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

Table of Contents

A. Introduction

Background

Industry partner

Project objective

Significance

B. Research methodology

Business Process Management Lifecycle

Method overview

Literature review

Phase 1 - Extraction of literature

Phase 2 – Organization and preparation for analysis of artefacts

Phase 3 – Coding and analysis

Phase 4 – Write up and presentation

Interviews

Project management approach for this research project

C. Results

Summary of literature review results

Waterfall approach

Agile

CRISP-DM

Hybrid agile and waterfall approach

Summary of interview findings

Big data Analytics Framework

1. Business Understanding

2. Understand and Prepare Data

3. Validate Business Understanding

4. Design Solution

5. Evaluate Solution

6. Validate Business Understanding

7. Deployment

D. Discussion

E. Conclusion

Project Objectives and Thematic Focus

The primary aim of this research is to identify best practice project management techniques for big data initiatives and to develop a structured framework that assists organizations in delivering successful big data projects. The research specifically addresses the high failure rate and unexpected costs currently associated with such projects by investigating industry-standard methodologies and validating them through corporate case study interviews.

• Analysis of project management methodologies for big data (Agile vs. Waterfall).
• Evaluation of the applicability of the CRISP-DM process in modern big data environments.
• Investigation into the effectiveness of hybrid project management approaches.
• Development of a standardized Big Data Analytics Framework.
• Validation of project management best practices through expert interviews in the corporate sector.

Excerpt from the Book

Summary of Chapters

A. Introduction: Provides background on big data, outlines the project objective, and details the significance of managing big data initiatives for the industry partner, Red Rocks Company.

B. Research methodology: Describes the application of the Business Process Management (BPM) Lifecycle, the literature review process, and the qualitative research methods used for conducting interviews.

C. Results: Presents the findings from the literature review and interviews, concluding with the development and step-by-step description of the Big Data Analytics Framework.

D. Discussion: Evaluates the research findings, correlates the project outcomes with the initial research goals, and addresses the limitations of the current study.

E. Conclusion: Synthesizes the core findings, confirming the value of the Big Data Analytics Framework and suggesting future research directions.

Keywords

Big Data, Project Management, Agile, Waterfall, CRISP-DM, Business Process Management, Data Analytics, Framework, Case Study, Innovation, Stakeholder Management, Hybrid Approach, Mining Industry, Technology Implementation, Process Automation

Frequently Asked Questions

What is the core focus of this research report?

The report investigates the project management approaches best suited for big data initiatives to address the current 50% failure rate and budget overruns common in the sector.

What are the central thematic fields covered in this document?

The core themes include the comparison of Agile and Waterfall methodologies, the role of CRISP-DM, and the practical challenges of integrating data analytics within large-scale corporate environments.

What is the primary objective of this work?

The objective is to understand best practices for managing big data projects and to develop a new, validated framework that helps organizations achieve their digital transformation goals.

Which scientific methodology was employed?

The researcher conducted a brief literature review of nine artefacts and performed five qualitative, semi-structured, face-to-face interviews with industry stakeholders.

What topics are discussed in the main part of the report?

The main section analyzes literature trends, presents interview findings, and outlines the seven-step Big Data Analytics Framework, providing justification for each phase.

Which keywords best characterize this work?

Key terms include Big Data, Project Management, Agile, Waterfall, CRISP-DM, and Data Analytics Framework.

Why is the CRISP-DM model still considered relevant in this framework?

The research concludes that while CRISP-DM is an older methodology, it provides the fundamental logical steps required for successful data analytics that many modern vendors continue to build upon.

How does the research reconcile the differences between Agile and Waterfall for big data?

The findings suggest a hybrid approach: While literature strongly recommends Agile, industry stakeholders often use Waterfall due to the nature of large assets, leading to the conclusion that a hybrid method is often more pragmatic.