a multidimensional set of performance measures that can support decision making and business planning. This way, performance measurement systems and big data analytics can be used to support decision making and the implementation of actions. There is evidence, in the literature, that big data analytics can be used in performance measurement systems. Following this context, this study aims at investigating how companies apply the big data analytics in using performance measurement systems. To achieve this objective, a systematic literature review was carried out for checking existing studies on the relationship between big data and performance measurement system. Then, case study method was applied. The empirical findings showed that big data analytics supports the decision making process, making it more efficient and effective. The results showed that big data analytics helps PMS identify, through analyses, how past actions can influence the future performance. Such analyses are in essence descriptive and predictive and it was applied in sales process. The empirical findings from the case studies showed that big data analytics contributes mainly to the use of PMSs related to planning and to influencing behavior. Therefore, it is possible to conclude that there is a contribution when big data analytics is used in performance measurement system. / Big data está associado a grande quantidade de dados de diferentes tipos, provindos de diversas fontes de forma acelerada, capazes de trazer valor aos negócios e com veracidade. Atualmente, muitas empresas buscam formas de extrair informações úteis deste grande volume de dados. Isso pode ser feito por meio de técnicas analíticas. A aplicação dessas técnicas ao big data é denominada big data analytics que pode influenciar a forma como os gestores tomam as suas decisões e gerenciam os negócios da empresa. Isto pode afetar os sistemas de medição de desempenho (SMDs) que são compostos por um conjunto de medidas de desempenho multidimensionais capaz de apoiar a tomada de decisões e o planejamento dos negócios. Dessa forma, os sistemas de medição de desempenho e o big data analytics podem ser utilizados para apoiar a tomada de decisão e dar suporte à realização das ações. Há evidências, na literatura pesquisada, de que o big data analytics possa ser utilizado nos sistemas de medição de desempenho. Dentro deste contexto, esta pesquisa tem como objetivo investigar como as empresas usam big data analytics nos sistemas de medição de desempenho. Para alcançar o objetivo deste trabalho, primeiramente, foi realizada uma revisão sistemática da literatura para verificar as publicações existentes a respeito da relação entre big data analytics e sistema de medição de desempenho. Em seguida, o método de pesquisa utilizado foi estudo de caso múltiplo de caráter exploratório. As análises dos dados comprovaram que o big data analytics auxilia para que o processo de tomada de decisão seja mais eficiente e efetivo. Os resultados apontaram que o big data analytics auxilia o SMD a identificar como ações passadas podem influenciar o desempenho futuro por meio das análises realizadas. Essas análises são descritivas e preditivas e contribuem nas ações de venda dos produtos. Os dados empíricos provindos dos estudos de caso mostraram que big data analytics contribui principalmente para o uso dos SMDs relacionado ao planejamento e a influenciar o comportamento. Portanto, é possível concluir que existe uma contribuição quando big data analytics é utilizado no sistema de medição de desempenho.

Large amounts of data in various forms are generated at a fast pace in today´s society. This is commonly referred to as “Big Data”. Making use of Big Data has been increasingly important for both business and in research. The forest industry is generating big amounts of data during the different processes of forest harvesting. In Sweden, forest infor-mation is sent to SDC, the information hub for the Swedish forest industry. In 2014, SDC received reports on 75.5 million m3fub from harvester and forwarder machines. These machines use a global stand-ard called StanForD 2010 for communication and to create reports about harvested stems. The arrival of scalable cloud technologies that com-bines Big Data with machine learning makes it interesting to develop an application to analyze the large amounts of data produced by the forest industry. In this study, a proof-of-concept has been implemented to be able to analyze harvest production reports from the StanForD 2010 standard. The system consist of a back-end and front-end application and is built using cloud technologies such as Apache Spark and Ha-doop. System tests have proven that the concept is able to successfully handle storage, processing and machine learning on gigabytes of HPR files. It is capable of extracting information from raw HPR data into datasets and support a machine learning pipeline with pre-processing and K-Means clustering. The proof-of-concept has provided a code base for further development of a system that could be used to find valuable knowledge for the forest industry.

<div>Equipment health is the root of productivity and profitability in a company; through the use of machine learning and advancements in computing power, a maintenance strategy known as Predictive Maintenance (PdM) has emerged. The predictive maintenance approach utilizes performance and condition data to forecast necessary machine repairs. Predicting maintenance needs reduces the likelihood of operational errors, aids in the avoidance of production failures, and allows for preplanned outages. The PdM strategy is based on machine-specific data, which proves to be a valuable tool. The machine data provides quantitative proof of operation patterns and production while offering machine health insights that may otherwise go unnoticed.</div><div><br> </div><div>Purdue University's Wade Utility Plant is responsible for providing reliable utility services for the campus community. The Wade Utility Plant has invested in an equipment monitoring system for a thirty-megawatt turbine generator. The equipment monitoring system records operational and performance data as the turbine generator supplies campus with electricity and high-pressure steam. Unplanned and surprise maintenance needs in the turbine generator hinder utility production and lessen the dependability of the system.</div><div><br> </div> The work of this study leverages the turbine generator data the Wade Utility Plant records and stores, to justify equipment care and provide early error detection at an in-house level. The research collects and aggregates operational, monitoring and performance-based data for the turbine generator in Microsoft Excel, creating a dashboard which visually displays and statistically monitors variables for discrepancies. The dashboard records ninety days of data, tracked hourly, determining averages, extrema, and alerting the user as data approaches recommended warning levels. Microsoft Excel offers a low-cost and accessible platform for data collection and analysis providing an adaptable and comprehensible collection of data from a turbine generator. The dashboard offers visual trends, simple statistics, and status updates using 90 days of user selected data. This dashboard offers the ability to forecast maintenance needs, plan work outages, and adjust operations while continuing to provide reliable services that meet Purdue University's utility demands. <br>

This thesis explores the building energy performance of the domestic sector in the city of Stockholm based on the building energy declaration database. The aims of this master thesis are to analyze the big data sets of around 20,000 buildings in Stockholm region, explore the correlation between building energy performance and different internal and external affecting factors on building energy consumption, such as building energy systems, building vintages and etc. By using clustering method, buildings with different energy consumptions can be easily identified. Thereafter, energy saving potential is estimated by setting step-by-step target, while feasible energy saving solutions can also be proposed in order to drive building energy performance at city level. A brief introduction of several key concepts, energy consumption in buildings, building energy declaration and big data, serves as the background information, which helps to clarify the necessity of conducting this master thesis. The methods used in this thesis include data processing, descriptive analysis, regression analysis, clustering analysis and energy saving potential analysis. The provided building energy declaration data is firstly processed in MS Excel then reorganized in MS Access. As for the data analysis process, IBM SPSS is further introduced for the descriptive analysis and graphical representation. By defining different energy performance indicators, the descriptive analysis presents the energy consumption and composition for different building classifications. The results also give the application details of different ventilation systems in different building types. Thereafter, the correlation between building energy performance and five different independent variables is analyzed by using a linear regression model. Clustering analysis is further performed on studied buildings for the purpose of targeting low energy efficiency groups, and the buildings with various energy consumptions are well identified and grouped based on their energy performance. It proves that clustering method is quite useful in the big data analysis, however some parameters in the process of clustering needs to be further adjusted in order to achieve more satisfied results. Energy saving potential for the studied buildings is calculated as well. The conclusion shows that the maximal potential for energy savings in the studied buildings is estimated at 43% (2.35 TWh) for residential buildings and 54% (1.68 TWh) for non-residential premises, and the saving potential is calculated for different building categories and different clusters as well.

Container technologies are rapidly changing how distributed applications are executed and managed on cloud computing resources. As containers can be deployed on a large scale, there is a tremendous need for Container Orchestration tools like Kubernetes that are highly automatic in deployment, scaling, and management. In recent times, the adoption of these container technologies like Docker has seen a rise in internal usage, commercial offering, and various application fields ranging from High-Performance Computing to Geo-distributed (Edge or IoT) applications. Big Data analytics is another field where there is a trend to run applications (e.g., Apache Spark) as containers for elastic workloads and multi-tenant service models by leveraging various container orchestration tools like Kubernetes. Despite the abundant research on the performance impact of containerizing big data applications, to the best of our knowledge, the studies that focus on specific aspects like scalability and resource management are largely unexplored, which leaves a research gap to study upon. This research studies the performance impact of autoscaling a big data analytics application on Kubernetes based on autoscaling mechanisms like Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). These state-of-art autoscaling mechanisms available for scaling containerized applications on Kubernetes and the available big data benchmarking tools for generating workload on frameworks like Spark are identified through a literature review. Apache Spark is selected as a representative big data application due to its ecosystem and industry-wide adoption by enterprises. In particular, a series of experiments are conducted by adjusting resource parameters (such as CPU requests and limits) and autoscaling mechanisms to measure run-time metrics like execution time and CPU utilization. Our experiment results show that while Spark performs better execution time when configured to scale with VPA, it also exhibits overhead in CPU utilization. In contrast, the impact of autoscaling big data applications using HPA adds overhead in terms of both execution time and CPU utilization. The research from this thesis can be used by researchers and other cloud practitioners, using big data applications to evaluate autoscaling mechanisms and derive better performance and resource utilization.

Big Data analytics är en fascinerande process som lyfter fram möjligheten att utvinna djupgående insikter och dra slutsatser från omfattande datamängder, och dess tillämpningsområden sträcker sig över flera branscher och områden inklusive köksbranschen och marknadsföring som denna studie fokuserar på. Utmaningen att dra nytta av Big Data analytics för att förstå konsumentbeteenden och preferenser kan dock vara betydande, men när företag tillhandahåller de rätta resurserna, kan de på ett effektivt sätt optimera sina marknadsföringsstrategier. Denna studie syftade till att utforska vilka specifika kompetenser och förmågor företag behöver för att framgångsrikt hantera stora datamängder, samt hur Big Data analytics kan bidra till en effektivare marknadsföring inom köksbranschen. Genom att använda en induktiv forskningsansats och genomföra semistrukturerade intervjuer med fyra företag inom den svenska köksbranschen, kopplades svaren till ett teoretiskt ramverk för att analysera datan och identifiera kompetenser, förmågor och effekter inom Big Data analytics. Resultaten visade på vikten av olika kompetenser såsom teknisk kunskap, datavisualisering, machine learning, marknadsföringskunskaper, kreativt tänkande, ledarskap och kommunikation för att uppnå framgång inom Big Data analytics. Studien betonade också den avgörande betydelsen av att samla in högkvalitativ data, integrera verktyg och använda insikterna för att optimera marknadsföringsstrategier inom köksbranschen. Som förslag till framtida forskning framhåller denna studie behovet av att undersöka organisatoriska och kulturella faktorer som påverkar potentialen inom Big Data analytics. Det föreslås även att framtida forskning bör utforska andra branscher och deras specifika behov när det gäller Big Data analytics, vilket kan bredda förståelsen inom detta område. Nyckelord: Big Data analytics, effektivare marknadsföring, kompetenser, Big Data analytics förmågor, dynamiska förmågor, marknadsrelaterade förmågor, materiella resurser, immateriella resurser, mänskliga förmågor / Summary Big Data analytics is a fascinating process that highlights the ability to extract profound insights and draw conclusions from large amounts of data. Its applications span across various industries and domains, including the kitchen industry and marketing, which this study focuses on. However, harnessing the power of Big Data analytics to understand consumer behavior and preferences can be challenging. Nevertheless, when companies provide the right resources, they can effectively optimize their marketing strategies. This study aimed to explore the specific competencies and skills that companies need to successfully handle large volumes of data, as well as how Big Data analytics can contribute to more efficient marketing in the kitchen industry. Employing an inductive research approach and conducting semi-structured interviews with four companies in the Swedish kitchen industry, the findings were linked to a theoretical framework to analyze the data and identify competencies, skills, and effects within Big Data analytics. The results underscored the importance of various competencies such as technical knowledge, data visualization, machine learning, marketing expertise, creative thinking, leadership, and communication in achieving success in Big Data analytics. The study also emphasized the crucial significance of collecting high-quality data, integrating tools, and utilizing insights to optimize marketing strategies within the kitchen industry. As a suggestion for future research, this study highlights the need to examine organizational and cultural factors that impact the potential of Big Data analytics. Additionally, it is proposed that future research should explore other industries and their specific requirements regarding Big Data analytics, thereby broadening the understanding in this field.

While many large firms have implemented Big Data Analytics (BDA), it is unclear whether Small and Medium-sized Enterprises (SMEs) are ready to adopt and use this technology. This study investigates BDA implementation from the perspective of both managers and data analysts. Managers are mostly influenced by factors from the external environment, while data analysts are mostly influenced by technological factors. Hence, in this study, it is contended that managers imitate the behavior of external institutions, while data analysts mostly evaluate technology characteristics in the process of BDA implementation. The present study draws on institutional, organizational change, and diffusion of innovation theories through the lens of an imitation-evaluation perspective to investigate readiness and adoption behaviours. Accordingly, a theoretical research model was developed to explore the salient variables that impact organizational and data analysts’ readiness for implementing BDA in SMEs. To test these assertions, two surveys were conducted with 340 responses including 170 managers and 170 data analysts in SMEs in North America. The findings demonstrate that: (1) an imitation perspective plays a significant role in organizational readiness to adopt BDA; (2) uncertainty in big data technologies can intensify the effect of normative pressures on organizational readiness; (3) big data complexity, trialability, and relative advantage impact data analysts’ readiness to use big data analytics; and (4) the influence of relative advantage is attenuated by the high level of data analytics skills. These findings provide valuable contributions to the theory and practice of BDA implementation in SMEs in the BDA adoption and use literature. / Dissertation / Doctor of Business Administration (DBA)

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.

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible.