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Data Discovery Tools Comparison
Kopecký, Martin ; Novotný, Ota (advisor) ; Maryška, Miloš (referee)
Diploma thesis focuses on Data Discovery tools, which have been growing in im-portance in the Business Intelligence (BI) field during the last few years. Increasing number of companies of all sizes tend to include them in their BI environments. The main goal of this thesis is to compare QlikView, Tableau and PowerPivot using a defined set of criteria. The comparison is based on development of human resources report, which was modeled on a real life banking sector business case. The main goal is supported by a number of minor goals, namely: analysis of existing comparisons, definition of a new set of criteria, basic description of the compared platforms, and documentation of the case study. The text can be divided into two major parts. The theoretical part describes elemental BI architecture, discusses In-memory databases and data visualisation in context of a BI solution, and analyses existing comparisons of Data Discovery tools and BI platforms in general. Eight different comparisons are analysed in total, including reports of consulting companies and diploma theses. The applied part of the thesis builds upon the previous analysis and defines comparison criteria divided into five groups: Data import, transformation and storage; Data analysis and presentation; Operations criteria; User friendliness and support; Business criteria. The subsequent chapter describes the selected platforms, their brief history, component architecture, available editions and licensing. Case study chapter documents development of the report in each of the platforms and pinpoints their pros and cons. The final chapter applies the defined set of criteria and uses it to compare the selected Data Discovery platforms to fulfil the main goal of this thesis. The results are presented both numerically, utilising the weighted sum model, and verbally. The contribution of the thesis lies in the transparent confrontation of three Data Discovery tools, in the definition of a new set of comparison criteria, and in the documentation of the practical testing. The thesis offers an indirect answer to the question: "Which analytical tool should we use to supplement our existing BI solution?"
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Big data - application in banking
Uřídil, Martin ; Slánský, David (advisor) ; Pour, Jan (referee)
There is a growing volume of global data, which is offering new possibilities for those market participants, who know to take advantage of it. Data, information and knowledge are new highly regarded commodity especially in the banking industry. Traditional data analytics is intended for processing data with known structure and meaning. But how can we get knowledge from data with no such structure? The thesis focuses on Big Data analytics and its use in banking and financial industry. Definition of specific applications in this area and description of benefits for international and Czech banking institutions are the main goals of the thesis. The thesis is divided in four parts. The first part defines Big Data trend, the second part specifies activities and tools in banking. The purpose of the third part is to apply Big Data analytics on those activities and shows its possible benefits. The last part focuses on the particularities of Czech banking and shows what actual situation about Big Data in Czech banks is. The thesis gives complex description of possibilities of using Big Data analytics. I see my personal contribution in detailed characterization of the application in real banking activities.
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