In 2016, 6.8 billion dollars were invested in the Big Data market in Europe. The “European Data Market study”, conducted by the European Commission, estimated that in 2020 the Big Data market will be able to bring a + 2% increase to the European GDP. The need for organisations to deal with Big Data Technologies (BDT) results in the need to make technical choices to enable or improve their business benefits. In this context, the main goal of this thesis is to help organizations to understand the link between their business processes and the technical choices required to enable these processes. The proposed methodology will support organization technical decisions by using technical benchmarking, the usage of benchmarks will allow organizations to pursue their business goals by effectively resolving technical challenges and will contribute to investigate the relation between IT investment and ROI (Return on Investment). More specifically, this thesis aims to link technical benchmarks and business KPIs (Key Performance Indicators) by presenting a new methodology useful to extract business process characteristics and data features of the dataflows involved in the process, to guide the design of different IT infrastructure configurations by considering requirements and challenges, and to select the most suitable technical benchmark to assess the performance of the different configurations. In order to describe a BDT use case, it is necessary to identify the main characteristics of its business process and the data features of the dataflows that enable the use case. The methodology supports this process and elicits business and technical requirements useful to design a set of appropriate IT infrastructure configurations, as a further step the methodology guides the selection of the most suitable technical benchmark to assess the performance of the drafted solutions. The methodology is composed by three macro-areas: the Business Process macro-area, which collects information regarding business goals and features of the organisation, the Data Features macro-area, which concerns the technical information about the Big Data involved in the use case, and the Technical Benchmark macro-area, which regards the features of the technical benchmarks. The thesis was developed within the DataBench project, the project focuses on understanding the relation between business benefits and technical benchmarks in order to help European organisations in developing BDT to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance. In this context, the methodology will be used and, possibly, adapted for an extensive case study analysis that will provide the requirements for the design of a tool supporting the selection and use of technical benchmarks, consistently with business goals and technical requirements.
Soltanto nel 2016 sono stati investiti 6,8 miliardi di dollari nel mercato dei Big Data in Europa. Lo studio “European Data Market”, condotto dalla Commissione Europea, ha stimato che nel 2020 il mercato dei Big Data sarà in grado di aumentare del 2% il PIL europeo. Con l'introduzione delle Big Data Technologies (BDT) nasce la necessità di fare scelte tecnologiche che siano in grado di apportare benefici all'azienda anche dal punto di vista economico. In questo contesto, l'obiettivo principale di questa tesi è aiutare le organizzazioni a comprendere il legame tra i loro processi aziendali e le scelte tecniche necessarie per abilitare questi processi. La metodologia proposta supporterà le decisioni tecniche dell'organizzazione utilizzando il benchmarking tecnico, l'utilizzo di benchmark consentirà alle organizzazioni di perseguire i propri obiettivi aziendali risolvendo efficacemente le sfide tecniche e contribuirà a indagare la relazione tra investimento IT e ROI (Return on Investment). Più specificamente, questa tesi mira a collegare benchmark tecnici e KPI aziendali (Key Performance Indicators) presentando una nuova metodologia utile per estrarre le caratteristiche dei processi di business e le caratteristiche dei dati dei flussi di dati coinvolti nel processo, per guidare la progettazione di diverse configurazioni dell'infrastruttura IT considerando i requisiti e le sfide tecnologiche, e per selezionare il benchmark tecnico più adatto per valutare le prestazioni delle diverse configurazioni. Per descrivere un caso d'uso BDT, è necessario identificare le caratteristiche principali del processo aziendale e le caratteristiche dei dati dei flussi di dati che abilitano il caso d'uso. La metodologia supporta questo processo e richiama i requisiti aziendali e tecnici utili per progettare una serie di configurazioni appropriate dell'infrastruttura IT, come ulteriore passo nella metodologia che guida la selezione del parametro tecnico più adatto per valutare le prestazioni delle soluzioni elaborate. La metodologia è composta da tre macro-aree: la macro-area del processo aziendale, che raccoglie informazioni sugli obiettivi e le caratteristiche aziendali dell'organizzazione, la macro-area delle caratteristiche dei dati, che riguarda le informazioni tecniche sui Big Data coinvolti nel caso d'uso e la macro-area relativa al benchmark tecnico, che riguarda le caratteristiche dei parametri tecnici. La tesi è stata sviluppata nell'ambito del progetto DataBench, il progetto si concentra sulla comprensione della relazione tra vantaggi di business e benchmark tecnici al fine di aiutare le organizzazioni europee a sviluppare BDT per raggiungere l'eccellenza e migliorare costantemente le loro prestazioni, misurando la loro attività di sviluppo tecnologico rispetto ai parametri di alta rilevanza commerciale. In questo contesto, la metodologia sarà utilizzata e, eventualmente, adattata per un'analisi approfondita di casi di studio che fornirà i requisiti per la progettazione di un tool che supporta la selezione e l'uso di parametri tecnici, coerentemente con gli obiettivi di business e i requisiti tecnici.
A methodology for the business-oriented selection of technical benchmarking tools to support big data technology projects
POLIDORI, LUCIA
2017/2018
Abstract
In 2016, 6.8 billion dollars were invested in the Big Data market in Europe. The “European Data Market study”, conducted by the European Commission, estimated that in 2020 the Big Data market will be able to bring a + 2% increase to the European GDP. The need for organisations to deal with Big Data Technologies (BDT) results in the need to make technical choices to enable or improve their business benefits. In this context, the main goal of this thesis is to help organizations to understand the link between their business processes and the technical choices required to enable these processes. The proposed methodology will support organization technical decisions by using technical benchmarking, the usage of benchmarks will allow organizations to pursue their business goals by effectively resolving technical challenges and will contribute to investigate the relation between IT investment and ROI (Return on Investment). More specifically, this thesis aims to link technical benchmarks and business KPIs (Key Performance Indicators) by presenting a new methodology useful to extract business process characteristics and data features of the dataflows involved in the process, to guide the design of different IT infrastructure configurations by considering requirements and challenges, and to select the most suitable technical benchmark to assess the performance of the different configurations. In order to describe a BDT use case, it is necessary to identify the main characteristics of its business process and the data features of the dataflows that enable the use case. The methodology supports this process and elicits business and technical requirements useful to design a set of appropriate IT infrastructure configurations, as a further step the methodology guides the selection of the most suitable technical benchmark to assess the performance of the drafted solutions. The methodology is composed by three macro-areas: the Business Process macro-area, which collects information regarding business goals and features of the organisation, the Data Features macro-area, which concerns the technical information about the Big Data involved in the use case, and the Technical Benchmark macro-area, which regards the features of the technical benchmarks. The thesis was developed within the DataBench project, the project focuses on understanding the relation between business benefits and technical benchmarks in order to help European organisations in developing BDT to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance. In this context, the methodology will be used and, possibly, adapted for an extensive case study analysis that will provide the requirements for the design of a tool supporting the selection and use of technical benchmarks, consistently with business goals and technical requirements.| File | Dimensione | Formato | |
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2018_10_Polidori.pdf
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Descrizione: Testo della tesi
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https://hdl.handle.net/10589/142835