Detta examensarbete har inspirerats av det inkonsekventa tillämpandet av olika kännetecken vid definiering av Big Data och hur det vidsträckta arbetet som expertrollen för konceptet går att förhålla till just dessa kännetecken. Studiens syfte är således att ge en klarare bild över vad den diffusa rollen Data Scientist går ut på och lyfta fram vad de grundläggande uppgifterna i rollen innebär med dessa kännetecken som riktlinjer. Information om rollen har samlats in genom semi-strukturerade intervjuer med Data Scientists i verksamheter av varierande typer och storlekar. Studiens analys ger målande beskrivningar över hur arbetet för deltagande respondenter ser ut, och fastslår hur deras arbetsuppgifter förhåller sig till olika kännetecken för Big Data. Studiens resultat skildrar hur arbetet för de olika respondenterna förhåller sig till definitionen för Big Data, och hur arbetet skiljer sig beroende på vilken typ och storlek av verksamhet som Data Scientists är verksam i. Resultatet belyser också att arbetet för Data Scientists går att gemensamt förhålla till kännetecknen Value, Visualization och Validity vilket besvarar studiens grundläggande frågeställningen. Resultatet och undersökningen i sig reflekteras över i uppsatsens diskussionsdel där upptäckter som gjorts under arbetets gång skildras, både om Big Data som koncept och om rollen Data Scientist, vilket bland annat ger förslag på vidare studier som kan leda till kategoriseringar av rollen.

The world’s data sets are growing exponentially every day due to the large number of devices generating data residue across the multitude of global data centers. What to do with the massive data stores, how to manage them and defining who are performing these tasks has not been adequately defined and agreed upon by academics and practitioners. Data science is a cross disciplinary, amalgam of skills, techniques and tools which allow business organizations to identify trends and build assumptions which lead to key decisions. It is in an evolutionary state as new technologies with capabilities are still being developed and deployed. The data science tasks and the data scientist skills needed in order to be successful with the analytics across the data stores are defined in this document. The research conducted across twenty-two academic articles, one book, eleven interviews and seventy-eight surveys are combined to articulate the convergence on the terms data science. In addition, the research identified that there are five key skill categories (themes) which have fifty-five competencies that are used globally by data scientists to successfully perform the art and science activities of data science. Unspecified portions of statistics, technology programming, development of models and calculations are combined to determine outcomes which lead global organizations to make strategic decisions every day. This research is intended to provide a constructive summary about the topics data science and data scientist in order to spark the dialogue for us to formally finalize the definitions and ultimately change the world by establishing set guidelines on how data science is performed and measured.

This thesis deals with the analysis of Big Data in healthcare. The aim is to define the term Big Data, to acquaint the reader with data growth in the world and in the health sector. Another objective is to explain the concept of a data expert and to define team members of the data experts team. In following chapters phases of the Big Data analysis according to methodology of EMC2 company are defined and basic technologies for analysing Big Data are described. As beneficial and interesting I consider the part dealing with definition of tasks in which Big Data technologies are already used in healthcare. In the practical part I perform the Big Data analysis task focusing on meteorotropic diseases in which I use real medical and meteorological data. The reader is not only acquainted with the one of recommended methods of analysis and with used statistical models, but also with terms from the field of biometeorology and healthcare. An integral part of the analysis is also information about its limitations, the consultation on results, and conclusions of experts in meteorology and healthcare.

Nowadays, becoming data-driven is the vision of almost all organizations. However, achieving this vision is not as easy as it may look like and there are many factors that affect, enable, support and sustain the data-driven ecosystem in an organization. Among these factors, this study focuses on data democracy which can be defined as the intra-organizational open data that aims to empower the employees getting faster and easier access to data in order to benefit from the business insight they need without the interfere of external help.  In the existing literature, while the importance of becoming data-driven has been widely discussed, when it comes to data democracy within organizations, there is a noticeable gap. As a result, this master’s thesis aims to justify the importance and role of the data democracy in becoming a data-driven organization, focusing on the case of a Swedish bank. Additionally, it intends to provide extra investigation on the role of data analytics tools in achieving data democracy.  The results of the study show that there is a strong connection between the benefits of the empowering different actors of the organization with the needed data knowledge, and the speeding up of the data-driven transformation journey. Based on the study, shared data and the availability of data to a larger number of stakeholders inside an organization result into a better understanding of different aspects of the problems, simplify the data-driven decision making and make the organization more data-driven. In the process of becoming data-driven, the organizations should provide the analytics tools not only to the data specialists but even to the non-data technical people. And by offering the needed support, training and collaboration possibilities between the two groups of employees (data specialists and non-data specialists), it should be attempted to enable the second group to extract the insight from the data, independently from the help of the data scientists.  An organization can succeed in the path of becoming data-driven when they invest on the reusable capabilities of its employees, by discovering the data science skills across various departments and turning their domain experts into citizen data scientists of the organization.