Model-data combinatie geeft beste inzicht in systeemgedrag

Tom Fiddaman van Ventana Systems over hoe data en System Dynamics elkaar kunnen versterken. Machine Learning lost niet alles op en juist in combinatie met System Dynamics kunnen we complexe vraagstukken aan: “The more your aspirations cross organizational silos, the more you need the engineering mindset, because you’ll have data gaps at the boundaries – variations in source, frequency, aggregation and interpretation. You can backfill those gaps with structural knowledge, so that the model-data combination yields good indirect measurements of system state. A machine learning algorithm doesn’t know about dimensional consistency, conservation of people, or accounting identities unless the data reveals such structure, but you probably do. On the other hand, when your problem is local, data is plentiful and your prior knowledge is weak, an algorithm can explore more possibilities than you can dream up in a given amount of time.” Tom, schot in de roos! Wij passen dit toe binnen het veld van asset management met Asset Dynamics.