In 2019, the Big Data market is expected to grow by 20%, and the Big Data Analytics market is set to reach $103 billion by 2023. The need for companies to deal with Big Data Technologies (BDT) results in the need to make technical choices to enable or improve their business bene ts. In this context, the main goal of this thesis is to analyse the relationship between Big Data Analytics and business bene ts, by formulating hypotheses sum- marized in a model that will be experimentally validated. More speci cally, this thesis model aims to show the progression of Big Data Analytics from a descriptive to a predictive and prescriptive approach, taking in consider- ation the importance of real-time data acquisition, the level of integration between Big Data and business processes, the size and technical skills of the companies. The thesis model and hypotheses are presented in the Re- search Hypotheses chapter and then validated through linear and logistic regression performed on a dataset taken from an online survey, administered through the network of IDC as part of the Databench EU project activities. The questionnaire has been completed by more than 700 european compa- nies. An additional goal of this thesis is also to make a comparison about Big Data Analytics between the present and future by means of a clustering operation. For representing the future perspective it was performed a desk analysis of BDT use cases in which Big Data analytics have been used to obtain business bene ts, while the Databench survey has been used for the current situation. In order to describe a BDT use case, it is necessary to identify the main characteristics of its business process and the data fea- tures of the data ows that enable the use case. The thesis was developed according to the DataBench project, whose goal is to help European organ- isations to develop Big Data Technologies and improve their performances, by understanding the relation between business bene ts and technical per- formances. The model discussed in the thesis will be used by DataBench for further analysis aimed to develop a tool able to relate technical and business performances of Big Data Technologies.
Si prevede che nel 2019 il mercato dei Big Data crescerà del 20%, mentre quello dell'analisi dei Big Data raggiungerà 103 miliardi di dollari entro il 2023. La necessità per le aziende di utilizzare le tecnologie Big Data Technologies (BDT) implica il bisogno di compiere scelte tecniche per abil- itare e migliorare i loro bene ci di business. In questo contesto, l'obiettivo principale di questa tesi è quello di analizzare la relazione tra Big Data Analytics e bene ci di business, formulando alcune ipotesi riassunte in un modello che sarà validato sperimentalmente. Più speci camente, questo modello di tesi mira a spiegare la progressione dell'analisi dei Big Data da un approccio descrittivo a un approccio predittivo e prescrittivo, tenendo conto dell'importanza dell'acquisizione dei dati in tempo reale, del livello di integrazione tra Big Data e processi di business, le dimensioni e competenze tecniche delle aziende. Il modello e le ipotesi di questa tesi sono presentati nel capitolo Research Hypotheses e validati attraverso regressioni lineari e logistiche eseguite su un data set ottenuto tramite un survey online, gestito attraverso la rete di IDC nell'ambito delle attivit a del progetto Databench dell'UE. Il questionario è stato completato da oltre 700 aziende europee. Un ulteriore obiettivo di questa tesi è anche quello di svolgere un confronto su Big Data Analytics tra presente e futuro mediante un'operazione di clustering. Per rappresentare la prospettiva futura è stata e ettuata una Desk Analysis dei casi d'uso BDT in cui l'analisi dei Big Data è stata utilizzata per ottenere bene ci di business, mentre il survey di Databench è stato utilizzato per descrivere la situazione attuale. Al ne di descrivere un caso d'uso BDT, è necessario identi care le principali caratteristiche del suo pro- cesso aziendale e le caratteristiche dei ussi di dati che consentono il caso d'uso. La tesi è stata sviluppata nel contesto del progetto DataBench, il cui obiettivo è di supportare le organizzazioni europee nello sviluppo di tecnologie Big Data e permettere un miglioramento delle loro prestazioni, evidenziando la relazione tra vantaggi aziendali e prestazioni tecniche. Il modello discusso nella tesi verrà utilizzato da DataBench per ulteriori analisi volte a sviluppare un tool in grado di relazionare le performance tecniche e di business nelle tecnologie Big Data.
A descriptive model for the relationship between technical and business KPIs of big data and analytics
Di DIO, FEDERICO
2018/2019
Abstract
In 2019, the Big Data market is expected to grow by 20%, and the Big Data Analytics market is set to reach $103 billion by 2023. The need for companies to deal with Big Data Technologies (BDT) results in the need to make technical choices to enable or improve their business bene ts. In this context, the main goal of this thesis is to analyse the relationship between Big Data Analytics and business bene ts, by formulating hypotheses sum- marized in a model that will be experimentally validated. More speci cally, this thesis model aims to show the progression of Big Data Analytics from a descriptive to a predictive and prescriptive approach, taking in consider- ation the importance of real-time data acquisition, the level of integration between Big Data and business processes, the size and technical skills of the companies. The thesis model and hypotheses are presented in the Re- search Hypotheses chapter and then validated through linear and logistic regression performed on a dataset taken from an online survey, administered through the network of IDC as part of the Databench EU project activities. The questionnaire has been completed by more than 700 european compa- nies. An additional goal of this thesis is also to make a comparison about Big Data Analytics between the present and future by means of a clustering operation. For representing the future perspective it was performed a desk analysis of BDT use cases in which Big Data analytics have been used to obtain business bene ts, while the Databench survey has been used for the current situation. In order to describe a BDT use case, it is necessary to identify the main characteristics of its business process and the data fea- tures of the data ows that enable the use case. The thesis was developed according to the DataBench project, whose goal is to help European organ- isations to develop Big Data Technologies and improve their performances, by understanding the relation between business bene ts and technical per- formances. The model discussed in the thesis will be used by DataBench for further analysis aimed to develop a tool able to relate technical and business performances of Big Data Technologies.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/149885