Challenges associated with data science
It is no secret that getting value from data science projects is hard, and many projects end in failure. While some of the reasons are common to any type of project, there are some unique challenges associated with data science projects. Data science is still a relatively young and immature discipline and therefore suffers from problems that any emerging discipline encounters. Data science practitioners can learn from other mature disciplines to avoid some of the mistakes that others have learned to avoid. Let's review some of the key issues that make data science projects challenging:
- Lack of good-quality data: This is a common refrain, but this is a problem that is not likely to go away anytime soon. The key reason is that most organizations are used to collecting data for reporting. This tends to be aggregate, success-oriented information. Data needed for building models, on the other hand, needs to be detailed and should capture...
