El presente proyecto de tesis tiene como objetivo analizar, diseñar y modelar la arquitectura software para el proceso de gestión de PMO. Este modelo arquitectural será utilizado como base de soporte a los procesos de gestión de llamadas y tickets de la consultora de TI “NECSIA”. La finalidad del presente proyecto es resolver la situación problemática del proceso mencionado que será parte de un análisis profundo la cual se detallará más adelante. El punto crítico de esta situación problemática es que muchas actividades de extracción, transformación y homologación de datos se realizan de manera manual, lo que impide una correcta centralización del flujo de datos en la empresa. El presente proyecto propone una solución de BI y Analytics donde se destaca el modelo arquitectural 4C que integrará las diversas fuentes de información en un repositorio unificado en Cloud. Por tal motivo, se podrá obtener una adecuada gestión y gobierno de datos, sobre todo en sus cálculos históricos de los proyectos involucrados en el proceso de Gestión de PMO. En este contexto, el documento a presentar plantea el uso del marco de trabajo Zachman para en realizar un análisis profundo del negocio con la finalidad de alinear el proceso evaluado a los objetivos estratégicos del negocio. En cuanto respecta al diseño del Modelado de los procesos de negocio se utilizó la notación BPMN. Este estándar nos permitirá mejorar la descomposición y modularización de las actividades que se involucran en los procesos. Finalmente, la presente solución de BI & Analytics busca ser parte del cambio continuo y estar alineados a los objetivos estratégicos de la empresa. / This thesis project aims to analyze, design and model the software architecture for the PMO management process. This architectural model will be used as a support base for the call and ticket management processes of the IT consultancy “NECSIA”. The purpose of this project is to solve the problematic situation of the mentioned process that will be part of an in-depth analysis which will be detailed later. The critical point of this problematic situation is that many data extraction, transformation and homologation activities are carried out manually, which prevents a correct centralization of the data flow in the company. This project proposes a BI and Analytics solution that highlights the 4C architectural model that will integrate the various sources of information in a unified repository in the Cloud. For this reason, adequate data management and governance can be obtained, especially in its historical calculations of the projects involved in the PMO Management process. In this context, the document to be presented proposes the use of the Zachman framework to carry out an in-depth analysis of the business in order to align the evaluated process with the strategic objectives of the business. Regarding the design of the Business Process Modeling, the BPMN notation was used. This standard will allow us to improve the decomposition and modularization of the activities that are involved in the processes. Finally, the present BI & Analytics solution seeks to be part of the continuous change and be aligned with the strategic objectives of the company. / Tesis

Los negocios necesitan transformar los datos que generan sus diversos procesos en información, que pueda ser comprendida por diferentes públicos, quienes necesitan conocer de la situación de la empresa desde múltiples perspectivas. Con el fin de poder consultar, analizar y así poder tomar decisiones estratégicas para la organización en el momento oportuno. Las tecnologías de información proporcionan mecanismos que permiten satisfacer la visualización de los datos. Es necesario recalcar que todo el proceso de obtener y centralizar los datos de diferentes fuentes, debe estar automatizado ya que esto permite asegurar que la información se pueda obtener de manera confiable y en el tiempo oportuno. El presente proyecto comprende el análisis y diseño de una propuesta tecnológica para la implementación de una solución de inteligencia de negocios, la cual se desarrolla para una empresa consultora de software orientada para su proceso de Gestión de Proyectos. La solución contempla el uso de tecnologías Microsoft Azure para de la arquitectura y Power BI para la herramienta de visualización de datos. Este proceso es de vital importancia para la organización debido que, no sólo comprende la etapa de desarrollo de un proyecto sino también involucra la gestión de la plataforma de la empresa llamada GeoPoint. Lo antes mencionado repercute en la rentabilidad del proyecto por lo que es necesario para la Junta Directiva pueda conocer el estado de una oportunidad de negocio y en base a ello determinar acciones que aseguren de manera positiva la rentabilidad económica y la imagen de la empresa. / Businesses need to transform the data generated by their various processes into information that can be understood by different audiences, who need to know the situation of the company from multiple perspectives. In order to consult, analyze and thus be able to make strategic decisions for the organization at the appropriate time. Information technologies provide mechanisms to satisfy the visualization of data. It should be emphasized that the entire process of obtaining and centralizing data from different sources must be automated, as this ensures that the information can be obtained reliably and in a timely manner. This project includes the analysis and design of a technological proposal for the implementation of a business intelligence solution, which is developed for a software consulting company focused on its Project Management process. The solution includes the use of Microsoft Azure technologies for the architecture and Power BI for the data visualization tool. This process is of vital importance for the organization because it not only includes the development stage of a project but also involves the management of the company's platform called GeoPoint. The abovementioned has an effect on the project profitability so it is necessary for the Board to know the status of a business opportunity and based on this determine actions that positively ensure the economic profitability and image of the company. / Tesis

As data becomes a ubiquitous part of today’s business, trust in data is recognised as a crucialfactor for organisations on the data-driven journey to stay competitive in the fast-evolvingmarketplace. To support the journey, Self-Service Business Intelligence (SSBI) has emerged asa popular approach for organisations to empower business users and gain actionable insightfrom data faster and better. Despite its importance and relevance at the organisational level,SSBI has suffered sluggish adoption rates at the user level. The purpose of this thesis is toexplore the importance of trust in data and how it influences SSBI adoption by business users.Through seven semi-structured interviews, this thesis is able to establish that: 1) trust in data isa prerequisite for SSBI adoption by business users; 2) business users trust the people behind thedata; 3) trust in SSBI tools is essential; and 4) trust in data is necessary for user adoption.Furthermore, these findings lead to a descriptive model of how trust in data influences SSBIadoption by business users as well as how business users can transition between a vicious cycleof SSBI resistance and a benign cycle of SSBI adoption.

Since the emergence of big data, traditional business intelligence systems have been unable to meet most of the information demands in many data-driven organisations. Nowadays, big data analytics is perceived to be the solution to the challenges related to information processing of big data and decision-making of most data-driven organisations. Irrespective of the promised benefits of big data, organisations find it difficult to prove and realise the value of the investment required to develop and maintain big data analytics. The reality of big data is more complex than many organisations’ perceptions of big data. Most organisations have failed to implement big data analytics successfully, and some organisations that have implemented these systems are struggling to attain the average promised value of big data. Organisations have realised that it is impractical to migrate the entire traditional business intelligence (BI) system into big data analytics and there is a need to integrate these two types of systems. Therefore, the purpose of this study was to investigate a framework for creating a hybrid data-driven decision support system that combines components from traditional business intelligence and big data analytics systems. The study employed an interpretive qualitative research methodology to investigate research participants' understanding of the concepts related to big data, a data-driven organisation, business intelligence, and other data analytics perceptions. Semi-structured interviews were held to collect research data and thematic data analysis was used to understand the research participants’ feedback information based on their background knowledge and experiences. The application of the organisational information processing theory (OIPT) and the fit viability model (FVM) guided the interpretation of the study outcomes and the development of the proposed framework. The findings of the study suggested that data-driven organisations collect data from different data sources and process these data to transform them into information with the goal of using the information as a base of all their business decisions. Executive and senior management roles in the adoption of a data-driven decision-making culture are key to the success of the organisation. BI and big data analytics are tools and software systems that are used to assist a data-driven organisation in transforming data into information and knowledge. The suggested challenges that organisations experience when they are trying to integrate BI and big data analytics were used to guide the development of the framework that can be used to create a hybrid data-driven decision support system. The framework is divided into these elements: business motivation, information requirements, supporting mechanisms, data attributes, supporting processes and hybrid data-driven decision support system architecture. The proposed framework is created to assist data-driven organisations in assessing the components of both business intelligence and big data analytics systems and make a case-by-case decision on which components can be used to satisfy the specific data requirements of an organisation. Therefore, the study contributes to enhancing the existing literature position of the attempt to integrate business intelligence and big data analytics systems. / Dissertation (MIT (Information Systems))--University of Pretoria, 2021. / Informatics / MIT (Information Systems) / Unrestricted

According to research findings, SMEs are continuously faced with unexpected changes within their operating environment. The rapid development of technology master’s new competitors, new products and markets which creates a source of uncertainty for these organisations. These changes are for instance demonstrated through changes in customer demands, lower barriers and government regulations, offering both opportunities as threats. Considering that SMEs play a significant role for society and the worldwide economy, they genuinely need to strive for innovative and efficient solutions in their business. By focusing on smarter use of information through Business Intelligence, SMEs can stay competitive in such an environment. Nevertheless, while BI utilization for efficient decision-making has been highly attractive to larger companies for some time, this has not been a reality for SMEs. The reasons for this are several and challenges vary. However, it is necessary to meet some basic conditions to effectively take advantage of BI, namely, to align BI with corporate strategies. This study applied the BISC framework on one strategic theme, the operations management, in an SME in order to identify gaps between BI and corporate strategy in their business performance management initiative. Gaps were identified by analysing current As-Is state of BI assets and the To-Be state. This thesis aims therefore to contribute in the understanding of problems and potentials regarding the process of aligning BI with corporate strategies in SMEs.

By using systems, organizations can increase the flow of information by getting the right information, in the right place and with the right person, at the right time. A system that has become more common today are Business Intelligence systems, which aims to support the decision-making process within organizations. Over the last decade, cloud systems have also become more common. Cloud systems are provided on cloud platforms over the Internet and are delivered like services. The adoption and use of such cloud services differs from the implementation and the use of traditional systems, which are implemented on premise by the customer. The system supplier has huge control over the cloud system, which leaves the customer with less control. The purpose of this study is to identify challenges that can arise when adopting and using a cloud- based Business Intelligence system, from a customer perspective. By using a case study design, we want to identify the different challenges that the customer may face when adopting and using such a system. This study is based on several literature reviews on topics such as Business Intelligence systems, cloud services, information security and user resistance etcetera. This study is also based on four qualitative interviews, from two respondents that sell the current cloud-based Business Intelligence system, as well as from two respondents who have chosen to use the system in their organizations. The study results in a conclusion that presents four overall themes that include a variety of challenges. The challenges relate to themes such as time to get started, information security requirements, needs of education and support and lastly, understanding the systems and its user interface. Several of the challenges can be specifically associated with the nature of systems and how the system is delivered, the customers’ knowledge has also been shown to be a significant aspect. Keywords: System adoption, System usage, Cloud service, Business Intelligence systems

Syftet med denna uppsats är att studera, beskriva och analysera ett företagsfinansiella beslutsfattande och hur dessa beslut kan påverkas av prediktiv analys.Finansiella beslut är beslut som är kritiska för ett företags framgång. BusinessIntelligence är ett sätt att förse organisationen med stora mängder beslutsunderlagvia tekniker så som big data och data mining för att kunna ta bättre beslut. Mångaföretag vill använda dessa beslutsunderlag för att kunna förutspå framtidabeteenden, detta för att kunna vara proaktiva i sitt beslutsfattande och på så sätteffektivisera olika delar av sin verksamhet och vara konkurrenskraftiga. Prediktivanalys är en process som utför analyser genom komplexa algoritmer som gerkvalificerade “gissningar” på sannolikheter om olika framtida händelser.För att besvara syftet så valde vi att göra en kvalitativ studie där den empiriskadatainsamlingen genomfördes via semistrukturerade intervjuer. Studiens resultatpresenterar olika effekter av hur prediktiv analys och prediktiva modeller kanpåverka en organisations finansiella beslutsfattande. Resultatet pekar mot attprediktiva analyser ökar kvaliteten på finansiella beslut. Det pekar även mot eneffektivisering av tidsaspekten för finansiellt beslutsfattande och att dessaanalyser inte kräver lika hög besluts kompetens vilket kan möjliggöra endecentralisering för finansiella beslut. Resultatet av studien visar att rätt sorts dataska användas och att denna ska ha hög kvalitet för att analyserna ska varaeffektiva och att dessa görs bäst via att koppla de prediktiva analyserna tillförslagsvis ett BI-system. / The purpose of this paper is to study, describe and analyse an organization'sfinancial decision making and how these decisions can be influenced bypredictive analytics. The financial decisions are critical for an organizationssuccess. Business Intelligence can provide organizations with huge amounts ofdecisions basis through different techniques such as big data and data mining tobe able to make better decisions. Many organizations seek to use this decisionsbasis to be able to predict future behaviours, this to be proactive in its decisionmaking and thus streamlining different parts of their business and trough this bemore competitive. Predictive analytics is a process that performs analyses throughcomplex algorithms that provide qualified “guesses” to probabilities of differentfuture events.To answer the purpose, we chose to do a qualitative study where the empiricaldata collection was conducted through semi-structured interview. The result of thestudy presents different effects of predictive analysis and predictive models mayaffect an organization's financial decision making. The results points to predictiveanalytics increasing the quality of financial decision making. It also points to anefficiency of the time-scale for financial decision making and that these analysesdo not require the same decisions skills which could allow decentralizations forfinancial decisions. The study shows that the right kind of data is to be used andof high quality for the analyses to be effective and that these are best done bylinking predictive analytics as a suggestion to a BI System

In an environment where data flows everywhere and in all forms, it can be difficult to extract something valuable of it. Business Intelligence, also known as BI, is a technology used to transform information into a valuable resource for primarily companies with a lot of information. But what opportunities does BI offer? In this essay, we use standardized techniques, popular tools and cloud services to perform a pure BI project. We will generate a report in which we will analyze whether there is any correlation between electricity prices and different types of weather data. After the practical part of the work, we will use our experience of the cloud to dig deeper into how safe the cloud reallys is. We will compare the concerns that an ordinary user has to the cloud and compare with how the cloud service provider (CSP) Azure adapts to this. / ett samhälle där information flödar i alla dess former så kan det vara svårt att utvinna någontingvärdefullt av detta. Business intelligence, även kallat BI, är en teknik som används för att kunnaomvandla informationen till en värdefull resurs för främst företag. Men vad kan man egentligengöra med BI? I denna uppsats används standardiserade tekniker, nya verktyg och molntjänster föratt utföra ett helt BI-projekt. Projektet innefattar en visuell rapport där det ska göras en grundliganalys om det finns någon korrelation mellan elpriser och olika typer av väderdata.Efter det praktiska arbetet så kommer en teoretiskt fördjupning inom molntjärnes säkerhet attutföras. Den teoretiska fördjupningen kommer att omfatta en jämförelse mellan de mestförekommande orosmoment som användare har inför molnet och hur Azure faktiskt ställer sig tilldessa.

Ett ökat vårdbehov hos våra äldre i kombination med en fortsatt personalbrist inom äldreomsorgen på många ställen i Sverige kräver en fortsatt digital utveckling inom hälso- och sjukvården, för att på ett effektivt sätt kunna möta det växande vårdbehovet. De senaste åren har intresset för Business Intelligence ökat inom just hälso- och sjukvården, och nya verktyg som kan implementeras för att effektivisera och förbättra vårdkvalitén är under ständig utveckling. Mycket tidigare forskning har bedrivits när det kommer till användandet av Business Intelligence inom hälso- och sjukvården, men där fokus legat på yrkeslegitimerad personal som sjuksköterskor och läkare, därför fokuserar den här studien på äldreomsorgen och undersköterskor då det anses vara en yrkeskategori som hamnat i skymundan i tidigare forskning. Den här studien har undersökt hur en BI dashboard kan användas för att visualisera vårdbehovet inom äldreomsorgen, för en teknisk möjlighet att spegla vårdbehovet mot antal personal.  Studien genomfördes med en kvalitativ metodansats i form av semistrukturerade intervjuer på ett avsiktligt urval, för att kunna uppnå en djupare förståelse kring respondenternas arbetssituation och deras upplevelser om vårdbehovet i dagsläget. Resultatet visar att arbetssituationen inom äldreomsorgen idag är ansträngd och där ett återkommande problemområde rör personalbrist i kombination med höga krav och små hjälpmedel. Det framgår att det finns goda grunder för implementering av verktyget, för att möjliggöra spegling av ett ständigt varierande vårdbehov mot antal personal, för att uppnå en lämplig bemanning och på så vis uppnå en högre vårdkvalité. Resultatet påvisar också vilka nyckeltal som anses relevanta när det kommer till hur stort eller litet ett vårdbehov är, i syfte att kunna använda de nyckeltalen för att möjliggöra effektiva mätningar av vårdbehov.

I denna studie undersöks hur intern Business Intelligence påverkar beslutsprocessen i stora svenska bolag. Business Intelligence används mer och mer i beslutsprocessen och anses nu vara essentiellt för organisationer som vill vara konkurrenskraftiga. Organisationer vill också bli alltmer datadrivna i sin beslutsprocess och då basera beslut på data och inte intuition. Genom en kvalitativ undersökning visar studien att BI-analytiker och beslutsfattare som använder sig av Business Intelligence är överens om att ett BI-system påverkar beslutsfattandeprocessen främst positivt. Den största nackdelen som tas upp i studien är kostanden som en implementering av ett BI-system medför, men att de positiva följderna av implementeringen väger tyngre. De främsta positiva följderna som tas upp i studien är att beslutsfattandeprocessen går fortare samt att beslutskvalitén ökar då ett BI-system tillhandahåller beslutsfattarna relevant, smart och riktad information snabbare än det tidigare var möjligt. / This study examines how internal Business Intelligence effects the decision-making process in large Swedish companies. Business Intelligence is used more and more in the decision-making process and is now considered essential for organizations if they want to be competitive. Organizations strive to become more data-driven in their decision-making and thus wants their decision-makers to primarily base their decisions on data and not intuition. Through a qualitative study, it highlights that both BI-analysts and decision-makers who use Business Intelligence agrees that a BI system affects the decision-making process mainly positively. The biggest disadvantage that is addressed in the study is the cost that the implementation of a BI system entails, but that the positive consequences of the implementation of a BI system are bigger. The main positive consequences that are addressed in the study are that the decision-making process becomes faster and that the quality of decisions increases. This is because a BI system provides the decision-makers with relevant, smart and targeted information faster than was previously possible.