Challenges from Data Science Projects
Data platform (legacy systems):
different platforms may lead to different results on the same dataset.
Data quality and data dictionaries:
In most companies, raw data is dirty (missing, inaccurate, duplicate, misleading, and non-integrated), and data dictionaries are incomplete or absent.
Data privacy and lack of data access:
In many projects, data is not available, or not available on time, due to data privacy issues. To resolve these issues, an upfront assessment of data privacy should be done at the scoping phase itself, and appropriate measures should be taken to address the issues. In some cases, a project may have to discontinue due to unresolved issues
Ethical Issues:
Many times, data science projects involve working with sensitive data such as race, gender, religion, national origin, and medical history, and we should be careful to use only data that is allowed by rules and regulations
Lack of project sponsorships:
Many companies do not focus on investing appropriately in data science projects.
Expectation management:
It is difficult to manage expectations about the impact of data science projects with management.
Focus on wrong problems:
The lack of clear direction, unclear problem statements, and unclear execution plans can cause data science projects to fail.
Big Data Market Size & Share
Big Data Market Size Revenue (Forecast)
(in billion U.S. dollars)
Big Data Market By Application (2022)
- Customer Analytics
- Supply Chain Analytics
- Marketing Analytics
- Pricing Analytics
- Spatial Analytics
- Workforce Analytics
- Risk & Credit Analytics
- Transportation Analytics
Market Share By Component Segment, in 2022
- Hardware
- Software
- Services