Customer knowledge plays a vital part in organisations today, particularly in sales and marketing processes, where customers can either be channel partners or final consumers. Managing customer data and/or information across business units, departments, and functions is vital. Frequently, channel partners gather and capture data about downstream customers and consumers that organisations further upstream in the channel require to be incorporated into their information systems in order to allow for management information delivery to their users. In this study, the focus is placed on manufacturing organisations using third party distribution since the flow of information between channel partner organisations in a supply chain (in contrast to the flow of products) provides an important link between organisations and increasingly represents a source of competitive advantage in the marketplace. The purpose of this study is to determine whether there is a significant difference in the use of sales and marketing data marts as management information delivery mechanisms in manufacturing organisations in different industries, particularly the pharmaceuticals and branded consumer products. The case studies presented in this dissertation indicates that there are significant differences between the use of sales and marketing data marts in different manufacturing industries, which can be ascribed to the industry, both directly and indirectly. / Thesis (MIS(Information Science))--University of Pretoria, 2002. / Information Science / MIS / unrestricted

Darbas apima banko klientų kredito istorinių duomenų dėsningumų tyrimą. Pirmiausia nagrinėjamos banko duomenų saugyklos, siekiant kuo geriau perprasti bankinius duomenis. Vėliau naudojant banko duomenų imtis, kurios apima kreditų grąžinimo istoriją, siekiama įvertinti klientų nemokumo riziką. Tai atliekama adaptuojant algoritmus bei programinę įrangą duomenų tyrimui, kuris pradedamas nuo informacijos apdorojimo ir paruošimo. Paskui pritaikant įvairius klasifikavimo algoritmus, sudarinėjami modeliai, kuriais siekiama kuo tiksliau suskirstyti turimus duomenis, nustatant nemokius klientus. Taip pat siekiant įvertinti kliento vėluojamų mokėti paskolą dienų skaičių pasitelkiami regresijos algoritmai bei sudarinėjami prognozės modeliai. Taigi darbo metu atlikus numatytus tyrimus, pateikiami duomenų vitrinų modeliai, informacijos srautų schema. Taip pat nurodomi klasifikavimo ir prognozavimo modeliai bei algoritmai, geriausiai įvertinantys duotas duomenų imtis. / This work is about analysing regularities in bank clients historical credit data. So first of all bank information repositories are analyzed to comprehend banks data. Then using data mining algorithms and software for bank data sets, which describes credit repayment history, clients insolvency risk is being tried to estimate. So first step in analyzis is information preprocessing for data mining. Later various classification algorithms is used to make models wich classify our data sets and help to identify insolvent clients as accurate as possible. Besides clasiffication, regression algorithms are analyzed and prediction models are created. These models help to estimate how long client are late to pay deposit. So when researches have been done data marts and data flow schema are presented. Also classification and regressions algorithms and models, which shows best estimation results for our data sets, are introduced.

The main goal of this thesis is to summarize and introduce general theoretical concepts of Data Warehousing by using the systems approach. The thesis defines Data Warehousing and its main areas and delimitates Data Warehousing area in terms of higher-level area called Business Intelligence. It also describes the history of Data Warehousing & Business Intelligence, focuses on key principals of Data Warehouse building and explains the practical applications of this solution. The aim of the practical part is to perform the evaluation of theoretical concepts. Based on that, design and build Data Warehouse in environment of an existing company. The final solution shall include Data Warehouse design, hardware and software platform selection, loading with real data by using ETL services and building of end users reports. The objective of the practical part is also to demonstrate the power of this technology and shall contribute to business decision-making process in this company.

The diploma thesis is focused on dashboards analysis and distribution and theirs implementation afterwards in SAP Dashboards and Web Intelligence tools. The main goal of this thesis is an analysis of dashboards for different area of company management according to chosen of architecture solution. Another goal of diploma thesis is to take into account the principles of dashboards within the company and it deals with indicator comparison as well. The author further defines data life cycle within Business Intelligence and deals with the decomposition of particular dashboard types in theoretical part. At the end of theory, it is included an important chapter from point of view data quality, data quality process and data quality improvement and an using of SAP Best Practices and KBA as well for BI tools published by SAP. The implementation of dashboards should be back up theoretical part. Implementation is divided into 3 chapters according to selected architecture, using multisource systems, SAP Infosets/Query and using Data Warehouse or Data Mart as an architecture solution for reporting purposes. The deep implementing section should be help reader to make his own opinion to different architecture, but especially difference in used BI tools within SAP Business Objects. At the end of each section regarding architecture and its solution, there are defined pros and cons.

This paper deals with data warehouses and OLAP. These technologies are defined and described here. Then an introduction of the architecture of product MS SQL Server and its tools for work with data warehouses and OLAP folow. The knowledge gained is used for creation of sample application.