Preparing for Document Automation
Several uses of data capture that represent the vast majority of documents being automated includes semi-automated data capture, template, semi-structured and combination of template and semi-structured. There are other document types that are less common but also benefit from automation, and are in some respects similar to one or more of the types mentioned.
There is no question about the value of automating the entry of paper documents. Computer processing is cheap, accurate, and stable. Human labor is expensive and slow. It is easy to see the value of automation for any organization; the trick is in preparation to automate. The difference between successful data capture projects and the unsuccessful ones very rarely has to do with the technology itself, but rather has to do with the amount of preparation done by the organization to secure success. Organizations that take the proper steps to prepare for the induction of data capture technology have a greater success rate and ROI, as well as fewer surprises.
All large projects kick off with a needs analysis. In the needs analysis phase, organizations develop their wish list of automation capabilities to apply to their documents. For most organizations automating paper, this is a request to automate one particular document type associated with a single business process. It is recommended for organizations to initially pick a discrete collection of document types that are a part of a single business process. Ideally, the document types will be of moderate organizational risk, and have a high value associated with their automation, meaning automation will provide an ROI but not change dramatically how things are done. Later, as the organization learns how to automate, it should move on to higher risk projects, yielding even higher rewards. When an organization knows what documents it wishes to automate, it can start collecting the critical facts. All of these steps happen before any vendor selection, and before any testing of technology takes place. The objective is a better understanding of the need before any investigation of technology.
The difference between successful data capture projects and the unsuccessful ones very rarely has to do with the technology itself, but rather with the amount of preparation done by the organization to secure success.