In most of the cases we are very much focused on the data we own. In case of large enterprises, we get access to different entities and sections but still the same is considered internal data. This gives you mostly a tunnel view by restricting your analysis and ability to find answers for bigger problems.
During my career I have noticed that getting access to external data can hugely impact your findings and enhance your analysis to the next level. Following are few examples of external data.
- Supply Chain Data
Most of the organizations are depending on critical supply chain for their production units. For example, a food manufacturing company is dependent on ingredients supplied by different suppliers to produce different products. If you are depending on internal data only you might spot dip in production of certain products which can only inform you about low production on certain dates. Even if you add procurement data, you can only spot low purchases resulting in slim production. However, if you get access to your partner data or productions or crops then you can find exact answer regarding reason of low production. This is also true for cases when you have access to government statistics regarding commodities. United Nations and World Bank also gather valuable data which can be used to add a different dimension to your analysis. Following links are worth consideration. Adding such data sources to your model can help you in broader analysis.
- Weather and Environment Data
Using weather and environment related data can be helpful in your reports to find certain answers which are usually not possible with internal data only. For example, if you are creating a report of your farming and related outputs then having weather data can be of great value. This is also good when you are forecasting future outputs. If you have a damaged crop this year then heavy rains or harsh weather conditions can be one of the reasons. Incorporating this data with your internal report can help you identify which crop can sustain tough weather conditions and help you in making proper contingency plan to protect your crops.
Interestingly, weather related data can also help you in finding reasons of rising accidents in certain dates. This may be due to low visibility, heavy rain or high temperature in different days and months or even timeslots. For example an unexpected heavy shower for an hour can cause long traffic jams and spike in accidents. However, without having such data you may able to know when you experienced more accidents but you will not be able to find a reason for that.
Furthermore, if a hospital is experiencing more number of asthma patients in certain months, having weather and environment data can give you exact reason for this traffic. This will help that hospital plan in better way for future to handle increased number of patients in similar weather condition.
World Bank provide Historic and Projection Data which can be of great help when doing weather related analysis.