This master thesis is about Big Data Governance and about software, which is used for this purposes. Because Big Data are huge opportunity and also risk, I wanted to map products which can be easily use for Data Quality and Big Data Governance in one platform. This thesis is not only on theoretical knowledge level, but also evaluates five key products (from my point of view). I defined requirements for every kind of domain and then I set up the weights and points. The main objective is to evaluate software capabilities and compere them.

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 allows the integration of all sectors for the country's economic growth. Data duplicity, invalid data and the inability to have reliable data for decision-making are generated without a formal Governance. For this reason, Data Governance is the key to enable an autonomous, productive and reliable work environment for the use of these. Although Data Governance models already exist, in most cases they don't meet the requirements of the sector, which has its own characteristics, such as the volume exponential growth, data criticality, and regulatory frameworks to which it is exposed. The purpose of this research is to design a reference model for the microfinance organizations, supported by an evaluation tool that provides a diagnosis with the objective of implementing and improving the organization processes regarding Data Governance. This model was implemented based on the information of Peru's microfinance organizations, from which a 1.72 score was diagnosed, which is encouraging for the organization, since it shows that it has defined all its plans concerning Data Governance. Finally, after the validation, it was concluded that the model serves as a medium to identify the current status of these organizations to ensure the success of the Data Governance initiatives.

Data governance reaches across the field of information technology and is increasingly important for big data efforts, regulatory compliance, and ensuring data integrity. The purpose of this qualitative case study was to explore strategies for implementing data governance practices. This study was guided by institutional theory as the conceptual framework. The study's population consisted of informatics specialists from a small hospital, which is also a research institution in the Washington, DC, metropolitan area. This study's data collection included semi structured, in-depth individual interviews (n = 10), focus groups (n = 3), and the analysis of organizational documents (n = 19). By using methodological triangulation and by member checking with interviewees and focus group members, efforts were taken to increase the validity of this study's findings. Through thematic analysis, 5 major themes emerged from the study: structured oversight with committees and boards, effective and strategic communications, compliance with regulations, obtaining stakeholder buy-in, and benchmarking and standardization. The results of this study may benefit informatics specialists to better strategize future implementations of data governance and information management practices. By implementing effective data governance practices, organizations will be able to successfully manage and govern their data. These findings may contribute to social change by ensuring better protection of protected health information and personally identifiable information.

Companies in these days deal with underlying issue that concerns about questions how to manage volume growth of corporate data needed to decision making processes and how to control credibility and relevance of derived information and knowledge. Other questions deal with problem of responsibility and data security that represents potential risk of information outflow. The Data Governance concepts provide comprehensive answer to these questions. However, making a decision on implementing a Data Governance program is usually triggering many other problems like setting up environments, making determination of project scope, allocating capacity of data experts and finding one's way in non-uniform Data Governance concepts offered by various IT vendors. The aim of this thesis is to draw the unified and universal implementation process that helps with setting up DG projects and makes certain conception about how to run these projects step-by-step. The first and the second part of the thesis are dedicated to describe principles, components and tools of Data Governance and also methods of measuring data quality levels. The third part is offering concrete approach for successful implementation of Data Governance conception into corporate data environment.

Septima Conferencia Iberoamericana de Complejidad, Informatica y Cibernetica, CICIC 2017 - 7th Ibero-American Conference on Complexity, Informatics and Cybernetics, CICIC 2017; Orlando; United States; 21 March 2017 through 24 March 2017; Code 131437 / Data management has undergone several changes over the last few years, leaving behind the days when it was necessary to convince people about the value of data in their organizations. Over the years, the volume and expense of data management have been increasing at a high rate. Today, organizations need to have strategic management that allows them to transform data collected from various sources with clear and accurate information. So, that they can dispose of it when they need it. The motivation of the present study is to generate a model of measurement of the level of organizational maturity that allows them to ensure the success of a Data Governance initiative. In this way ensure that all the information of the organization meets the demands of the business. It is for this reason that an organizational maturity model is proposed for the success of Data Governance initiatives based on 11 categories taking into consideration the analysis of the most widespread and adopted frameworks by industry (Kalido, Dataflux, etc.) in order to know the level of maturity and the steps to be taken at each of these levels. In this way ensure the success of a Data Governance initiative. / Revisión por pares

There are series of competition battles emerging in present day while companies are recovering from last economic crisis. These battles are for customers. Take financial market for example -- it's quite saturated. Most of people do have some financial product since their birth date. Each one of us has insurance and most of us have at least standard banking account. It is imperative that insurance companies, banks and such firms have needed information about ourselves, for us to be allowed the use of these products. As the time passes we change the settings of these products, we change products themselves, buy new ones, set their portfolios, go to competition, even employees and financial advisors who take care of us do change over time. All of the above means new data (or a change, to say at least). Our every action specified leaves a digital footprint in the information systems of financial services providers who then try to process these data and use them to raise the profit using various methods. From the individual company's point of view is customer (in this case a person who has at least one product historically) unfortunately tracked multiple times due to the above changes, so this person actually seems like multiple persons instead of one. There are many reasons behind this and they are well known in common practice (Many of them are named in theoretical part). One of the main reasons for this is a fact that data quality was not a priority in past. However, this is not the case of present day and one of the success factors when it comes to spoiling client base portfolio is the level of quality of information that are tracked by companies. Several methodologies for data quality governance are being created and defined nowadays, although there is still lack of knowledge of their implementation (not just in the local Czech market). These experiences are well prized but most of internal IT departments are facing lack of knowledge and capacity dispositions. This is where great opportunity emerges for companies that use accumulated know-how from various projects that are not quite frequent in individual firms. One of such company is KPMG, Czech republic, LLC., thanks to which this work was created. So, what is the purpose and field of knowledge that is covered on the pages following? The purpose is to describe one such project concerning analysis and implementation of chosen tools and methodologies of data quality in real company. Main output is represented by a supporting framework as well as a tool that will help managers cease administration and difficulties when managing projects that concern 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

Topic of this thesis is the Data Governance implementation in the large companies. These companies struggle during governing and managing data to get useful insights for the decision making. Data Governance is new approach to managing the companies which helps to solve the data management pain points and helps organizations to work with data effectively and without any problems. Data Governance helps to transform data into asset. This thesis is divided into theoretical and practical part. In the theoretical part are discussed reasons for emerging Data Governance, analysis of approaches to Data Governance by world leading methodologies and possible focus of the Data Governance projects as well as its benefits. Important part of this theoretical part is Data Governance components definition. Implementation of the Data Governance is discussed in the practical part. The goal of the practical part is to describe required artifacts which should exist during the implementation. Described artifacts use the best practice from the existing literature. These deliverables will help to better structure, govern and successfully implement the Data Governance. Delivering these artifacts bring the value for the company. Each project deliverable has definitions of the importance for the project team and the company. Most important benefit of the practical part is aspiration to eliminate pain points during the Data Governance implementation as appropriate project team, cooperation definition, buy-in and deliverables.

Data Governance is a concept in evolution which includes people who have large responsibilities within organizations and the processes that these used to be able to manage information. This project proposes the creation of a maturity model of data governance based on the IBM Data Governance Maturity Model. The objective of this model is to help organizations to understand their level of maturity in relation to the management of your data and identify its weaknesses to subsequently take corrective action before opting for the implementation of a Data Governance program. / Data Governance o gobierno de datos es un concepto en evolución que incluye las personas que tienen grandes responsabilidades dentro de organizaciones y los procesos que estas utilizan para poder gestionar la información. El presente proyecto plantea la creación de un Modelo de Madurez de Data Governance basado en el IBM Data Governance Maturity Model. El objetivo de este modelo es ayudar a las organizaciones a conocer su nivel de madurez en relación con la gestión de sus datos e identificar sus puntos débiles para posteriormente tomar medidas correctivas antes de optar por la implementación de un programa de Data Governance. / Tesis

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In the today’s market, there is a wide range of failed IT projects in specialized small and medium-sized companies because of poor control in the gap between the business and its vision. In other words, acquired goods are not being sold, a scenario which is very common in tourism retail companies. These companies buy a number of travel packages from big companies and due to lack of demand for these packages, they expire, becoming an expense, rather than an investment. To solve this problem, we propose to detect the problems that limit a company by re-engineering the processes, enabling the implementation of a business architecture based on sentimental analysis, allowing small and medium-sized tourism enterprises (SMEs) to make better decisions and analyze the information that most possess, without knowing how to exploit it. In addition, a case study was applied using a real company, comparing data before and after using the proposed model in order to validate feasibility of the applied model. / Revisión por pares