El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The microfinance sector has a strategic role since they facilitate integration and development of all social classes to sustained economic growth. In this way the actual point is the exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage due to the lack of valuable and quality information for decision-making and process improvement. The Master Data Management (MDM) give a new way in the Data management, reducing the gap between the business perspectives versus the technology perspective In this regard, it is important that the organization have the ability to implement a data management model for Master Data Management. This paper proposes a Master Data management maturity model for microfinance sector, which frames a series of formal requirements and criteria providing an objective diagnosis with the aim of improving processes until entities reach desired maturity levels. This model was implemented based on the information of Peruvian microfinance organizations. Finally, after validation of the proposed model, it was evidenced that it serves as a means for identifying the maturity level to help in the successful of initiative for Master Data management projects. / Revisión por pares

The aim of the thesis is to prove measurability of the Data Quality which is a relatively subjective measure and thus is difficult to measure. In doing this various aspects of measuring the quality of data are analyzed and a Complex Data Quality Monitoring System is introduced with the aim to provide a concept for measuring/monitoring the overall Data Quality in an organization. The system is built on a metrics hierarchy decomposed into particular detailed metrics, dimensions enabling multidimensional analyses of the metrics, and processes being measured by the metrics. The first part of the thesis (Chapter 2 and Chapter 3) is focused on dealing with Data Quality, i.e. provides various definitions of Data Quality, gives reasoning for the importance of Data Quality in a company, and presents some of the most common tools and solutions that target to managing Data Quality in an organization. The second part of the thesis (Chapter 4 and Chapter 5) builds on the previous part and leads into measuring Data Quality using metrics, i.e. contains definition and purpose of Data Quality Metrics, places them into the multidimensional context (dimensions, hierarchies) and states five possible decompositions of Data Quality metrics into detail. The third part of the thesis (Chapter 6) contains the proposed Complex Data Quality Monitoring System including description of Data Quality Management related dimensions and processes, and most importantly detailed definition of bottom-level metrics used for calculation of the overall Data Quality.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The microfinance sector has a strategic role since they facilitate integration and development of all social classes to sustained economic growth. In this way the actual point is the exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage due to the lack of valuable and quality information for decision-making and process improvement. The Master Data Management (MDM) give a new way in the Data management, reducing the gap between the business perspectives versus the technology perspective In this regard, it is important that the organization have the ability to implement a data management model for Master Data Management. This paper proposes a Master Data management maturity model for microfinance sector, which frames a series of formal requirements and criteria providing an objective diagnosis with the aim of improving processes until entities reach desired maturity levels. This model was implemented based on the information of Peruvian microfinance organizations. Finally, after validation of the proposed model, it was evidenced that it serves as a means for identifying the maturity level to help in the successful of initiative for Master Data management projects. / Revisión por pares

Connectivity provided by technology and liberation of trade have led to a globalization of organizations, affecting supply chains to expand in complexity. As a result, many organizations today have challenges of managing information in a consistent manner throughout a complex system environment. This study aims to identify the most valuable attributes of a solution for managing master data, in an efficient and consistent manner, across an extended and complex supply chain. Master data, such as products, customers and suppliers, can be defined as valuable core business information, since it is vital for supporting business operations. A requirements elicitation was performed, including interviews conducted internally with employees at IFS and externally with customers. Furthermore, a requirements analysis resulted in a specification of requirements including the most desirable attributes of a future Master Data Management (MDM) solution. Five main themes of the attributes were identified; architecture, availability and integration, governance, user interface and lifecycle management. The study contributes to the area of research, by identifying challenges and valuable attributes to consider when developing or investing in a solution for MDM.

This diploma thesis deals with the Product Information Management (PIM) and compares PIM software tools. Its goal is to introduce the area of the PIM systems in Czech language. Next subgoal is to define system of criteria. It is also necessary to achieve the last goal -- to analyze and compare PIM software. The method I used is the exploration of information sources; obtaining information through email communication and use of empirical knowledge to define system of criteria. The contribution of this work is the same as its goals. The work is divided into two parts. The first theoretical part deals with PIM definitions, context, functionality, architectures and PIM market developing. The second practical part involves selecting of particular PIM software tools, defining system of criteria and comparison of PIM software tools.

Abstract As the business complexity, number of customers continues to grow and customers evolve into multinational organisations that operate across borders, many companies are faced with great challenges in the way they manage their customer data. In today’s business, a single customer may have a relationship with several entities of an organisation, which means that the customer data is collected through different channels. One customer may be described in different ways by each entity, which makes it difficult to obtain a unified view of the customer. In companies where there are several sources of data and the data is distributed to several systems, data environments become heterogenic. In this state, customer data is often incomplete, inaccurate and inconsistent throughout the company. This thesis aims to study how organisations with heterogeneous customer data sources implement the Master Data Management (MDM) concept to achieve and maintain high customer data quality. The purpose is to provide recommendations for how to achieve successful customer data management using MDM based on existing literature related to the topic and an interview-based empirical study. Successful customer data management is more of an organisational issue than a technological one and requires a top-down approach in order to develop a common strategy for an organisation’s customer data management. Proper central assessment and maintenance processes that can be adjusted according to the entities’ needs must be in place. Responsibilities for the maintenance of customer data should be delegated to several levels of an organisation in order to better manage customer data.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Microfinance has undergone a great growth in the last years, bringing consequently the significant increase of the data of the transactions and daily operations, manual processes of cleaning, complexity in IT projects and, in comparison with the traditional bank, a less amount of resources. For this reason, the model must allow the master data to have maintenance processes that reduce manual cleaning activities and contribute to the implementation of technology projects in an agile manner. On the other hand, the research seeks to combine a basic pillar such as Master Data Management (MDM) for the analysis of information with the lean approach, already used in the industry for the operational cost and additionally an evaluation measure prior to this process obtaining the state of the capabilities in the organization. In this way, the result will be that the organization can be previously evaluated and quickly identify which points should be improved to achieve the implementation of MDM initiatives. Likewise, within the research it is concluded that the Peruvian microfinance sector is prepared for the implementation of master data management with a 'proactive' maturity level of 3.46 points.

In organisations, duplicate customer master data present a recurring problem. Duplicate records can result in errors, complication, and inefficiency since they frequently result from dissimilar systems or inadequate data integration. Since this problem is made more complicated by changing client information over time, prompt detection and correction are essential. In addition to improving data quality, eliminating duplicate information also improves business processes, boosts customer confidence, and makes it easier to make wise decisions. This master’s thesis explores machine learning’s application to the field of Master Data Management. The main objective of the project is to assess how machine learning may improve the accuracy and consistency of master data records. The project aims to support the improvement of data quality within enterprises by managing issues like duplicate customer data. One of the research topics of study is if machine learning can be used to improve the accuracy of customer data, and another is whether it can be used to investigate scientific models for customer analysis when cleaning data using machine learning. Dimension identification, appropriate algorithm selection, appropriate parameter value selection, and output analysis are the four steps in the study's process. As a ground truth for our project, we came to conclusion that 22,000 is the correct number of clusters for our clustering algorithms which represents the number of unique customers. Saying this, the best performing algorithm based on number of clusters and the silhouette score metric turned out the be KMEANS with 22,000 clusters and a silhouette score of 0.596, followed by BIRCH with 22,000 number of clusters and a silhouette score of 0.591.

Stora företag har olika avdelningar där informationen från dessa måste hanteras. Master Data Management(MDM) är ett informationshanteringssystem för att hantera information från olika källor. En MDM-implementation sker med en entitet i taget. Arbetets problemställning är att rekommendera nästa entitet att inkludera i MDM-implementationen hos Scania samt vilken leverantör som passar till implementationen. En rekommendation av entitet framställs av material från Scania och intervjuer med anställda på Scania. Rekommendationen av leverantör framställs från material från leverantörer och intervjuer med leverantörerna. Entiteten som rekommenderas är produkt som individ för att informationen i området har behov av förbättrad hantering och entiteten är nära kärnverksamheten. Orchestra Networks är leverantören som rekommenderas för att de ligger i framkant inom MDM, de är nischade mot området och är starka inom produktinformation. / Enterprises has different departments and the information from them needs management. Master Data Management(MDM) is an information handling system for handling information from different sources.  One entity at the time is implemented to MDM. The work's problem is to recommend the next entity to include in the MDM implementation at Scania as well as which provider fits the implementation. A recommendation of entity is prepared from materials provided by Scania and interviews with employees at Scania. A recommendation of provider is prepared from materials from the providers and interviews with the providers. The recommended entity is product as individual because information in the area needs improved management. Orchestra Networks is the recommended supplier because they are a leader among the MDM providers, they are specialised in the area and they are strong in the product information area.

Product Information Management (PIM) is a field that deals with the product master data management and combines into one base the experience and the principles of data integration and data quality. Product Information Management merges the specific attributes of products across all channels in the supply chain. By unification, centralization and standardization of product information into one platform, quality and timely information with added value can be achieved. The goal of the theoretical part of the thesis is to construct a picture of the PIM, to place the PIM into a broader context, to define and describe various parts of the PIM solution, to describe the main differences in characteristics between the product data and data about clients and to summarize the available information on the administration and management of knowledge bases of the PIM data quality relevant for solving practical problems. The practical part of the thesis focuses on designing the structure, the content and the method of filling the knowledge base of the Product Information Management solution in the environment of the DataFlux software tools from SAS Institute. The practical part of the thesis further incorporates the analysis of the real product data, the design of definitions and objects of the knowledge base, the creation of a reference database and the testing of the knowledge base with the help of specially designed web services.