The Pros And Cons Of IT Project Fail

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Introduction
This thesis has grown and evolved from a single question: ”Why do IT projects fail”?
As part of the Maersk Line IT Business Intelligence Program, I have experienced first-hand that even with management support, sizeable investments and highly competent employees, delivering large scale IT projects is a challenge.
VP of Hitachi Consulting, Damian Smith, notes that when reviewing the last two decades of research on the topic, it seems that IT project failure is the norm rather than the exception. Most studies of IT project failure conclude that 65-80% of projects fail. Failure defined as projects that exceed their budgets, fail to meet deadlines or require material rework after launch. (Smith, 2008).
The most recent Chaos Manifesto published by the Standish Group supports the conclusion drawn by Smith.

Figure 1

Figure 2
The Maersk Line BI Program (hereafter MLBI) has, as illustrated in section xx, become part of this unfortunate tendency within the industry.
The question posed in the beginning of the paper is placed within a context and plausible explanations are gathered from key actors across the program. Further, the practices of MLBI are compared to predominant literature and industry practice in the field.

Defining an IT project
The first challenge faced when attempting to investigate above question is defining what an IT project actually is. As John Smyrk notes, the term “IT project” is a firmly established concept in business language, but its meaning varies greatly. Smyrk infers three separate meanings of the term IT project. (Smyrk, 2007)
1. A project with at least one information system or technology amongst its outputs
2. A project undertaken within an IT functional unit.
3. Any project in whic...

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... but in order to not complicate matters the team representing the business customers will be referred to as the BICC.
The team on the IT side is currently known as the Business Intelligence and Master Data Management team (BIMDM).
The key motivation behind the business case for the BI program was a wish to ensure and improve access to data, enable manipulation and analysis across Mearsk Line. Furthermore, the BI program was intended to create “one single source of truth”. This wish was a clear response to the complex and cumbersome IT landscape that Maersk Line had built over the years. Managers experienced spending significant amounts of time arguing over supposed facts because different sources provided different versions of the truth.

Below is the vision for the BI program as presented in the business case.
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History of bi in ML
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