More than 85% of Data Science projects fail, according to Gartner. Every step in the Intellerts’ 8-step model has pitfalls. Avoiding them will dramatically increase the chance of project success.
Data Science projects do not often fail because of operational issues. Typically, tactical and strategic aspects dictate the success of a Data Science project. These aspects are usually outlined at the start of a project. So, it is important to scope the project correctly. What’s more, many critical success factors are not in place at the start of the project. This is often due to a lack of support from key stakeholders, uneducated business leaders or a lack of teamwork or good data science team.
If the change management side is neglected, this can also lead to project failure. Other, less common reasons are data quality issues and problems during the modeling step.
X COMMON MISTAKES
Thinking about the solution at too early a stage (have a look at our 8-step model).
Understanding of the problem is too vague and lacks detail (only high level understanding).
Problem is described with jargon where the meaning is not fully understood.
Problem is not aligned to the business strategy.
The real, deep lying problem is not defined.
Domain knowledge not properly taken in account.
Not enough support from stakeholders.