Although Business Intelligence (BI) is highly promoted and praised, organisations implementing a BI solution do not always achieve expected benefits. Instead, numerous reports of failed BI implementations and challenges prevail. Even organisations indicating they receive benefit from their BI solutions strive for improvement in BI. This highlights a need for BI to improve and for it to overcome its challenges. In response, this thesis proposes a paradigm shift for BI. It provides a literature and case study, representing an interpretive enquiry using a qualitative research approach. The case study is set within a large South African bank, extending to BI vendors providing BI solutions to the bank. Two scenarios are used to compare the views of BI providers and BI customers. In one scenario, the bank’s internal BI departments represent the BI provider view, providing BI to other departments within the bank as their BI customers. In the other scenario, the BI vendors represent the BI provider view and the BI customer view is represented by the bank’s BI departments as well as other internal bank departments – who are also the BI customers of the BI departments. The thesis starts by identifying BI’s prevailing challenges, highlighting the restrictive tendency evident within BI literature and practice whereby typical Information System (IS) challenges are raised as BI challenges. Challenges are then examined to understand their BI-specific aspects and to identify a list of BI’s prevailing challenges. The thesis then examines current measures proposed to address BI’s challenges, establishing that these are largely ineffective. Rather than attempt to resolve BI’s challenges in the same manner as previous attempts do, this thesis then analyses BI at a conceptual level to reveal a common worldview of BI held by BI practitioners and academics. It is identified that this common worldview is predominantly based on a Goods-Dominant (G-D) Logic, resulting in many of BI’s challenges. A suggestion is made to shift this worldview to a Service-Dominant (S-D) Logic. Although S-D Logic is not a new lens, it has not yet been explicitly applied to BI or a BI-related discipline at a conceptual level, offering the opportunity to examine BI from a new perspective wherein new insights to address BI’s persistent challenges emerge. / Thesis (PhD)--University of Pretoria, 2012. / Informatics / unrestricted

The aim of this study is to investigate the role Business Intelligence (BI) can play in government and more specifically at the municipality level. The study aims to investigate how data collected from a municipality Contact Center can be leveraged with the help of a BI solution. The study focuses on the Contact Center at Järfälla Kommun (municipality) and investigates whether a BI implementation can help to realize more effective planning, resource allocation and improved services and e-services. Municipality Contact Centers are becoming an increasingly popular precedent providing municipal residents with a centralized service where they can make inquiries, provide information, lodge complaints or commend actions related to activities within the specific municipality. BI can turn raw data into concrete figures and reports, map patterns and trends and support effective decision making. It can also however be costly and difficult to integrate and face resistance due to perceived complexity. This paper aims to take such notions into consideration and investigate the feasibility of implementing such a solution in the context of a municipality Contact Center. This paper identifies various benefits and drawbacks from literature which are then modeled into a SWOT framework. In addition, semi-structured interviews are utilized in this study and targeted at stakeholders knowledgeable in the Contact Center, BI, or both. Findings from the SWOT framework will be measured against the findings from the interviews and an analysis of correlations between the two sources will be investigated.

In der vorliegenden Masterarbeit werden verschiedene Graphdatenbanksysteme in einer funktionalen und technischen Evaluation hinsichtlich ihrer Eignung für ein aktuelles Forschungsvorhaben der Abteilung Datenbanken der Universität Leipzig untersucht. Ziel des Forschungsprojektes ist die Integration von Unternehmensdaten in ein Informationsnetzwerk und eine darauf aufbauendegraphenorientierte Analyse der Daten.

Applications of artificial intelligence (AI) in hospitality and accommodation have taken an enormous percentage of service-provision, helping automate most of the processes involved such as booking and purchasing, improving the guest experience, tracking of guest preferences and interests, etc. The aim of the study is to understand the roles, benefits and issues with the improvement of business intelligence (BI) in hospitality. This research is purposed to discover the applications of BI in hotel booking and accommodation. The investigation focuses on hotel guest experience, business operations and guest satisfaction. The research also shows how acquiring proper BI is supported by implementing a dynamic technology framework integrated with AI and a big data resource. In such a system, the intensive collection of customer data combined with an improved technology standard is achievable using AI. The research employs a qualitative approach for data discovery and collection. A thematic analysis helps generate proper findings that indicate an improvement in the entire hospitality service delivery system as well as customer satisfaction. In this thesis, there are examined various subsets of BI in tourism. The assessment analyzes competition arising from the application of these technologies. The study also shows the importance and application of harnessing data to gather insights about guest interests and preferences through the establishment of well-developed BI. Insights enable the customization of hotel services and products for individual guests. There is a considerable improvement in guest services and guest information collection, which is achieved through the creation of guest profiles. The research performs a discussion on the incorporation of AI and big data among other sub-components in creating diversified BI and seeks to identify the need for current BI applications in the hotel industry.

In recent years graph data models became increasingly important in both research and industry. Their core is a generic data structure of things (vertices) and connections among those things (edges). Rich graph models such as the property graph model promise an extraordinary analytical power because relationships can be evaluated without knowledge about a domain-specific database schema. This dissertation studies the usage of graph models for data integration and data mining of business data. Although a typical company's business data implicitly describes a graph it is usually stored in multiple relational databases. Therefore, we propose the first semi-automated approach to transform data from multiple relational databases into a single graph whose vertices represent domain objects and whose edges represent their mutual relationships. This transformation is the base of our conceptual framework BIIIG (Business Intelligence with Integrated Instance Graphs). We further proposed a graph-based approach to data integration. The process is executed after the transformation. In established data mining approaches interrelated input data is mostly represented by tuples of measure values and dimension values. In the context of graphs these values must be attached to the graph structure and aggregated measure values are graph attributes. Since the latter was not supported by any existing model, we proposed the use of collections of property graphs. They act as data structure of the novel Extended Property Graph Model (EPGM). The model supports vertices and edges that may appear in different graphs as well as graph properties. Further on, we proposed some operators that benefit from this data structure, for example, graph-based aggregation of measure values. A primitive operation of graph pattern mining is frequent subgraph mining (FSM). However, existing algorithms provided no support for directed multigraphs. We extended the popular gSpan algorithm to overcome this limitation. Some patterns might not be frequent while their generalizations are. Generalized graph patterns can be mined by attaching vertices to taxonomies. We proposed a novel approach to Generalized Multidimensional Frequent Subgraph Mining (GM-FSM), in particular the first solution to generalized FSM that supports not only directed multigraphs but also multiple dimensional taxonomies. In scenarios that compare patterns of different categories, e.g., fraud or not, FSM is not sufficient since pattern frequencies may differ by category. Further on, determining all pattern frequencies without frequency pruning is not an option due to the computational complexity of FSM. Thus, we developed an FSM extension to extract patterns that are characteristic for a specific category according to a user-defined interestingness function called Characteristic Subgraph Mining (CSM). Parts of this work were done in the context of GRADOOP, a framework for distributed graph analytics. To make the primitive operation of frequent subgraph mining available to this framework, we developed Distributed In-Memory gSpan (DIMSpan), a frequent subgraph miner that is tailored to the characteristics of shared-nothing clusters and distributed dataflow systems. Finally, the results of use case evaluations in cooperation with a large scale enterprise will be presented. This includes a report of practical experiences gained in implementation and application of the proposed algorithms.

Purpose – The increasing amounts of data has become an important factor for organizations. A visual dashboard is a BI tool that can be used for communication of insights from big data. One way for individuals in organizations to get insights from timely and large data sets is through visualizations displayed in visual dashboards, but studies show that most of them fall short of their potential. Therefore, the aim of this study is to examine how individuals make use of visual dashboards. Design/Methodology – To obtain this understanding a literature review was performed, followed by a study conducted in two phases. Firstly, a multiple-case study of four organizations was performed, which included interviews and the think-aloud technique. Secondly, the findings from the multiple-case study were tested through interviews with experts in the BI area. Findings – The findings indicate that low democratization, scarce effects, and simplicity are reasons for why the use of visual dashboards is not fully exploited. Low attention and understanding combined with a lack of timely information means that data-driven actions are not taken. The phase of predictive analysis has not yet been reached, rather organizations are still using the visual dashboard for descriptive analysis, which in turn hinders the possibility for effects. For these reasons the use of visual dashboards does not meet the often described purpose to make better and faster decisions, and organizations are still to take steps in that direction. Research limitations – The sampling of industries in the multiple-case study could affect variables as number of KPIs.

This thesis deals with implementation of a new framework, which allows to define and generate demo data for the star schema structure. Work analyses and com-pares available demo data generators. In theoretical part of the thesis, there are described concepts and technologies using in the field of Business Intelligence. Practical part deals with the design and implementation of the framework. In the final section, there are actual examples of generated data and their possible rep-resentation (samples of possible reports and dashboards).

Författaren vill i denna uppsats identifiera hur en mognadsmodell för att mäta Business Intelligence kan hjälpa en organisation och ett konsultföretag. Författaren vill även identifiera vad som behöver mätas och vad ett BI-system består utav. BI är ett komplext område, som företag kan ha stor affärsnytta av att använda. För att göra en bedömning på hur moget ett företag är i detta område, alltså hur väl de utnyttjar all data och statistik, finns det många olika modeller för att mäta detta. I denna forskning har en kvalitativ undersökning gjorts, där semistrukturerade intervjuer har genomförts på fyra respondenter varav två är konsulter och två är konsulternas kunder från olika företag. En utav konsulterna har alltså genomfört en analys enligt deras mognadsmodell på de två kunderna. Författarens kom fram till att ett BI-system består utav data, verktyg, människor, beslutsnivåer, förvaltningsteam och organisation. Att analysera ett BI-system i en organisation hjälper framförallt organisationen att få en tydlig och klar nulägesbild över hur det ser ut i dagsläget. Det hjälper även organisationen att vidare kunna utifrån analysens resultat sätta upp ett förändringsarbete och mål med hur organisationen vill att deras BI-system bli. En mognadsmodell hjälper ett konsultföretag med en direkt ekonomisk vinning då företaget kan ta betalt för att genomföra en analys. Ett konsultföretag kan även få fortsatt förtroende av kunden och vara med i förändringsarbetet eller bli partners med kunden. För att mäta mognad i ett BI-system handlar det framförallt om att få människors perspektiv av hur systemet upplevs och fungerar i organisationen. / In this paper, the author wants to identify how a maturity model for measuring Business Intelligence can help an organization and a consulting company. The author also wants to identify what needs to be measured and what a BI system consists of. BI is a complex area that companies can have great business benefits from using. In order to make an assessment of how mature a company is in this area, ie how well they utilize all data and statistics, there are many different models for measuring this. In this research, a qualitative study was conducted, in which four respondents were interviewed, two of whom are consultants and two are clients from different companies. One of the consultants has thus carried out an analysis according to their maturity model on the two customers. The author concluded that a BI system consists of data, tools, people, decision levels, management teams and organization. In particular, analyzing a BI system in an organization helps the organization to get a clear picture of what it looks like today. It also helps the organization to be able to set up a change work and goals based on the results of the analysis with how the organization wants their BI system to be. A maturity model assists a consulting firm with a direct financial gain as the company can charge to perform an analysis. A consulting firm can also gain continued trust from the customer and be involved in the change work or become partners with the customer. To measure maturity in a BI system, it is primarily about getting people's perspectives of how the system experienced and works in the organization

Bright is a company that provides customer and employee satisfaction surveys, and uses this information to provide feedback to their customers. Data from the surveys are stored in a relational database and information is generated both by directly querying the database as well as doing analysis on extracted data. As the amount of data grows, generating this information takes increasingly more time. Extracting the data requires significant manual work and is in practice avoided. As this is not an uncommon issue, there is a substantial theoretical framework around the area. The aim of this degree project is to explore the different methods for achieving flexible and efficient data analysis on large amounts of data. This was implemented using a multidimensional database designed for analysis as well as an OnLine Analytical Processing (OLAP) cube built using Microsoft's SQL Server Analysis Services (SSAS). The cube was designed with the possibility to extract data on an individual level through PivotTables in Excel. The implemented prototype was analyzed, showing that the prototype consistently delivers correct results severalfold as efficient as the current solution as well as making new types of analysis possible and convenient. It is concluded that the use of an OLAP cube was a good choice for the issue at hand, and that the use of SSAS provided the necessary features for a functional prototype. Finally, recommendations on possible further developments were discussed. / Bright är ett företag som tillhandahåller undersökningar för kund- och medarbetarnöjdhet, och använder den informationen för att ge återkoppling till sina kunder. Data från undersökningarna sparas i en relationsdatabas och information genereras både genom att direkt fråga databasen såväl som att göra manuell analys på extraherad data. När mängden data ökar så ökar även tiden som krävs för att generera informationen. För att extrahera data krävs en betydande mängd manuellt arbete och i praktiken undviks det. Då detta inte är ett ovanligt problem finns det ett gediget teoretiskt ramverk kring området. Målet med detta examensarbete är att utforska de olika metoderna för att uppnå flexibel och effektiv dataanalys på stora mängder data. Det implementerades genom att använda en multidimensionell databas designad för analys samt en OnLine Analytical Processing (OLAP)-kub byggd med Microsoft SQL Server Analysis Services (SSAS). Kuben designades med möjligheten att extrahera data på en individuell nivå med PivotTables i Excel. Den implementerade prototypen analyserades vilket visade att prototypen konsekvent levererar korrekta resultat flerfaldigt så effektivt som den nuvarande lösningen såväl som att göra nya typer av analys möjliga och lättanvända. Slutsatsen dras att användandet av en OLAP-kub var ett bra val för det aktuella problemet, samt att valet att använda SSAS tillhandahöll de nödvändiga funktionaliteterna för en funktionell prototyp. Slutligen diskuterades rekommendationer av möjliga framtida utvecklingar.

The recent increase in the amount of stored company data and exceeding interest in data analysis has resulted in new requirements on Data Warehousing solutions. This has led to the development of Data Warehouse Appliances, which this research project aims to investigate the business value of. The result is intended to support companies that are considering an investment, and give them an understanding of the technology’s benefits. The research project was conducted in two parts. Vendors of the Appliance technology were interviewed, as well as their customers. The results from the vendor interviews together with a literature study provided a knowledge base for the analysis of the user companies’ interviews. The results clearly indicate that there is value in the technology for larger companies. The research shows that although the main benefits advocated by the vendors match the perceived ones of the user companies, there are other aspects which they value even more. Examples of this include a reduced amount of administrative tasks and support from a single source. The research also reveals that the benefits estimated by the customer at the time of purchase were not their most valued benefits in hindsight. / Företag lagrar allt större datamängder och låter dessa ligga till grund för komplicerade dataanalyser, vilket ställer nya krav på deras befintliga Data Warehouse--‐lösningar. Detta har lett till utvecklingen av Data Warehouse Appliance, vars affärsnytta detta projekt syftar till att utreda. Resultatet kommer tillhandahålla beslutsunderlag för de företag som överväger en investering i tekniken. Undersökningen genomfördes i två steg. Intervjuer genomfördes med leverantörer som tillhandahåller tekniken såväl som med deras användande kunder. Resultaten från leverantörsintervjuerna tillsammans med en omfattande litteraturstudie låg sedan till grund för den analys som gjordes av intervjuerna med de användande företagen. Resultaten visar på ett verkligt värde i tekniken för företag med stora datamängder. Undersökningen visar att de fördelar som framhålls som teknikens främsta av leverantörerna bekräftas av deras användande kunder, men att det finns andra vinster de värdesätter ännu mer. Dessa inkluderar en minskad teknisk komplexitet, en minskad mängd administrativa uppgifter samt support från en enda källa. Undersökningen visar även att de faktorer som spelat störst roll vid investeringen inte är desamma som tillskrivs störst värde i efterhand.