This thesis is focused on Master Data Management of Product Data. At present, most publications on the topic of MDM take into account customer data, and a very limited number of sources focus solely on product data. Some resources actually do attempt to cover MDM in full-depth. Even those publications are typically are very customer oriented. The lack of Product MDM oriented literature became one of the motivations for this thesis. Another motivation was to outline and analyze specifics of Product MDM in context of its implementation and software requirements for a vendor of MDM application software. For this I chose to create and describe a methodology for implementing MDM of product data. The methodology was derived from personal experience on projects focused on MDM of customer data, which was applied on findings from the theoretical part of this thesis. By analyzing product data characteristics and their impacts on MDM implementation as well as their requirements for application software, this thesis helps vendors of Customer MDM to understand the challenges of Product MDM and therefore to embark onto the product data MDM domain. Moreover this thesis can also serve as an information resource for enterprises considering adopting MDM of product data into their infrastructure.

Masterdata är viktigt för företag att ha kontroll över, vilket underlättas med masterdatahantering. Masterdata används i hela företaget vilket gör det komplext att hantera och kräver struktur samt en gemensam bild. Med de tekniska möjligheter som finns idag kan dessa hjälpa till att bibehålla god masterdatakvalitet. För att det ska ske behöver dessa teknologier även integreras i systemen. Syftet med studien är därför att bidra till utvecklingen av masterdatahantering. Detta sker genom att applicera ett befintligt ramverk på ett fallföretag för att undersöka om tekniska utvecklingsmöjligheter kan identifieras. Vid applicering av ett befintligt ramverk undersöks även vilka faktorer som påverkar hur ett större företags masterdatahantering beskrivs. För att möjliggöra masterdatahantering finns olika ramverk tillgängliga. The Seven Building Blocks of MDM (Radcliffe, 2007) är ett av flera och i denna studie appliceras det på fallföretaget för att sammanställa empiri utifrån ramverket. I studien framkommer det att The Seven Building Blocks of MDM är övergripande och omfattar de delar som andra ramverk inom litteraturen tar upp. Genom att applicera ramverket framkom ett behov av ytterligare teknologisk infrastruktur hos fallföretaget. Med hjälp av litteratur utöver det befintliga ramverket upptäcktes det att en masterdataplattform skulle kunna bidra till utvecklingen av masterdatahanteringen. Att avgöra vilka konkreta tekniska möjligheter som finns enbart genom ramverket visade studien var svårt. Studiens slutsats är att det är svårt att identifiera vilka konkreta tekniska utvecklingsmöjligheter som finns för ett större företag med enbart The Seven Building Blocks of MDM som grund.

Syfte: Syftet är att identifiera vilka krav som ställs på Företag X produktinformation inom miljömässig och social hållbarhet idag och i framtiden från kunder, konsumenter och myndigheter, samt hur hanteringen av produktrelaterad masterdata och information på Företag X kan förbättras. Metod: Gemensamt för alla forskningsfrågor är dess kvalitativa forskningsmetod, induktiva ansats, semistrukturerade intervjuer med 11 respondenter och etiska överväganden. Likväl tillämpas ett icke sannolikhetsurval och därigenom ett snöbollsurval genom en kontakt på fallföretaget Företag X, för att intervjua adekvata respondenter. Till forskningsfrågor 1 och 2 antogs en representativ och informationsrik/avslöjande fallstudie baserat på fallföretaget Företag X och vid analys av forskningsfrågorna tillämpades pattern matching. För forskningsfråga 3 användes multipla fallstudier genom skapandet av fyra olika scenarion (fall), där analysen bestod av en komparativ analys och pattern matching. Slutsats: Studien har identifierat flera önskvärda krav från kunder på produktinformation inom miljömässig och social hållbarhet, medan myndigheterna utgör indirekta krav för att skapa en efterfråga på produktinformation och således stödja intressenter att göra välgrundade beslut. Dagens krav speglar sig huvudsakligen av transparens och spårbarhet inom både miljömässig och social hållbarhet. År 2045 utgörs kraven på produktinformation av bevis och certifiering. Kunder, konsumenter och myndigheter ställer utökade krav på biologisk mångfald, lojalitet mot sin befolkning, transparens samt socialt ansvarstagande genom prisgarantier och priser som passar alla plånböcker. Vidare påvisar studien flertal förbättringsåtgärder Företag X kan vidta för att förbättra hanteringen av produktinformation, där tydligare ansvarsområden, kontinuerlig dataunderhåll och kontroll samt tydligare rutiner och policys är några av möjliga förbättringsåtgärder. Studiens bidrag: Fördjupad inblick i vilka krav som ställs på produktinformation inom miljömässig och social hållbarhet möjliggör en ökad förståelse och förenklad prioritering för Företag X och likartade hälsokostföretag. Likväl kan identifierade förbättringsåtgärder för hantering av produktrelaterad masterdata och information bidra till att skapa en förbättrad hantering och en mer stödjande organisation, vilket kan generaliseras till likartade hälsokostföretag. Studien bidrar till en djupare förståelse för vilka krav som ställs på produktinformation utifrån ett hållbarhetsperspektiv, där förståelsen kan möjliggöra mer samarbete mellan hälsokostföretag och dess intressenter, vilket skapar en bild av samhällets roll i relation till studiens syfte. De framtida krav som ställs på produktinformation inom miljömässig och social hållbarhet kan bidra till organisatoriskt lärande och möjliggöra ett proaktivt agerande hos hälsokostföretag. Nyckelord: Miljömässig hållbarhet, social hållbarhet, produktinformation, legala krav, önskvärda krav, hälsokostföretag, masterdata, master data management, informationshantering

This thesis deals with the Master Data Management (MDM), specifically its implementation. The main objectives are to analyze and capture the general approaches of MDM implementation including best practices, describe and evaluate the implementation of MDM project using Microsoft SQL Server 2008 R2 Master Data Services (MDS) realized in the Czech environment and on the basis of the above theoretical background, experiences of implemented project and available technical literature create a general procedure for implementation of the MDS tool. To achieve objectives above are used these procedures: exploration of information resources (printed, electronic and personal appointments with consultants of Clever Decision), cooperation on project realized by Clever Decision and analysis of tool Microsoft SQL Server 2008 R2 Master Data Services. Contributions of this work are practically same as its goals. The main contribution is creation of a general procedure for implementation of the MDS tool. The thesis is divided into two parts. The first (theoretically oriented) part deals with basic concepts (including definition against other systems), architecture, implementing styles, market trends and best practices. The second (practically oriented) part deals at first with implementation of realized MDS project and hereafter describes a general procedure for implementation of the MDS tool.

Le manque de qualité des données continue d'avoir un impact considérable pour les entreprises. Ces problèmes, aggravés par la quantité de plus en plus croissante de données échangées, entrainent entre autres un surcoût financier et un rallongement des délais. De ce fait, trouver des techniques efficaces de correction des données est un sujet de plus en plus pertinent pour la communauté scientifique des bases de données. Par exemple, certaines classes de contraintes comme les Dépendances Fonctionnelles Conditionnelles (DFCs) ont été récemment introduites pour le nettoyage de données. Les méthodes de nettoyage basées sur les CFDs sont efficaces pour capturer les erreurs mais sont limitées pour les corriger . L’essor récent de la gestion de données de référence plus connu sous le sigle MDM (Master Data Management) a permis l'introduction d'une nouvelle classe de règle de qualité de données: les Règles d’Édition (RE) qui permettent d'identifier les attributs en erreur et de proposer les valeurs correctes correspondantes issues des données de référence. Ces derniers étant de très bonne qualité. Cependant, concevoir ces règles manuellement est un processus long et coûteux. Dans cette thèse nous développons des techniques pour découvrir de manière automatique les RE à partir des données source et des données de référence. Nous proposons une nouvelle sémantique des RE basée sur la satisfaction. Grace à cette nouvelle sémantique le problème de découverte des RE se révèle être une combinaison de la découverte des DFCs et de l'extraction des correspondances entre attributs source et attributs des données de référence. Nous abordons d'abord la découverte des DFCs, en particulier la classe des DFCs constantes très expressives pour la détection d'incohérence. Nous étendons des techniques conçues pour la découverte des traditionnelles dépendances fonctionnelles. Nous proposons ensuite une méthode basée sur les dépendances d'inclusion pour extraire les correspondances entre attributs source et attributs des données de référence avant de construire de manière automatique les RE. Enfin nous proposons quelques heuristiques d'application des ER pour le nettoyage de données. Les techniques ont été implémenté et évalué sur des données synthétiques et réelles montrant la faisabilité et la robustesse de nos propositions. / Dirty data continues to be an important issue for companies. The datawarehouse institute [Eckerson, 2002], [Rockwell, 2012] stated poor data costs US businesses $611 billion dollars annually and erroneously priced data in retail databases costs US customers $2.5 billion each year. Data quality becomes more and more critical. The database community pays a particular attention to this subject where a variety of integrity constraints like Conditional Functional Dependencies (CFD) have been studied for data cleaning. Repair techniques based on these constraints are precise to catch inconsistencies but are limited on how to exactly correct data. Master data brings a new alternative for data cleaning with respect to it quality property. Thanks to the growing importance of Master Data Management (MDM), a new class of data quality rule known as Editing Rules (ER) tells how to fix errors, pointing which attributes are wrong and what values they should take. The intuition is to correct dirty data using high quality data from the master. However, finding data quality rules is an expensive process that involves intensive manual efforts. It remains unrealistic to rely on human designers. In this thesis, we develop pattern mining techniques for discovering ER from existing source relations with respect to master relations. In this set- ting, we propose a new semantics of ER taking advantage of both source and master data. Thanks to the semantics proposed in term of satisfaction, the discovery problem of ER turns out to be strongly related to the discovery of both CFD and one-to-one correspondences between sources and target attributes. We first attack the problem of discovering CFD. We concentrate our attention to the particular class of constant CFD known as very expressive to detect inconsistencies. We extend some well know concepts introduced for traditional Functional Dependencies to solve the discovery problem of CFD. Secondly, we propose a method based on INclusion Dependencies to extract one-to-one correspondences from source to master attributes before automatically building ER. Finally we propose some heuristics of applying ER to clean data. We have implemented and evaluated our techniques on both real life and synthetic databases. Experiments show both the feasibility, the scalability and the robustness of our proposal.

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.

In current information systems the requirement to integrate disparate applications into cohesive package is greatly accented. While well-established technologies facilitating functional and comunicational integration (ESB, message brokes, web services) already exist, tools and methodologies for continuous integration of disparate data sources on enterprise-wide level are still in development. Master Data Management (MDM) is a major approach in the area of data integration and referrential data management in particular. It encompasses the referrential data integration, data quality management and referrential data consolidation, metadata management, master data ownership, principle of accountability for master data and processes related to referrential data management. Thesis is focused on technological aspects of MDM implementation realized via introduction of centrallized repository for master data -- Master Data Integration Hub (MDI Hub). MDI Hub is an application which enables the integration and consolidation of referrential data stored in disparate systems and applications based on predefined workflows. It also handles the master data propagation back to source systems and provides services like dictionaries management and data quality monitoring. Thesis objective is to cover design and implementation aspects of MDI Hub, which forms the application part of MDM. In introduction the motivation for referrential data consolidation is discussed and list of techniques used in MDI Hub solution development is presented. The main part of thesis proposes the design of MDI Hub referrential architecture and suggests the activities performed in process of MDI Hub implementation. Thesis is based on information gained from specialized publications, on knowledge gathererd by delivering projects with companies Adastra and Ataccama and on co-workers know-how and experience. Most important contribution of thesis is comprehensive view on MDI Hub design and MDI Hub referrential architecture proposal. MDI Hub referrential architecture can serve as basis for particular MDI Hub implementation.

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.