While most intelligent document processing market (IDP) solutions work behind-the-scenes, measuring return on investment (ROI) is more than just factoring time and materials costs.
A project should only be pursued if it is supported by a well-reasoned business case. The outcome is the most important part of the journey.
Because IDP solutions will grow to solve new use-cases, understanding the total cost of ownership is vital for determining ROI.
Total cost of ownership comes from the following:
These costs depend on the complexity of the project.
The cost of licensing is the easiest to determine because it is based on:
The cost of development is a one-time cost that includes consulting, planning, design, testing, and deployment. Some development costs to keep in mind:
Once an IDP solution has been built, there are costs associated with maintaining it. These will vary based on subscription and deployment methods and should include the following:
Software in the intelligent document processing market excels at automating manual data entry from documents.
With an almost never-ending supply of unstructured data, you will receive increasing ROI by maintaining focus on establishing new use-cases for extracting new data sets and achieving new business outcomes.
For example, if your initial IDP deployment was for accounts payable automation, turn your focus to human resources workflows. Expand to other departments like legal, procurement, and operations; and to new market innovations by unlocking electronic data contained in PDFs, logs, reports, and other transactional data.
Three primary sources of ROI:
Just one of these may be enough to justify an IDP solution.
Increasing revenue and achieving better business outcomes is the goal of all technology. It’s important to look at exactly how intelligent document processing market software delivers promised results.
Because intelligent document processing extracts data from documents, one of the most critical capabilities is preparing documents to be “read” by software. Accuracy and quality are only as good as the software’s ability to read and understand the text.
In fact, the same image cleanup commands that are used to make documents machine readable are also used to make another cleaned-up version that’s easier for humans to read. This is great for older documents or those that have been printed, scanned, emailed, printed again, passed around the office, scanned again…you get the point.
When accuracy is below a certain confidence score, the software will automatically choose another OCR engine and apply it to the low accuracy text. This is repeated until the highest accuracy of text recognition is achieved. The software will then synthesize all results together. This is the only way to achieve 99+% OCR accuracy.
Therefore, data extraction must use machine learning to intelligently identify both the document’s type (invoice, purchase order, lease, etc.) and specific information within the document.
And you can’t have robust machine learning without built-in natural language processing (NLP). NLP should not be used as an add-on software module because it’s critical in all aspects of document classification and data extraction.
NLP is more than just a “library” of known words. With NLP, data extraction is based on things like sentiment analysis (interpreting and classifying emotions), and text tagging (part-of-speech, named entity, and features within the document).
Transparent AI shows software designers exactly how conclusions were drawn when the machine made a choice about document identification or information extraction. With the ability to “see” the training data, changes are made with predictable results. Eliminating mysteries is vital for IDP to be trusted.
For massive data extraction projects, multiple simultaneous transactions must happen within the software. This is called parallel processing. Very large projects, or projects that must happen within a short timeframe, take advantage of as many computer processors as possible to complete work quicker.
This comes in many forms. Some organizations take advantage of computer processing available in-house (like unused computer labs or servers), or in the cloud with elastically expanding resources. Taking advantage of all available hardware or compute-power is never a limitation with IDP.
No machine or human is perfect. The ultimate goal of IDP is to deliver 100% accurate data. This is achievable by providing a built-in review phase that enables humans to review extracted data before it is integrated into workflows and other line of business software applications.
Fields flagged for human review are based on things like mathematical validations that don’t add up, extraction accuracy thresholds that are below a set percentage, or failing to validate with external sources (like databases).
By serving up only questionable data for review, a single worker will validate immensely more data in a day than a team of people manually hand-keying in document information.
To meet demands of complex workflows, intelligent document processing market solutions will integrate with virtually any enterprise software application. This is important for validating information and for putting extracted information into the systems that need it. IDP will output data in the format of your choice to ensure information governance is in alignment with your master data model.
Data and documents exist within many silos in every organization. With the ability to access and process (extract / redact) this data without manually moving it around, an immense amount of time is saved while achieving desired business outcomes. IDP processes data where it rests, whether in the cloud, or in local storage.
For IDP to deliver on promised ROI, it must provide the capability to build, test, and deploy in a single, seamless interface that provides fast time to data migration. Additionally, deployment must be scalable to new use-cases within your organization.
ROI is difficult to justify for data extraction technology that only works on one document type, or must be “cobbled together” to attempt a fit with another document type or workflow.
IDP is state-of-the-art technology and scalable to any document-based workflow and any document type. There are no technical limitations to the quantity of documents or the number of fields extracted. IDP will scale up to the daily extraction of billions of data fields.
Software in the intelligent document processing market should provide a proven return on investment for automating critical workflows and data extraction or it should not be used.