I'm used to applying data reconciliation in petrochemical process. Although we know the benefits of these techniques in terms of process monitoring, it has been difficult to show these benefits to managers in term of % of return.
The process of identify the gain in $ is difficult, time expensive and most of the time depends on long future measurements.
Therefore, how can we predict the profit of apply Data Reconciliation?
The idea of reconciliation of prediction on grouped time-series has been previously proposed to provide optimal forecasts based on such data. In different application areas, the prediction of values that are hierarchically related is required. Regression analysis provides a simple framework for predicting numerical target attributes from a set of independent predictive attributes. in many situations, it is important that the sum of the prediction per month is equal to the prediction for the full year. The R4R method is very useful for the prediction.
Accurate predictions are crucial to reacting quickly and better anticipating events that could have a profound impact on your company. AI algorithms use existing data to pinpoint trends, identify risks, and provide better financial information for future planning. Through the use of enhanced simultaneous optimization and solution techniques the algorithm provides a general framework within which efficient state and parameter estimation can be performed.
https://books.google.com.ng/books?id=v56WnrRsvy0C&pg=PA29&lpg=PA29&dq=How+can+we+predict+the+profit+of+Data+Reconciliation?&source=bl&ots=wTAyGOJXkS&sig=ACfU3U0LBc_ynazeY0thejHnSlPcY3esEw&hl=en&sa=X&ved=2ahUKEwiHqr-lx_TpAhUQhlwKHTZsA9IQ6AEwBXoECAkQAQ
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