 Overall, we show that simplistic approaches, such as assuming that the value of the dataheld by companies or drivers is proportional to its volumeare inaccurate, because they fail to consider the complex complementarities that may exist among different datasets. To remedy this ,  more complex notions of value-sharing from economics and game-theory, such as the Shapley value need to be used to capture the effect of mixing datasets on the accuracy of forecasting algorithms driven by them .
<clarity>  Overall, we show that simplistic approaches, such as assuming that the value of the dataheld by companies or drivers is proportional to its volumeare inaccurate, because they fail to consider the complex complementarities that may exist among different datasets. To remedy this ,  more complex notions of value-sharing from economics and game-theory, such as the Shapley value need to be used to capture the effect of mixing datasets on the accuracy of forecasting algorithms driven by them .
 Overall, we show that simplistic but popular approaches for estimating the relative value of data, such as using volume , more complex notions of value-sharing from economics and game-theory, such as the Shapley value need to be used to capture the effect of mixing datasets on the accuracy of forecasting algorithms driven by them .