Deriving a big data analytics framework. Approaching the project management process for big data initiatives

Theres Mitscherling

ISBN 10: 3346033546 / ISBN 13: 9783346033543

Published by GRIN Verlag Feb 2020, 2020

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Master's Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 7, Queensland University of Technology (Faculty of Science and Engineering), course: Master in Business Process Management, language: English, abstract: This thesis investigates the project management approach for big data projects for industry partner Red Rocks Company. The aim of this project is to understand best practice project management for big data initiatives and to develop a framework to help such projects to deliver the expected advantages. A brief literature review is undertaken to find out how big data projects are managed. From this, a Big Data Analytics Framework is derived which is based on CRISP-DM. The framework is validated through interviews with stakeholders from the corporate sector. For this case study, the first three phases of the Business Process Management Lifecycle are applied: process discovery, analysis and design. Key findings of the case study are that literature recommends an agile project management approach for big data initiatives. On the contrary, the majority of interviewed industry stakeholders confirmes a waterfall approach is conducted more often to deliver such projects. The developed Big Data Analytics Framework will add significant benefits to Red Rocks Company as it will help to successfully deliver big data initiatives in future. Big data is considered a key enabler for future decision making and process automation. The topic is however very new and not well understood yet. Hence 50% of big data projects are not delivering the expected benefits and are costing more than initially planned. 28 pp. Englisch.

Seller Inventory # 9783346033543

Bibliographic Details

• Title: Deriving a big data analytics framework. Approaching the project management process for big data initiatives
• Publisher: GRIN Verlag Feb 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 3346033546
• ISBN 13: 9783346033543
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 3D millimeters 210 millimeters width by 148 millimeters height by 3 millimeters depth
• Weight: 56 grams

About this title

Synopsis

Master's Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 7, Queensland University of Technology (Faculty of Science and Engineering), course: Master in Business Process Management, language: English, abstract: This thesis investigates the project management approach for big data projects for industry partner Red Rocks Company. The aim of this project is to understand best practice project management for big data initiatives and to develop a framework to help such projects to deliver the expected advantages. A brief literature review is undertaken to find out how big data projects are managed. From this, a Big Data Analytics Framework is derived which is based on CRISP-DM. The framework is validated through interviews with stakeholders from the corporate sector. For this case study, the first three phases of the Business Process Management Lifecycle are applied: process discovery, analysis and design. Key findings of the case study are that literature recommends an agile project management approach for big data initiatives. On the contrary, the majority of interviewed industry stakeholders confirmes a waterfall approach is conducted more often to deliver such projects. The developed Big Data Analytics Framework will add significant benefits to Red Rocks Company as it will help to successfully deliver big data initiatives in future. Big data is considered a key enabler for future decision making and process automation. The topic is however very new and not well understood yet. Hence 50% of big data projects are not delivering the expected benefits and are costing more than initially planned.