2. The method of claim 1 further comprising, based on the quality group: applying a business process of inspection and validation on the data values belonging to a same quality group; and combining the outcomes of a series of business process applications on available quality groups for generation of high quality business relevant data values.
1. In a data entry environment having a set of documents receivable as a stream of data responsive to automated data extraction, a non-transitory computer readable storage medium having instructions that when executed by a processor responsive to the instructions, perform a method for extracting a business relevant data value and evaluating a confidence value comprising: identifying a stream of data items scanned from a set of training documents, each data item in the stream corresponding to a data value; applying, in a series of transformations, a conversion of the scanned data item to the data value, the series of transformations defining a combined sequence from an input stream of data form to a final data value to convert the scanned data item to the data value, each transformation being a computation that transforms the input data form to an output form resulting in extraction of the final data value; deriving a component confidence from each of the transformations indicative of a likelihood of the transformation achieving an accurate output form; using each of the component confidences as an input to a statistical model that combines the component confidences in a weighted and non-linear manner to calculate a final confidence value corresponding to the final data value; using a sample set of input documents and control data corresponding to the sample documents as a training set to be used for a learning phase; comparing, in the learning phase, for each of the data items, the final data value to a control data value from a control data set based on the set of training documents; concluding, in the learning phase, based on the comparing, whether a match exists between the final data value and the corresponding control data item; adjusting the weights of the non-linear statistical model, in learning phase, based on the aforementioned match outcomes, so as to enable calculation of the final confidence indicative of a likelihood of the data value accurately representing the scanned data item; applying the series of transformations to a production set of documents for generation of data values and the statistical model to a production set of documents by combining the component confidence from each transformation to compute a final confidence for a data item extracted from the production set; and labeling the final data value having a confidence attribute, indicative of the final confidence, with a quality group.