Big data analytics (BDA) has become an increasingly popular topic over the years amongst academics and practitioners alike. Big data, which is an important part of BDA, was originally defined with three Vs, being volume, velocity and variety. In later years more Vs have surfaced to better accommodate the current need. The analytics of BDA consists of different methods of analysing gathered data. Analysing data can provide insights to organisations which in turn can give organisations competitive advantage and enhance their businesses. Looking into the necessary resources needed to build big data analytic capabilities (BDAC), this thesis sought out to find how Swedish organisations enable and use BDA in their businesses. This thesis also investigated whether BDA could lead to performance enhancement and competitive advantage to organisations. A theoretical framework based on previous studies was adapted and used in order to help answer the thesis purpose. A qualitative study was deemed the most suitable for this study using semi-structured interviews. Previous studies in this field pointed to the fact that organisations may not be aware of how or why to use or enable BDA. According to current literature, different resources are needed to work in conjunction with each other in order to create BDAC and enable BDA to be utilized. Several different studies discuss challenges such as the culture of the organisation, human skills, and the need for top management to support BDA initiatives to succeed. The findings from the interviews in this study indicated that in a Swedish context the different resources, such as data, technical skills, and data driven culture, amongst others, are being used to enable BDA. Furthermore, the result showed that business process improvements are a first staple in organisations use of benefiting from BDA. This is because of the ease and security in calculating profits and effect from such an investment. Depending on how far an organisation have come in their transformation process they may also innovate and/or create products or services from insights made possible from BDA. / Big data analytics (BDA) har blivit ett populärt ämne under de senaste åren hos akademiker och utövare. Big data, som är en viktig del av BDA, var först definierad med tre Vs, volym, hastighet och varietet. På senare år har flertalet V framkommit för att bättre uttrycka det nuvarande behovet. Analysdelen i BDA består av olika metoder av analysering av data. Dataanalysering som görs kan ge insikter till organisationer, som i sin tur kan ge organisationer konkurrensfördelar och förbättra deras företag. Genom att definiera de resurser som krävs för att bygga big data analytic capabilities (BDAC), så försökte denna avhandling att visa hur svenska organisationer möjliggör och använder BDA i sina företag. Avhandlingen försökte också härleda om BDA kan leda till prestandaförbättringar och konkurrensfördelar för organisationer. Ett teoretiskt ramverk, baserat på tidigare studier, anpassades och användes för att hjälpa till att svara på avhandlingens syfte. En kvalitativ studie utsågs vara den mest passande ansatsen, tillsammans med semi-strukturerade intervjuer. Tidigare studier inom området visade på att organisationer kanske inte helt är medvetna om hur eller varför BDA möjliggörs eller kan användas. Enligt den nuvarande litteraturen så behöver olika resurser arbeta tillsammans med varandra för att skapa BDAC och möjliggöra att BDA kan utnyttjas till fullo. Flera olika studier diskuterade utmaningar såsom kulturen inom organisationen, kompetens hos anställda och att ledningen behöver stödja BDA initiativ för att lyckas. Fynden från studiens intervjuer indikerade, i ett svenskt sammanhang, att olika resurser såsom data, tekniska färdigheter och datadriven kultur bland annat, används för att möjliggöra BDA. Fortsättningsvis påvisade resultatet att affärsprocessförbättring är en första stapel i användandet av fördelarna från BDA. Anledningen till det är för att det är lättare och säkrare med beräkning av förtjänst och effekt från en sådan investering. Beroende på hur långt en organisation har kommit i deras transformationsprocess kan de också innovera och/eller skapa produkter eller tjänster som möjliggjorts av insikter från BDA.

The central question was how the relationship between trust-in-technology and intent-to-use Big Data Analytics in an organization is mediated by both Perceived Risk and Perceived Usefulness. Big Data Analytics is quickly becoming a critically important driver for business success. Many organizations are increasing their Information Technology budgets on Big Data Analytics capabilities. Technology Acceptance Model stands out as a critical theoretical lens primarily due to its assessment approach and predictive explanatory capacity to explain individual behaviors in the adoption of technology. Big Data Analytics use in this study was considered a voluntary act, therefore, well aligned with the Theory of Reasoned Action and the Technology Acceptance Model. Both theories have validated the relationships between beliefs, attitudes, intentions and usage behavior. Predicting intent-to-use Big Data Analytics is a broad phenomenon covering multiple disciplines in literature. Therefore, a robust methodology was employed to explore the richness of the topic. A deterministic philosophical approach was applied using a survey method approach as an exploratory study which is a variant of the mixed methods sequential exploratory design. The research approach consisted of two phases: instrument development and quantitative. The instrument development phase was anchored with a systemic literature review to develop an instrument and ended with a pilot study. The pilot study was instrumental in improving the tool and switching from a planned covariance-based SEM approach to PLS-SEM for data analysis. A total of 277 valid observations were collected. PLS-SEM was leveraged for data analysis because of the prediction focus of the study and the requirement to assess both reflective and formative measures in the same research model. The measurement and structural models were tested using the PLS algorithm. R2, f2, and Q2 were used as the basis for the acceptable fit measurement. Based on the valid structural model and after running the bootstrapping procedure, Perceived Risk has no mediating effect on Trust-in-Technology on Intent-to-Use. Perceived Usefulness has a full mediating effect. Level of education, training, experience and the perceived capability of analytics within an organization are good predictors of Trust-in-Technology.

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Implementing Big Data Analytics (BDA) has been widely known as a major source of competitiveness and innovation. While previous research suggests several process models and identifies critical factors for the successful implementation of BDA, there is a lack of understanding of how this organizational process is realized by its primary recipients, that is, Data Science Professionals (DSPs) whose innovation with BDA technologies stands at the core of big data-driven innovation. In particular, far less understood are the motivational and contextual factors that derive DSPs’ innovation with BDA technologies. This study proposes that commitment is the force that can attach DSPs to the BDA implementation process and motivate them to engage in innovative behaviors. It also introduces two organizational mechanisms, namely, BDA communication reciprocity and BDA leader theme-specific reputation, that can be employed to develop this constructive force in DSPs. Inspired by this, a theoretical model was developed based on the assertions of Commitment in Workplace Theory and the literature on creativity in organizations to assess the impact of DSPs’ commitment to BDA implementation and organizational context on their innovation with BDA technologies. This study theorizes that communication reciprocity and leader theme-specific reputation influence the three components of DSPs’ commitment (affective, continuance, and normative) to BDA implementation through their perceived participation in organizational decision-making and positive uncertainty, which, in turn, derive DSP’s innovation with BDA technologies. To further enrich the theorization, the moderating role of DSPs’ competency on the effect of DSPs’ components of commitment on their innovation with BDA technologies is investigated. Predictions were tested following an experimental vignette methodology with 240 subjects where the two organizational mechanisms were manipulated. Results indicate that organizational mechanisms provoke mediating psychological perceptions, though with varying strengths. In addition, results suggest that DSPs’ innovation with BDA technologies is primarily rooted in their affective and continuance commitments, and DSPs’ competency interacts with DSPs’ affective commitment to affect their innovation with BDA technologies. This research enhances the theoretical understanding of the role of commitment and organizational context in fostering DSPs’ innovation with BDA technologies. The results of this study also offer suggestions for information systems implementation practitioners on the effectiveness of organizational mechanisms that facilitate big data-driven innovation. / Thesis / Doctor of Philosophy (PhD)

Syftet med denna studie är att utreda begreppen big data och analytics. Utifrån vetenskapliga teorier om begreppen undersöks hur konsultföretag uppfattar och använder sig av big data och analytics. För att skapa en nyanserad bild har även en organisation inom vården undersökts för att få kunskap om hur de kan dra nytta av big data och analytics. Ett antal viktiga svårigheter och framgångsfaktorer kopplade till båda begreppen presenteras. De svårigheterna kopplas sedan ihop med en framgångsfaktor som anses kunna bidra till att lösa det problemet. De mest relevanta framgångsfaktorer som identifierats är att högkvalitativ data finns tillgänglig men även kunskap och kompetens kring hur man hanterar data. Slutligen tydliggörs begreppens innebörd där man kan se att big data oftast beskrivs ur dimensionerna volym, variation och hastighet och att analytics i de flesta fall syftar till att deskriptiv och preventiv analys genomförs. / The purpose of this study is to investigate the concepts of big data and analytics. The concepts are explored based on scientific theories and interviews with consulting firms. A healthcare organization has also been interviewed to get a richer understanding of how big data and analytics can be used to gain insights and how an organisation can benefit from them. A number of important difficulties and sucess facors connected to the concepts are presented. These difficulties are then linked to a sucess factor that is considered to solve the problem. The most relevant success factors identified are the avaliability of high quality data and knowledge and expertise on how to handle the data. Finally the concepts are clarified and one can see that big data is usually described from the dimensions volume, variety and velocity and analytics is usually described as descriptive and preventive analysis.

Affärsanalys är en ökande trend som många organisationer idag använder på grund av potentialen att fastställa värdefulla insikter, ökad lönsamhet och förbättrad operativ effektivitet. Något som visat sig vara problematiskt då det önskade resultatet inte alltid är en självklarhet. Syftet med studien är att undersöka hur modeföretag kan använda datadriven affärsanalys för att generera positiva insikter genom värdeskapande mekanismer. Utifrån semistrukturerade intervjuer med anställda på ett modeföretag har vi, med utgångspunkt i tidigare forskning, kartlagt hur datadriven affärsanalys brukas för att skapa värde genom att applicera en processmodell på verksamheten. Empirin resulterade i tre värdefulla insikter (1) Det studerade företaget använder affärsanalys för ökad lönsamhet (2) Företagets data tillgångar är tillräckliga för att utvinna värdefulla insikter (3) Vidare såg vi att företaget arbetar med influencers vilket är en ny affärsanalys-funktion som inte definierats i tidigare forskning. / Business analysis is an increasingly popular trend that many organisations use because of its potential to establish valuable insights, increased profitability and improved operational efficiency. Something that has proved to be rather problematic as the desired results rarely is a certainty. The purpose of the study is to examine how fashion retailers can use business analytics to generate positive insights through value-creating mechanisms by applying a process model. Based on semi-structured interviews with the employees of a fashion company and a starting point in previous research, we have mapped how business analysis can be used to obtain value. The empirical study resulted in three valuable insights (1) The examined organisation uses business analysis to increase profitability. (2) The data assets of the organisation are enough to acquire valuable insights. (3) Further we discovered that the organisation uses influencers as a valuable asset and can be categorised as a business analysis capability, previously undefined in preceding research.

To maintain public confidence in the financial system, it is essential that most financial fraud is prevented and that incidents of fraud are detected and punished. The responsibility of uncovering creatively implemented fraud is placed, in a large part, on auditors. Recent advancements in technology are helping auditors turn the tide against fraudsters. Big Data, made possible by the proliferation, widespread availability and amalgamation of diverse digital data sets, has become an important driver of technological change. Big Data analytics are already transforming the traditional audit. Sampling and testing a limited number of random samples has turned into a much more comprehensive audit that analyzes the entire population of transactions within an account, allowing auditors to flag and investigate all sorts of potentially fraudulent anomalies that were previously invisible. Artificial intelligence (AI) programs, typified by IBM’s Watson, can mimic the thought processes of the human mind and will soon be adopted by the auditing profession. Machine learning (ML) programs, with the ability to change when exposed to new data, are developing rapidly and may take over many of the decision-making functions currently performed by auditors. The SEC has already implemented pioneering fraud-detection software based on AI and ML programs. The evolution of the auditor’s role has already begun. Current accounting students must understand the traditional auditing skillset will not longer be sufficient. While facing a future with fewer auditing positions available due to increased automation, auditors will need training for roles that will be more data analytical and computer-science based.

<p>Cumulon is a system aimed at simplifying the development and deployment of statistical analysis of big data in public clouds. Cumulon allows users to program in their familiar language of matrices and linear algebra, without worrying about how to map data and computation to specific hardware and cloud software platforms. Given user-specified requirements in terms of time, monetary cost, and risk tolerance, Cumulon automatically makes intelligent decisions on implementation alternatives, execution parameters, as well as hardware provisioning and configuration settings -- such as what type of machines and how many of them to acquire. Cumulon also supports clouds with auction-based markets: it effectively utilizes computing resources whose availability varies according to market conditions, and suggests best bidding strategies for them. Cumulon explores two alternative approaches toward supporting such markets, with different trade-offs between system and optimization complexity. Experimental study is conducted to show the efficiency of Cumulon's execution engine, as well as the optimizer's effectiveness in finding the optimal plan in the vast plan space.</p> / Dissertation

Unfortunately, cyber-attacks, which are the consequence of our increasing dependence on digital technology, is a phenomenon that we have to live with today. As technology becomes more advanced and complex, so have the types of malware that are used in these cyber-attacks. Currently, targeted cyber-attacks directed at CIIs such as financial institutions and telecom companies are on the rise. A particular group of malware known as APTs, which are used for targeted attacks, are very difficult to detect and prevent due to their sophisticated and stealthy nature. These malwares are able to attack and wreak havoc (in the targeted system) within a matter of seconds; this is very worrying because traditional cyber security defence systems cannot handle these attacks. The solution, as proposed by some in the industry, is the use of BDA systems. However, whilst it appears that BDA has achieved greater success at large companies, little is known about success at smaller companies. Also, there is scarcity of research addressing how BDA is deployed for the purpose of detecting and preventing cyber-attacks on CII. This research examines and discusses the effectiveness of the use of BDA for detecting cyber-attacks and also describes how such a system is deployed. To establish the effectiveness of using a BDA, a survey by questionnaire was conducted. The target audience of the survey were large corporations that were likely to use such systems for cyber security. The research concludes that a BDA system is indeed a powerful and effective tool, and currently the best method for protecting CIIs against the range of stealthy cyber-attacks. Also, a description of how such a system is deployed is abstracted into a model of meaningful practice.

The purpose of this project is to work as an interdisciplinary team whose primary role is to mentor a team of WPI undergraduate students completing their Major Qualifying Project (MQP) in collaboration with Vestigo Ventures, LLC. (“Vestigo Ventures�) and Cogo Labs. We worked closely with the project sponsors at Vestigo Ventures and Cogo Labs to understand each sponsor’s goals and desires, and then translated those thoughts into actionable items and concrete deliverables to be completed by the undergraduate student team. As a graduate student team with a diverse set of educational backgrounds and a range of academic and professional experiences, we provided two primary functions throughout the duration of this project. The first function was to develop a roadmap for each individual project, with concrete steps, justification, goals and deliverables. The second function was to provide the undergraduate team with clarification and assistance throughout the implementation and completion of each project, as well as provide our opinions and thoughts on any proposed changes. The two teams worked together in lock-step in order to provide the project sponsors with a complete set of deliverables, with the undergraduate team primarily responsible for implementation and final delivery of each completed project.