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Top 10 Challenges to Practicing Data Science at Work

- Dirty data (36% reported)
- Lack of data science talent (30%)
- Company politics (27%)
- Lack of clear question (22%)
- Data inaccessible (22%)
- Results not used by decision makers (18%)
- Explaining data science to others (16%)
- Privacy issues (14%)
- Lack of domain expertise (14%)
- Organization small and cannot afford data science team (13%)

- Insights not Used in Decision Making: These challenges include company politics, an inability to integrate study findings into decision-making processes and lack of management support.
- Data Privacy, Veracity, Unavailability: These challenges revolved around the data itself, including how "dirty" it is, its availability as well as privacy issues.
- Limitations of tools to scale / deploy: Challenges in this category are related to the tools that are used to extract insights, deploy models as well as scaling solutions up to the full database.
- Lack of Funds: Challenges around lack of funding impact what the organization can purchase with respect to external data sources, data science talent and, perhaps, domain expertise.
- Wrong Questions Asked: Challenges are about the difficulty in maintaining expectations about the impact of data science projects and not having a clear question to answer or a clear direction to go in with the available data.