This thesis investigates human product interaction with a focus on the physical experience provided by products. The differences of users&#039 / perceptions are discussed according to the differences of bodily experiences served by products. The interaction with products is taken as a holistic experience phenomenon, and in order to assess users&#039 / understandings and evaluations about the experience with products / perceived pragmatic qualities, perceived hedonic qualities and elicited emotional reactions are analyzed. The research is conducted by means of surveys in order to compare users&#039 / perceptual differences in relation to two different product groups: automobiles and the iPhone, which differ in content of interaction, namely one serves a more physical (bodily) experience while the other a more virtual one. In order to find out the perceptual differences, verbal descriptions of perceived qualities and emotional states are used as measurement tools. A list consisting of bipolar word pairs in relation with pragmatic qualities, hedonic qualities and emotional reactions has been composed, and perceptual differences are investigated through the bipolar word pairs&#039 / relevancy levels according to the product. In addition, in order to show that meaning associations related to the same verbal description are context dependent, the meanings that are associated with the same word pairs for both products are investigated. Apparent differences between the relevant word pairs of the two different product groups have been observed, in addition with pragmatic qualities&#039 / higher relevancy scores compared to hedonic qualities and emotional reactions in defining users&#039 / interactions with products.

The amount of data accumulated by organizations have grown significantly during the last couple of years, increasing the importance of data quality. Ensuring data quality for large amounts of data is a complicated task, but crucial to subsequent analysis. This study investigates how to maintain and improve data quality in a data warehouse. A case study of the errors in a data warehouse was conducted at the Swedish company Kaplan, and resulted in guiding principles on how to improve the data quality. The investigation was done by manually comparing data from the source systems to the data integrated in the data warehouse and applying a quality framework based on semiotic theory to identify errors. The three main guiding principles given are (1) to implement a standardized format for the source data, (2) to implement a check prior to integration where the source data are reviewed and corrected if necessary, and (3) to create and implement specific database integrity rules. Further work is encouraged on establishing a guide for the framework on how to best perform a manual approach for comparing data, and quality assurance of source data. / Mängden data som ackumulerats av organisationer har ökat betydligt under de senaste åren, vilket har ökat betydelsen för datakvalitet. Att säkerställa datakvalitet för stora mängder data är en komplicerad uppgift, men avgörande för efterföljande analys. Denna studie undersöker hur man underhåller och förbättrar datakvaliteten i ett datalager. En fallstudie av fel i ett datalager på det svenska företaget Kaplan genomfördes och resulterade i riktlinjer för hur datakvaliteten kan förbättras. Undersökningen gjordes genom att manuellt jämföra data från källsystemen med datat integrerat i datalagret och genom att tillämpa ett kvalitetsramverk grundat på semiotisk teori för att kunna identifiera fel. De tre huvudsakliga riktlinjerna som gavs är att (1) implementera ett standardiserat format för källdatat, (2) genomföra en kontroll före integration där källdatat granskas och korrigeras vid behov, och (3) att skapa och implementera specifika databasintegritetsregler. Vidare forskning uppmuntras för att skapa en guide till ramverket om hur man bäst jämför data genom en manuell undersökning, och kvalitetssäkring av källdata.