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Data Quality Monitoring Methods Applied to Data Processed by Decision Support Systems
Hološková, Kristína ; Lorenc, Miroslav (advisor) ; Petráš, Miroslav (referee)
The business data could be considered as the raw material for decision-making process, for the development of corporate strategies and overall running of the business. Therefore, adequate attention should be paid to quality of the data. The main goal of the diploma thesis is elaboration of a specific framework for data quality assurance, which combines three theoretical concepts: time series analysis, data screening and data profiling -- business-specific data profiles are monitored by data screening during the data warehouse ETL (extract, transform and load) process and results are afterwards compared with the values predicted by time series analysis. Achievement of this goal is based on the analysis of "data quality" in literature, exact problem definition and selection of appropriate means for its solution. Moreover, the thesis is analysing alternative solutions available on the market and comparing their functionality with the functionality of own framework, as well.
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Client data quality management
Vacek, Martin ; Slánský, David (advisor) ; Pour, Jan (referee)
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.
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Principles of function and the possibility of BI in small and medium enterprises
Tříska, Aleš ; Pour, Jan (advisor) ; Plach, Marek (referee)
Business intelligence is an effort to better understanding of company processes and business consequences in which company occur. The main goal of this thesis is to characterize the principles and possibilities of implementation and application Business intelligence in small or medium enterprise. This goal should be achieved by description of general needs which small or medium enterprise, looking forward to use business intelligence, has. This includes evaluation and universal description of information technologies in small and medium enterprises, as well as what data are used for managerial decisions and how are they made. In order to implement BI into the small or medium enterprise, it is necessary to imply how the Business intelligence work and on what principles. The application part of this thesis describes real procedures in preparation of BI implementation, impact on company and evaluation of the meaningfulness of this solution.
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Data quality management in small and medium enterprises
Zelený, Pavel ; Pour, Jan (advisor) ; Novotný, Ota (referee)
This diploma thesis deals with the data quality management. There are many tools and methodologies to support the data quality management even in Czech market but they are all only for large companies. Small and middle companies can't afford them because of high cost. The first goal of this thesis is to summarize principles of the methodologies and then on the base of the methodologies to suggest more simple methodology for small and middle companies. In the second part of thesis is created and adapted the methodology for a specific company. The first step is to choose the data area of interest in the company. Because of impossibility to buy a software tool to clean data, there are defined relatively simple rules which are base source to create cleaning scripts in SQL language. The scripts are used for automatic data cleaning. On the base of next analyze is decided what data should be cleaned manually. In the next step are described recommendations how to remove duplicities from the database. There is used a functionality of the company's production system. The last step of the methodology is to create a control mechanism which have to keep the required data quality in future. At the end of thesis is made a data research in four data sources. All these sources are from companies using the same production system. The reason of research is to present the overview of data quality and to help with decision about cleaning data in the companies also.
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Data Quality Audit Methodology
Kotek, Aleš ; Slánský, David (advisor) ; Pour, Jan (referee)
The goal of this thesis is to summarize and to describe all available know-how and experiences of Adastra employees related to Data Quality Audit in organization. The entire thesis should serve as a guideline for sales and implementation staff within the Adastra Corp. The first part of this thesis (chapter 2 and 3) is generally concerned with Data Quality, i.e. provides various definitions of Data Quality, points out importance/relevance of Data Quality in organization and describes the most important tools and Data Quality Management Solutions. The second part (chapter 4 and 5) uses the theoretical basis of the previous chapters and form the main methodical part of this thesis. Chapter 4 is rather focused on business/sales side, defines the most important terms and used principles, and is considered as a necessary precondition for correct understanding following chapter. Chapter 5 shows detailed procedures of Data Quality Audit. Single activities are written in a standardized form to ensure clear, accurate and brief step description. The result of this thesis is the most detailed description of Data Quality Audit in Adastra Corp. including all identified services/products.
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Framework for Data quality assessment
Šíp, Libor ; Slánský, David (advisor) ; Pour, Jan (referee)
The goal of this thesis is to produce a tool that allow fast, conclusively calculate impacts of problems caused by poor data quality. Before making the tool is necessary to be familiarize with problems related to data quality like what is data quality, how to manage data quality, how to evaluate data quality and finaly how to choose right solution. First chapter is about Data Quality and shows it in different views. There is an explanation of the Data Quality meaning there, its state in companies is shown on research done by specialized organizations. The way how to solve Data Quality mananagement through the whole company is described in chapter two, which deals with Data Governance. The third chapter explains what is a Business Case and why should a Business Case be written. Otherwise shows key elements of a Business Case and provide approach how to calculate costs and benefits of the project. The fourth chapter describes potential threat which is related with decisions and how to minimize them. The fifth chapter shows examples of problems of Data Quality in an organization. There is an attempt to find evaluation of the problem and its causes. Suggestions of potential solutions towards disponsible finacial sources is made at the end. Comparison of the quality of solutions is also done.
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Visualization of Data Quality in Business Intelligence
Pohořelý, Radovan ; Pour, Jan (advisor) ; Zajíc, Ján (referee)
This thesis deals with the area of Business Intelligence and especially the part of data quality. The goal is provide an overview of data quality issue and possible ways the data can be shown to have better and more engaging informative value. Another goal was to make a proposal solution to the visualization of the system state, particularly the section of data quality, at the concrete enterprise. The output of this thesis should provide a quideline for the implementation of the proposed solution.
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Data Governance - The implementation project concept
Kmoch, Václav ; Svatá, Vlasta (advisor) ; Kalina, Jaroslav (referee)
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.
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Master Data Management, Customer Data Integration and value for business
Rais, Filip ; Novotný, Ota (advisor) ; Slánský, David (referee)
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.
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Data Quality And Tools For Its Management
Tezzelová, Jana ; Pour, Jan (advisor) ; Polášek, Marek (referee)
This diploma thesis deals with data quality, with emphasis on issues of management and on tools which were developed for solving data quality issues. The goal of this work is to summarize knowledge about data quality problems which includes its evaluation, management, description of key problems in data and possibilities of their solutions. The aims of this thesis are among others also analysis of market of software tools for support and management of data quality and mainly comparison of functionalities and possibilities of several of those tools. This work is split into two consequential parts. The first theoretical part is focusing on opening to problems of data quality and mainly data quality management, including identification of main steps for successful management. The second practical part is focusing on the market with data quality tools, especially its characteristics, segmentation, evolution, current state and expectable trends. The important section of this part is also practical comparison of features and evaluation of the work with several data quality tools. This work aims to be beneficial for all the audience interested in data quality problems, especially its management and supporting technology. Thanks to focusing on data quality tools market and tools comparison this work could be also useful guide for companies which are currently choosing the proper tool for introducing the data quality. Regarding this work focus the readers are expected to have at least basic orientation in Business Intelligence.
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