Data are often the foundation of digital innovation and are seen as a highly valuable asset for any organization. Many companies aim to put data at the core of their business, but struggle with regulating data in complex environments. Data governance becomes an integral part for data-driven business. However, only a minority of companies fully engage in data governance. Research also lacks knowledge about data governance in complex environments such as digital platforms. Therefore, this thesis examines the role of data governance in digital platforms, by researching the conceptual characteristics of platform data governance. The iterative taxonomy development process by Nickerson et al. (2013) has been used to classify the characteristics of platform data governance. The results are derived from existing literature and motivated by new insights from expert interviews as well as a case analysis of a real-life platform. The final taxonomy shows that the conceptual characteristics of platform data governance are based on the dimensions purpose, platform data, responsibilities, decision domains and compliance. The findings address challenges of data governance in inter organizational settings and help practitioners to define their own data governance. Additionally, the thesis highlights the potential for future research.

The use of IT in companies is constantly expanding, and therefore, so is the energy consumption and carbon footprint from IT. If companies do not take steps towards more sustainable IT usage, the footprint will continue to expand, toxic metals will be used in hardware and the usage of world resources will not become more circular. Few studies are made regarding internal and external factors that affect the implementation of sustainable IT. Sustainability is seen as a “core IT objective” by 90% of European IT leaders. There is a knowledge gap concerning internal and external demands within sustainable IT, since no research studies have been found. The aim of this study is to understand what large companies in Sweden do to act more sustainably in their IT usage. What demands are put on suppliers to make more sustainable solutions and which demands are put on employees internally to make their IT usage more sustainable. The research question is: “What external sustainability demands do large companies put on their suppliers of IT systems and hardware, and what internal demands do the companies have on their own IT usage?”. This study will be carried out using survey as a research strategy. Data will be collected from semi-structured interviews with employees from nine different companies as well as the collection of internal documents and documents from the websites of the companies. The data analysis method used is thematic analysis. From the interviews with employees with high knowledge in the companies sustainable IT work, five themes with sixteen different demands were found. Five of the demands were identified as new that previous research has not addressed while the remaining eleven demands can be found in previous research. Some of the demands were repeated no matter how long the company have worked with sustainable IT, such as Recycling hardware, Increasing the life cycle, Involve employees in sustainable IT, Server location and Move to cloud computing. While other demands like System boundaries, Sustainable software development, and Sustainably developed software were more common among more mature companies within sustainable IT. This study found that the two most considerable demands are the external demand Hardware can be recycled and the internal demand Recycling hardware, which implies that circularity has become a more critical topic.

With extensive data available in today's organizations, it has become increasingly important to secure valuable insights through data. As a result, the management of data to support decision-making processes is receiving increasing attention in organizations' IT strategies. The healthcare sector is no exception. However, there is an urgent need for tools that help organizations extract valuable insights from the rapidly growing volumes of data, one of the most important steps of which is data mining. So far, the healthcare sector has not found a way to harness its full potential, due to limited methods to extract useful knowledge hidden in large data sets. Knowledge gained from data mining can help healthcare to better serve patients, but there is a limited comprehensive picture of applications regarding data mining processes in healthcare. Against this background, the purpose of this study is to investigate practical dimensions of the data mining process in healthcare and further identify barriers that can inhibit this process. To answer our research question, we used a qualitative case study with semi structured interviews based on the CRISP-DM framework. Our findings indicate barriers that can inhibit the data mining process, which are related to the objectives, data availability and final reports.

Data has been recognized as an important resource in the public sector. As a result, the expectations regarding data and its potential use cases have increased. In Sweden, key stakeholders have emphasized the importance of data for data-driven purposes. However, the amount of data collected, stored and analyzed has dramatically increased, which from an organizational perspective presents new demands on how public organizations govern and control their data management. In this study, data governance in the public sector at the municipal level was investigated with the purpose of creating an overview of how data governance occurs within municipalities. The study was divided into two sections, the first phase consisted of interviews with a specific municipality, whereas the second phase consisted of a survey distributed to all 290 Swedish municipalities. The results show that the current approach to data governance presents several challenges in the municipal sector. The results also indicate that the municipal sector may potentially be more focused on information governance rather than data governance and that there may be a lack of knowledge about the distinction between data and information. The findings suggest that the municipal sector lacks the prerequisites for managing data as a valuable and strategic asset, which is a concern given the public sector’s ambitions for data to generate and enhance services for society. / Data har blivit alltmer uppmärksammad som en viktig resurs i den offentliga sektorn. I och med detta har även förhoppningarna om data och dess användningsområden ökat inom sektorn. Bara i Sverige har nyckelaktörer påpekat betydelsen av data för datadrivna ändamål. Emellertid, har mängden data som samlas in, lagras och analyseras ökat, vilket utifrån ett organisatoriskt perspektiv ställer nya krav på hur offentliga organisationer styr och kontroller deras datahantering. Med hänsyn till detta har datastyrning i den offentliga sektorn, mer specifikt på kommunal nivå, undersökts i denna studie med syftet att skapa en överblick över hur datastyrning sker inom kommuner. Studien delades in i två faser, där den första fasen innefattade inledande intervjuer med en specifik kommun, medan den andra fasen innebar en enkätundersökning som distribuerades ut till alla 290 kommuner IT-chefer eller motsvarande. Resultatet visar på att rådande arbetssätt med datastyrning visar på ett antal utmaningar i den kommunala sektorn. Vidare är det möjligt att det råder en kunskapsbrist om skillnaden mellan data och information samt att den kommunala sektorn möjligtvis är mer fokusar på informationsstyrning än datastyrning. Med hänsyn till den offentliga sektorns förhoppningar om data för att skapa och förbättra tjänster för samhället visar resultatet att den kommunala sektorn inte har förutsättningarna att hantera data som en strategisk och värdefull tillgång.

no / Efficient management of toxicity information as an enterprise asset is increasingly important for the chemical, pharmaceutical, cosmetics and food industries. Many organisations focus on better information organisation and reuse, in an attempt to reduce the costs of testing and manufacturing in the product development phase. Toxicity information is extracted not only from toxicity data but also from predictive models. Accurate and appropriately shared models can bring a number of benefits if we are able to make effective use of existing expertise. Although usage of existing models may provide high-impact insights into the relationships between chemical attributes and specific toxicological effects, they can also be a source of risk for incorrect decisions. Thus, there is a need to provide a framework for efficient model management. To address this gap, this paper introduces a concept of model governance, that is based upon data governance principles. We extend the data governance processes by adding procedures that allow the evaluation of model use and governance for enterprise purposes. The core aspect of model governance is model representation. We propose six rules that form the basis of a model representation schema, called Minimum Information About a QSAR Model Representation (MIAQMR). As a proof-of-concept of our model governance framework we develop a web application called Model and Data Farm (MADFARM), in which models are described by the MIAQMR-ML markup language. (C) 2013 Elsevier Ltd. All rights reserved.

La valorisation du patrimoine des données des organisation est mise au cœur de leur transformation digitale. Sous l’influence des données massives le système d’information doit s’adapter et évoluer. Cette évolution passe par une transformation des systèmes décisionnels mais aussi par l’apparition d’un nouveau composant du système d’information : Les lacs de données. Nous étudions cette évolution des systèmes décisionnels, les éléments clés qui l’influence mais aussi les limites qui apparaissent , du point de vue de l’architecture, sous l’influence des données massives. Nous proposons une évolution des systèmes d’information avec un nouveau composant qu’est le lac de données. Nous l’étudions du point de vue de l’architecture et cherchons les facteurs qui peuvent influencer sa conception , comme la gravité des données. Enfin, nous amorçons une piste de conceptualisation des lacs de données en explorant l’approche ligne de produit.Nouvelle versionSous l'influence des données massives nous étudions l'impact que cela entraîne notamment avec l'apparition de nouvelles technologies comme Apache Hadoop ainsi que les limite actuelles des système décisionnel.Les limites rencontrées par les systèmes décisionnels actuels impose une évolution au système d 'information qui doit s'adapter et qui donne naissance à un nouveau composant : le lac de données.Dans un deuxième temps nous étudions en détail ce nouveau composant, formalisons notre définition, donnons notre point de vue sur son positionnement dans le système d information ainsi que vis à vis des systèmes décisionnels.Par ailleurs, nous mettons en évidence un facteur influençant l’architecture des lacs de données : la gravité des données, en dressant une analogie avec la loi de la gravité et en nous concentrant sur les facteurs qui peuvent influencer la relation donnée-traitement.Nous mettons en évidence , au travers d'un cas d'usage , que la prise en compte de la gravité des données peut influencer la conception d'un lac de données.Nous terminons ces travaux par une adaptation de l'approche ligne de produit logiciel pour amorcer une méthode de formalisations et modélisation des lacs de données. Cette méthode nous permet :- d’établir une liste de composants minimum à mettre en place pour faire fonctionner un lac de données sans que ce dernier soit transformé en marécage,- d’évaluer la maturité d'un lac de donnée existant,- de diagnostiquer rapidement les composants manquants d'un lac de données existant qui serait devenu un marécage,- de conceptualiser la création des lacs de données en étant "logiciel agnostique”. / Data is on the heart of the digital transformation.The consequence is anacceleration of the information system evolution , which must adapt. The Big data phenomenonplays the role of catalyst of this evolution.Under its influence appears a new component of the information system: the data lake.Far from replacing the decision support systems that make up the information system, data lakes comecomplete information systems’s architecture.First, we focus on the factors that influence the evolution of information systemssuch as new software and middleware, new infrastructure technologies, but also the decision support system usage itself.Under the big data influence we study the impact that this entails especially with the appearance ofnew technologies such as Apache Hadoop as well as the current limits of the decision support system .The limits encountered by the current decision support system force a change to the information system which mustadapt and that gives birth to a new component: the data lake.In a second time we study in detail this new component, formalize our definition, giveour point of view on its positioning in the information system as well as with regard to the decision support system .In addition, we highlight a factor influencing the architecture of data lakes: data gravity, doing an analogy with the law of gravity and focusing on the factors that mayinfluence the data-processing relationship. We highlight, through a use case, that takingaccount of the data gravity can influence the design of a data lake.We complete this work by adapting the software product line approach to boot a methodof formalizations and modeling of data lakes. This method allows us:- to establish a minimum list of components to be put in place to operate a data lake without transforming it into a data swamp,- to evaluate the maturity of an existing data lake,- to quickly diagnose the missing components of an existing data lake that would have become a dataswamp- to conceptualize the creation of data lakes by being "software agnostic “.

This diploma thesis deals with registers of state administration in term of data quality. The main objective is to analyze the ways how to evaluate data quality and to apply appropriate method to data in business register. Analysis of possibilities of data cleansing and data quality improving and proposal of solution of found inaccuracy in business register is another objective. The last goal of this paper is to analyze approaches how to set identifier of persons and to choose suitable key for identification of persons in registers of state administration. The thesis is divided into several parts. The first one includes introduction into the sphere of registers of state administration. It closely analyzes several selected registers especially in terms of which data contain and how they are updated. Description of legislation changes, which will come into operation in the middle of year 2010, is great contribution of this part. Special attention is dedicated to the impact of these changes from data quality point of view. Next part deals with problems of legal and physical entities identifiers. This section contains possible solution how to identify entities in data from registers. Third part analyzes ways how to determine data quality. Method called data profiling is closely described and applied to extensive data quality analysis of business register. Correct metadata and information about incorrect data are the outputs of this analysis. The last chapter deals with possibilities how to solve data quality problems. There are proposed and compared three variations of solution. The paper as a whole represents compact material how to solve problems with effective using of data contained in registers of state administration. Nevertheless, proposed solutions and described approaches can be used in many other projects which deal with data quality.

This thesis is focused on Master Data Management (MDM), Customer Data Integration (CDI) area and its main domains. It is also a reference to a various theoretical directions that can be found in this area of expertise. It summarizes main aspects, domains and presents different perspectives to referenced principles. It is an exhaustive background research in area of Master Data Management with emphasis on practical use with references on authors experience and opinions. Secondary focus is directed to the field of business value of Master Data Management initiatives. Thesis presents a thought concept for initiations of MDM project. The reason for such a concept is based on current trend, where companies are struggling to determine actual benefits of MDM initiatives. There is overall accord on the subject of necessity of such initiatives, but the struggle is in area of determining actual measureable impact on company's revenue or profit. Since the MDM initiative is more of an enabling function, rather than direct revenue function, the benefit is less straight forward and therefore harder to determine. This work describes different layers and mapping of business requirements through layers for transparent linkage between enabling functions to revenue generating ones. The emphasis is given to financial benefit calculation, measurability and responsibility of business and IT departments. To underline certain conclusions thesis also presents real world interviews with possible stakeholders of MDM initiative within the company. These representatives were selected as key drivers for such an initiative. Interviews map their recognition of MDM and related terms. It also focus on their reasons and expectations from MDM. The representatives were also selected to equally represent business and IT departments, which presents interesting clash of views and expectations.

Fast evolving digital technologies lead to a rapidly changing environment where decisions have to be made in a short time. The promised solution is data-driven decision making and business intelligence. However, business intelligence has until now only been available to executives and managers whereas many of the workers' wishes for their own business analytics could not be fulfilled. The concept of self-service business intelligence (SSBI) opens the gates to democratized business intelligence for everybody. Yet the implementation of SSBI tools is proving to be extremely difficult and has led to the consequence that many SSBI projects remain unsuccessful. This thesis therefore examines the influence of self-service business intelligence on organizational structures and business processes. Furthermore, assumptions made by contemporary industry studies and best practice reports are evaluated. The case study of the Power BI implementation project at a German medical and safety technology manufacturer successfully identifies real-life challenges. Moreover, the thesis stresses the importance of data governance and data infrastructures in the context of SSBI.

While the proliferation of AI technologies offers opportunities for the workplace and its processes, their implementation in business effectively is still a challenge. Today, companies require strategic guidance in their AI management. Accordingly, there is a need for more research on the topic with a holistic approach including governance of data. Considering the challenges of the private sector and the gap in the IS research, this thesis focuses on the barriers to implementing AI in the private sector. It specifically assesses the sociotechnical mechanisms for AI evolution in the case of the agricultural machinery industry. The conducted case study suggests an overall approach including data governance for AI implementation and an alignment between the digital, it, and business strategies. Based on the research findings, this study suggests a model for AI management with three parts: The opportunities and the new data generation to realize these opportunities lie on the benefit side of the digital transformation while the sociotechnical mechanisms to tackle the barriers stand at the core. By introducing a model for AI management, the thesis offers a roadmap for the case company while bringing a new perspective to the literature and further research.