 On July 16, 2007, a magnitude 6.6 earthquake occurred in Niigata. Around the epicenter, cities and villages were destroyed, and 11 people died. It was a very localized strong earthquake, but it had a huge impact on Japanese economy. All domestic car makers stopped their production line for a week. This was due to the breakdown of a factory called Riken, which produces a piston ring for engine. Riken held 50% of piston ring markets in Japan and 20% in the world. To better estimate the economic loss due to the disaster, my idea is to utilize a big data from the business transaction network. We have some unique database in Japan. The database covers financial statements for 1 million firms each year, and 4 million buy and sell transaction relations for each year. Here is an example of a business transaction network with a Riken in the center. Each node is a firm. AROH shows the direction of money flow. Within two links, we can find many car companies. When Riken disappears from the network, all the related links to Riken also disappear. As a result, the flow of production stopped, and this effect is known as a breakdown of supply chain. It caused a production loss of 130,000 cars. We analyzed the network data to find a relation among annual money flow and sales value of the nodes. We found that the distribution of flow is unequal. The amount of flow depends on the size of the seller. The larger seller gets the greater flow. Using these relations of sales and flow, we can estimate the annual transaction flow for each transaction link by using the sales value. We confirmed that the estimated flow amounts and real sales amount match very well in log scale. To quantify the relation between flow and sales, we established the money transport equation. Using this equation, we can estimate the sales value for an arbitrary node for a given network structure. So we tested the validity of our model by comparing estimated sales with real sales and found that they match very well. Now, we can say that sales by a firm can be estimated solely from the transaction network structure. This point is very different from the conventional economics. We make an application of the money transport equation. By removing the target's node from the network, we can estimate the amount of economic loss following natural disasters. So we also applied our model for the earthquake in Kumamoto in 2016. We removed the firms in evacuation area and the areas of intensity level 6. The epicenter is famous for its industrial park and actually a Sony factory with the highest production of CMOS sensor in the world is located there. Through our model, we found that the loss in sales amounted to 0.7 trillion yen per month. It took more than two days to collect the information of the local disaster situation, but following this information, it took only a few minutes to estimate the economic loss by the computer. In the online platform RISES provided by Japan's Cabinet Office, our model is used in calculating the annual transaction flow from one region to the another. It is highly recommended to use for regional policymaking. RISES is a practical example of data-driven policymaking and evidence-based policymaking tools. Now, we are developing our model to predict the future business transaction network. Finding the solution for the population decline, I hope we can discuss the way to sustain our super smart society.