The use of internet has increased the amount of data available and gathered. Companies are investing in big data analytics to gain insights from this data. However, the value of the analysis and decisions made based on it, is dependent on the quality ofthe underlying data. For this reason, data quality has become a prevalent issue for organizations. Additionally, failures in data quality management are often due to organizational aspects. Due to the growing popularity of decentralized organizational structures, there is a need to understand how a decentralized organization can improve data quality. This thesis conducts a qualitative single case study of an organization currently shifting towards becoming data driven and struggling with maintaining data quality within the logistics industry. The purpose of the thesis is to answer the questions: • RQ1: What is data quality in the context of logistics data? • RQ2: What are the obstacles for improving data quality in a decentralized organization? • RQ3: How can these obstacles be overcome? Several data quality dimensions were identified and categorized as critical issues,issues and non-issues. From the gathered data the dimensions completeness, accuracy and consistency were found to be critical issues of data quality. The three most prevalent obstacles for improving data quality were data ownership, data standardization and understanding the importance of data quality. To overcome these obstacles the most important measures are creating data ownership structures, implementing data quality practices and changing the mindset of the employees to a data driven mindset. The generalizability of a single case study is low. However, there are insights and trends which can be derived from the results of this thesis and used for further studies and companies undergoing similar transformations. / Den ökade användningen av internet har ökat mängden data som finns tillgänglig och mängden data som samlas in. Företag påbörjar därför initiativ för att analysera dessa stora mängder data för att få ökad förståelse. Dock är värdet av analysen samt besluten som baseras på analysen beroende av kvaliteten av den underliggande data. Av denna anledning har datakvalitet blivit en viktig fråga för företag. Misslyckanden i datakvalitetshantering är ofta på grund av organisatoriska aspekter. Eftersom decentraliserade organisationsformer blir alltmer populära, finns det ett behov av att förstå hur en decentraliserad organisation kan arbeta med frågor som datakvalitet och dess förbättring. Denna uppsats är en kvalitativ studie av ett företag inom logistikbranschen som i nuläget genomgår ett skifte till att bli datadrivna och som har problem med att underhålla sin datakvalitet. Syftet med denna uppsats är att besvara frågorna: • RQ1: Vad är datakvalitet i sammanhanget logistikdata? • RQ2: Vilka är hindren för att förbättra datakvalitet i en decentraliserad organisation? • RQ3: Hur kan dessa hinder överkommas? Flera datakvalitetsdimensioner identifierades och kategoriserades som kritiska problem, problem och icke-problem. Från den insamlade informationen fanns att dimensionerna, kompletthet, exakthet och konsekvens var kritiska datakvalitetsproblem för företaget. De tre mest förekommande hindren för att förbättra datakvalité var dataägandeskap, standardisering av data samt att förstå vikten av datakvalitet. För att överkomma dessa hinder är de viktigaste åtgärderna att skapa strukturer för dataägandeskap, att implementera praxis för hantering av datakvalitet samt att ändra attityden hos de anställda gentemot datakvalitet till en datadriven attityd. Generaliseringsbarheten av en enfallsstudie är låg. Dock medför denna studie flera viktiga insikter och trender vilka kan användas för framtida studier och för företag som genomgår liknande transformationer.

This thesis explores the implementation and improvement of data-driven processes within a Swedish industrial organization, specifically focusing on long-term maintenance planning in the energy sector. Despite the recognized benefits of data-driven decision-making, many organizations, including those in the energy sector, struggle to fully adopt this approach due to challenges such as organizational culture, knowledge management, and lack of top management support. This study addresses these challenges by investigating how data is currently utilized within a Swedish energy producer, identifying barriers to effective data use, and exploring the role of knowledge sharing in enhancing data-driven practices. Employing theoretical models such as Nonaka’s SECI model and Gökalp et. al. (2020) data analytics capability process maturity level, the research highlights that while some data-driven strategies are in place, there is a need for more standardized processes and greater involvement from top management. The study reveals significant impacts of knowledge sharing on data utilization, identifying barriers such as lack of training, scheduling conflicts, and physical and informational silos due to high-security requirements. Furthermore, it examines the gap between data availability and its utilization, attributing it to factors like the complexity of information systems, perceived data quality issues, and insufficient involvement of knowledgeable personnel. The findings suggest that addressing these issues through improved training, streamlined data systems, and strategic management of high-security constraints can enhance the overall effectiveness of data-driven decision-making. By fostering a data-driven culture and enhancing knowledge sharing- practices, the organization can better leverage its data assets, ultimately improving maintenance planning and operational efficiency in a high-security, regulated environment. / Detta arbete undersöker implementeringen och förbättringen av datadrivna processer inom en svensk industriell organisation, med särskilt fokus på långsiktig underhållsplanering i energisektorn. Trots fördelarna med datadrivet beslutsfattande, kämpar många organisationer, inklusive de inom energisektorn, med att fullt ut anta detta tillvägagångssätt på grund av utmaningar såsom organisationskultur, kunskapshantering och brist på stöd från ledningen. Denna studie tar itu med dessa utmaningar genom att undersöka hur data för närvarande används inom en svensk energiproducent, identifiera hinder för effektiv dataanvändning och utforska kunskapsdelningens roll i att förbättra datadrivna metoder. Genom att använda teoretiska modeller som Nonakas SECI-modell och Gökalp et. al. (2020) mognadsnivå för dataanalytisk förmåga, belyser studien att även om vissa datadrivna strategier är på plats, finns ett behov av mer standardiserade processer och större engagemang från ledningen. Studien visar betydande effekter av kunskapsdelning på datanyttjande, och identifierar hinder som brist på utbildning, schemakonflikter samt fysiska och informationsmässiga silos på grund av höga säkerhetskrav. Vidare undersöker den gapet mellan tillgänglighet och utnyttjande av data, vilket tillskrivs faktorer som komplexiteten i informationssystem, upplevda datakvalitetsproblem och otillräckligt inkludering av kunnig personal. Resultaten tyder på att genom att ta itu med dessa problem genom förbättrad utbildning, strömlinjeformade datasystem och strategisk hantering av höga säkerhetskrav kan den övergripande effektiviteten av datadrivet beslutsfattande förbättras. Genom att främja en datadriven kultur och förbättra kunskapsdelningspraxis kan organisationen bättre utnyttja sina dataresurser, vilket i slutändan förbättrar underhållsplanering och operationell effektivitet i en högsäkerhetsmiljö.

The examination of the role of the state in the broader context of socio-technical entanglement within data governance has received less attention due to the emergence of other powerful corporate actors (e.g. Big Tech). This thesis utilizes quantitative content analysis to investigate and compare how state agencies (the White House, FTC, Congress) and CNN framed data governance in 2022, focusing on the role of the state amid growing data privacy concerns following the Roe v. Wade overruling. The ultimate aim is to pinpoint the sociotechnical imaginaries that gain traction, having a constitutive effect on the US data governance order. Empirical results indicate that framings between the two units rather align than differ, particularly in the wake of the overruling. The evidence points to the active and multiple roles of the state and the coexistence of multiple sociotechnical imaginaries within asymmetric power dynamics, with the vision of ensuring consumer trust rising as prominent at the expense of viewing data privacy as a sovereign right of citizens. Despite the emerging perspective of viewing data privacy as a right, CNN, as a significant place of mediation, has amplified rather than challenged the market-based approach. Lastly, evidence indicates that media is not only a significant place for elevating certain sociotechnical imaginaries but could be considered one of the crucial places of initially discursively negotiated policymaking.

Information Security nowadays is becoming a very popular subject of discussion among both academics and organizations. Proper Data Governance is the first step to an effective Information Security policy. As a consequence, more and more organizations are now switching their approach to data, considering them as assets, in order to get as much value as possible out of it. Living in an IT-driven world makes a lot of researchers to approach Data Governance by borrowing IT Governance frameworks.The aim of this thesis is to contribute to this research by doing an Action Research in a big Financial Institution in the Netherlands that is currently releasing a Data Lake where all the data will be gathered and stored in a secure way. During this research a framework on implementing a proper Data Governance into the Data Lake is introduced.The results were promising and indicate that under specific circumstances, this framework could be very beneficial not only for this specific institution, but for every organisation that would like to avoid confusions and apply Data Governance into their tasks. / <p>Validerat; 20151222 (global_studentproject_submitter)</p>

This masters thesis tackles the issue of process management in a financial institution, spe-cifically the analysis and optimization of the creation of a certain management report. The first part of this masters thesis is dedicated towards explaining basic theoretical concepts, which will help the reader correctly understand the rest of the paper. There you can find explained the principles of reporting and process management, which will introduce the reader to the issues of processes, their analysis and subsequent optimization. The practical portion first introduces the reader to the particular financial institution, then it describes the organization structure and the activities of data governance, thorough analysis of the particular process, the identification of and a suggestion of optimization which should lead to a more effectively working employees as well as the elimination of narrow places of the process. The conclusion of the masters thesis introduces steps of real implementation of the suggested solutions.

Abstract Products and services are critical for companies as they create the foundation for companies’ financial success. Twenty per cent of company products typically account for some eighty per cent of sales volume. Nevertheless, the product portfolio decisions — how to strategically renew company product offering — tend to involve emotions, pet products and who-shout-the-loudest mentality while facts, numbers, and quantitative analyses are missing. Profitability is currently measured and reported at a company level, and firms seem unable to measure product-level profitability in a constant way. Consequently, companies are unable to maintain and renew their product portfolio in a strategically or commercially balanced way. The main objective of this study is to provide a data-driven product portfolio management (PPM) concept, which recognises and visualises in real-time and based on facts which company products are concurrently strategic and profitable, and what is the share of them in the product portfolio. This dissertation is a qualitative study to understand the topical area by the means combining literature review, company interviews, observations, and company internal material, to take steps towards data-driven decision-making in PPM. This study indicates that company data assets need to be combined and governed company-widely to realise the full potential of company strategic assets — the DATA. Data must be governed separately from business IT technology and beyond it. Beyond data and technology, the data-driven company culture must be adopted first. The data-driven PPM concept connects key business processes, business IT systems and several concepts, such as productization, product lifecycle management and PPM. The managerial implications include, that the shared understanding of the company products is needed, and the commercial and technical product structures are created accordingly, as they form the backbone of the company business as the skeleton to gather all product-related business-critical information for product-level profitability analysis. Also, product classification for strategic, supportive and non-strategic is needed, since the strategic nature of the product can change during the entire product lifecycle, e.g. due to the technology obsolescence, disruptive innovations by competitors, or for any other reason. / Tiivistelmä Tuotteet ja palvelut ovat yrityksille kriittisiä, sillä ne luovat perustan yritysten taloudelliselle menestykselle. Kaksikymmentä prosenttia yrityksen tuotteista edustaa tyypillisesti noin kahdeksaakymmentä prosenttia myyntimääristä. Siitä huolimatta tuoteporfoliopäätöksiin — kuinka strategisesti uudistetaan yrityksen tuotetarjoomaa — liittyy tunteita, lemmikkituotteita ja kuka-huutaa-kovimmin -mentaliteettia faktojen, numeroiden ja kvantitatiivisten analyysien puuttuessa. Kannattavuutta mitataan ja raportoidaan tällä hetkellä yritystasolla, ja yritykset eivät näyttäisi pystyvän mittaamaan tuotetason kannattavuutta johdonmukaisesti. Tämä estää yrityksiä ylläpitämästä ja uudistamasta tuotevalikoimaansa strategisesti tai kaupallisesti tasapainoisella tavalla. Tämän tutkimuksen päätavoite on tarjota dataohjattu (data-driven) tuoteportfoliohallinnan konsepti, joka tunnistaa ja visualisoi reaaliajassa ja faktapohjaisesti, mitkä yrityksen tuotteet ovat samanaikaisesti strategisia ja kannattavia ja mikä on niiden osuus tuoteportfoliossa. Tämä väitöskirja on laadullinen tutkimus, jossa yhdistyy kirjallisuuskatsaus, yrityshaastattelut, havainnot ja yritysten sisäinen dokumentaatio, joiden pohjalta pyritään kohti dataohjautuvaa päätöksentekoa tuoteportfolion hallinnassa. Tämä tutkimus osoittaa, että yrityksen data assettit on yhdistettävä ja hallittava yrityksenlaajuisesti, jotta yrityksen strategisten assettien — DATAN — potentiaali voidaan hyödyntää kokonaisuudessaan. Data on hallittava erillään yrityksen IT-teknologiasta ja sen yläpuolella. Ennen dataa ja teknologiaa on omaksuttava dataohjattu yrityskulttuuri. Dataohjatun tuoteportfolionhallinnan konsepti yhdistää keskeiset liiketoimintaprosessit, liiketoiminnan IT-järjestelmät ja useita konsepteja, kuten tuotteistaminen, tuotteen elinkaaren hallinta ja tuoteportfolion hallinta. Yhteisymmärrys yrityksen tuotteista ja sekä kaupallisen että teknisen tuoterakenteet luominen vastaavasti on ennakkoedellytys dataohjatulle tuoteportfolion hallinnalle, koska ne muodostavat yrityksen liiketoiminnan selkärangan, joka yhdistää kaikki tuotteisiin liittyvät liiketoimintakriittiset tiedot tuotetason kannattavuuden analysoimiseksi. Lisäksi tarvitaan tuotteiden kategorisointi strategisiin, tukeviin ja ei-strategisiin tuotteisiin, koska tuotteen strateginen luonne voi muuttua tuotteen elinkaaren aikana, johtuen esimerkiksi teknologian vanhenemisesta, kilpailijoiden häiritsevistä innovaatioista tai mistä tahansa muusta syystä.

<p>Data Governance and Data Privacy are critical aspects of organizational management that are widely utilized across all organizational scales. However, this research focused specifically on the significance of Data Governance and Data Privacy in Small and Medium Enterprises (SMEs). While the importance of maintaining these systems is paramount across all organizations, the challenges faced by SMEs in maintaining these systems are greater due to their limited resources. These challenges include potential errors such as data leaks, use of corrupted data, or insufficient data, as well as the difficulty in identifying clear roles and responsibilities regarding data handling. To address these challenges, this research investigated the impact of utilizing Lean Six Sigma (LSS) tools and practices to overcome the anticipated gaps and challenges in SMEs. The qualitative methodology utilized is a grounded theory design, chosen due to the limited understanding of the best LSS practices for achieving data governance and data privacy in SMEs and how LSS can improve the adoption of data governance concerning privacy in SMEs. Data were collected using semi-structured interview questions that were reviewed by an expert panel and pilot tested. The sampling method included purposive, snowballing, and theoretical sampling, resulting in 20 participants being selected for interviews. Open, axial, and selective coding were performed, resulting in the development of a grounded theory. The obtained data were imported into NVivo, a qualitative analysis software program, to compare responses, categorize them into themes and groups, and develop a conceptual framework for Data Governance and Data Privacy. An iterative data collection and analysis approach was conducted to ensure that all aspects were considered. The applied grounded theory resulted in retrieving the themes used to generate a theory from the participants’ descriptions of LSS, SMEs, data governance, and data privacy. Finally, ISM technique has been applied to identify the relationships between the concepts and factors resulted from the grounded theory. It helps arranging the levels the criteria, drawing the relationships in a flowchart, and providing valuable insights to the researcher. </p>

The exceeding supply over demand and very hard competitive conditions are nowadays the main features of the majority of sectors. A successful company is the one that is able to satisfy specific customers' needs, the one that has efficient cooperation with its suppliers throughout the whole supply chain and also the one that is able to speed up the in-house information exchange. Thus the company has to seek constantly new and innovative solutions. This is not possible without standardization and automatization of business processes. This master's thesis is dedicated to one of the possible solutions -- the Product Information Management (PIM). Since it is intended for business managers (without deep IT knowledge), at the beginning it answers the question why it is so important to know master data and to manage it. It specializes in managing product data, brings its comprehensive overview and identifies the advantages and drawbacks of the implementation as well as financial and organizational impacts. The consecutive chapter deals with simplified yet applicable approach to data management analysis (with emphasis on the PIM) and based on research, it mentions main mistakes of the implementation. In addition to the overview of main vendors of the PIM solution, it presents the latest trends in the PIM. Besides internal data synchronization, the thesis analyses several product standards -- the fundamental step towards external data synchronization, the key topic of the practical part. The whole thesis is conceived to provide an organization with a simple yet compact and therefore very effective tool for master product data insight and thus to help it to gain a competitive advantage.

Online disinformation damage on the world’s democracy has been critical. Yet, states fail to handle online content harms. Due to exception from legal liability for hosted content, Facebook, used by a third of the world population, operates ‘duty-free’ along with other social media companies.Concerned with solutions, this has given rise to the idea in studies that social resistance could be one of the most effective ways for combating disinformation. However, how exactly do we resist, is an unsettled subject. Are there any socially-driven processes against disinformation happening out there?This paper aimed to identify such processes for giving a boost to theory-building around the topic. Two central evidence cases were developed: #IAmHere digital movement fighting disinformation and innovative tool ‘Who is Who’ for distinguishing fake accounts. Based on findings, I argue that efforts by even a very small part of society can have a significant impact on defeating online disinformation. This is because digital activism shares phenomenal particularities for shaping online political discourse around disinformation. Tools such as ‘Who is Who’, on the other hand, build social resilience against the issue, also giving boost digital activists for mass reporting of disinformation content. User-driven solutions have significant potential for further research.Keywords: Online disinformation; algorithms; digital activism; user-driven solutions.