More than 85% of Data Science projects fail, according to Gartner. Every step in data science has its own pitfalls. Avoiding them will dramatically increase the chance of project success.
X COMMON MISTAKES
Typically, tactical and strategic aspects dictate the success of a Data Science project. Often, it’s not because of operational issues. Tactical and strategic 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. This is often due to a lack of support from key stakeholders, uneducated business leaders, a lack of teamwork, or a 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.
Also, think about:
- Thinking about the solution at too early a stage (have a look at our 8-step model).
- The understanding of the problem is too vague and lacks detail (only high-level understanding).
- The problem is described with jargon where the meaning is not fully understood.
- The problem is not aligned with the business strategy.
- The real, deep-lying problem is not defined.
- Domain knowledge is not properly taken into account.
- Not enough support from stakeholders.