Analysts agree that the more data you have, the better trained your models will be. So how does a data shortage factor in when determining how to create a data set for machine learning? The solution may be to look for data in unique places and pull from research and prior collection. At the recent AI World Conference & Expo, data scientist Madhu Bhattacharyya, managing director of enterprise data and analytics at global consultancy firm Protiviti talked internal data shortages, mediating bias and the importance of external data collection. What are some tips for how to create a data set for machine learning if you have limited internal data?