This thesis deals with the application of Business Intelligence (BI) to support the corporate performance management in ISS Europe, spol. s r. o. This company provides licences and implements original software products as well as third-party software products. First, an analysis is conducted in the given company, which then serves as basis for the implementation of the BI solution that should be interconnected with the company strategies. The main goal is the implementation of a pilot BI solution to aid the monitoring and optimisation of corporate performance. Among secondary goals are the analysis of related concepts, business strategy analysis, strategic goals and systems identification and the proposition and implementation of a pilot BI solution. In its theoretical part, this thesis focuses on the analysis of concepts related to corporate performance and BI implementations and shortly describes the company together with its business strategy. The following practical part is based on the theoretical findings. An analysis of the company is carried out using the Balanced Scorecard (BSC) methodology, the result of which is depicted in a strategic map. This methodology is then supplemented by the Activity Based Costing (ABC) analytical method, which divides expenses according to assets. The results are informational data about which expenses are linked to handling individual developmental, implementational and operational demands for particular contracts. This is followed by an original proposition and the implementation of a BI solution which includes the creation of a Data Warehouse (DWH), designing Extract Transform and Load (ETL) and Online Analytical Processing (OLAP) systems and generating sample reports. The main contribution of this thesis is in providing the company management with an analysis of company data using a multidimensional perspective which can be used as basis for prompt and correct decision-making, realistic planning and performance and product optimisation.

Research Question: Which are the decisive factors that impact the adoption of business intelligence systems (BIS) among IT SMEs in Sweden? Purpose: This paper aims to examine Swedish SMEs and understand which decisive factors have an impact on the decision makers and their adoption of BIS. This study aims to study SMEs that have already adopted BIS. Method: A deductive qualitative approach was used to answer the study’s research question. The primary data was conducted through seven semi-structured interviews with Swedish IT SMEs. Empirical Findings: The empirical findings highlight that some factors had a greater influence on the adoption of BIS than others. These were possession of the right type of data, support of top management and service provider support. Conclusion: The presented study identified that small and medium sized enterprises are prone to BIS adoption when considering three decisive factors. First, the importance of possessing data and having the right type of data was a critical need and a factor for BIS adoption. Second, the use of service provider support for SMEs seemed to contribute with important value according to the findings. Third, working proactively with change management affected the perceived usefulness of technology and led to a higher chance of small and medium sized adopting BIS. / Frågeställning: Vilka är de avgörande faktorerna som påverkar antagande av business intelligence-system bland små och medelstora (SME) IT företag i Sverige? Syfte: Den här uppsatsen syftar på att undersöka svenska små och medelstora företag (SME) och förstå vilka faktorer som påverkar beslutsfattare och deras antagande av BIS. Uppsatsen syftar till att studera SME som redan har antagit BIS. Tillvägagångssätt: Denna studie använde en deduktiv kvalitativ metod för att besvara forskningsfrågan. Den primära datan samlades in genom sju semistrukturerade intervjuer med svenska SME inom IT-branschen. Bidrag: Resultaten visar att vissa faktorer hade ett större inflytande på antagande av BIS än andra. Dessa var innehav av rätt typ av data, stöd från högsta ledningen samt support från tjänsteleverantörer. Slutsats: Den presenterade studien identifierade att svenska SME är benägna att anta BIS när man överväger tre avgörande faktorer. För det första var vikten av att ha data och ha rätt typ av data ett kritiskt behov och en faktor för BIS antagande. För det andra verkade användningen av tjänsteleverantör stöd för små och medelstora företag bidra med ett viktigt värde enligt resultaten. För det tredje, att arbeta proaktivt med förändringsledning påverkade den upplevda användbarheten av teknik och ledde till en högre chans för SME att antagande BIS.

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)