Keys to Success for Any Intelligent Enterprise: Digital Document Management & Automation
Digital Document Management and Automation for the Intelligent Enterprise is an essential aspect of modern-day business operations. With the increasing need to go digital, organizations face the challenge of managing and automating their document processes effectively.
Although digital documents have replaced paper, the need for paper-feeding roles such as routing, markup, revision, approval, indexing, and filing still exists.
However, removing paper has led to an increase in document management problems. The switch to digital means that organizations must deal with multiple ingestion points, file types, and repositories. The real value of going digital is realized when documents become digitally accessible, shareable, and searchable.
No one wants to be called a “paper pusher” these days. Although the term has acquired connotations of negativity and waste, the role of paper feeding has not disappeared as organizations strive to go digital. In most cases, processing digital documents still requires “paper-feeding” roles such as routing, markup, revision, approval, indexing, and filing.
Removing paper had the effect of increasing document management problems. A pile of paper turned into a terabyte-sized file. Organizations must deal with multiple ingestion points, file types, and repositories. Beyond basic capture to convert paper to digital format, real value is realized when documents become digitally accessible, shareable, and searchable.
Paper Pusher Automation
Assessing your current transactional and operational processes typically reveals the current state of manual classification within your organization. A national automotive maintenance and service company centrally processes 20 bunker boxes per month containing an average of 5,000 bills of lading and related store location documents, requiring 20 to complete document ingestion and indexing.
A data extraction process that applies optimization to find, extract, and classify important information from complex and diverse documents based on data extraction rules to recognize word pattern types and other document identification options.
Automation is now possible to validate extracted data against a master database system to verify and improve the accuracy of automatic classification, and against simple to complex business conditional rules to identify data issues. Rules are implemented to validate information.
The results of implementing Intelligent Capture at this national auto service company were dramatic. With the retrieval process automated and data extraction rules working, a single indexer can process the same number of documents per month that previously required 20 people.
