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Article originally posted on Data Science Central. Visit Data Science Central
This article was written by Bob Hayes
A recent survey of over 16,000 data professionals showed that the most common challenges to data science included dirty data (36%), lack of data science talent (30%) and lack of management support (27%). Also, data professionals reported experiencing around three challenges in the previous year. A principal component analysis of the 20 challenges studied showed that challenges can be grouped into five categories.
Data science is about finding useful insights and putting them to use. Data science, however, doesn’t occur in a vacuum. When pursuing their analytics goals, data professionals can be confronted by different types of challenges that hinder their progress. This post examines what types of challenges experienced by data professionals. To study this problem, I used data from the Kaggle 2017 State of Data Science and Machine Learning survey of over 16,000 data professionals (survey data collected in August 2017).
Barriers and Challenges at Work
The survey asked respondents, “At work, which barriers or challenges have you faced this past year? (Select all that apply).” Results appear in Figure 1 and show that the top 10 challenges were:
- 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%)
Results revealed that, on average, data professionals reported experiencing three (median) challenges in the previous year. The number of challenges experienced varied significantly across job title. Data professionals who self-identified as a Data Scientist or Predictive Modeler reported using four platforms. Data pros who self-identified as a Programmer reported only one challenge.
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