It's important to understand the differences between Structured, Semi Structured and Unstructured Data.   

Structured data is a very predictable format where there is minimal, if no variance, as to where data is located.  A good example might be an intake form at a doctor's office or customs form one has to fill out in order to enter countries.  So, data values like name, date, nationality are always in the same spot and the answers are consistently next to those items.  This type of scanning requires OCR and evaluating the key value pair for each answer, i.e., Name: John Smith.  Name will always have the name next to it.  Extraction is high positional since these items don’t move.

That can’t be said for semi structured data.  While some of the data is predictable, frequently there is some variance.  Invoices, receipts, and purchase orders typically fall in this category. The challenge that comes with these types of documents are the variances in each of the document types.  An example could be an invoice from Staples.  One could order 1 item or 100 items and the positional nature is lost because of where the end of the invoice varies from order to order.  It can get even more complex when trying to process, not just Staples invoices, but all of the other companies that are sending invoices.  One company may have Invoice Number as “Invoice #” another might have it as “Invoice No.”. This category of structured data is fertile with solutions from various document understanding companies and continues to improve enormously.  UiPath for instance, has one of the top Document Understanding products in this very competitive space.

Finally, my favorite category of the bunch and the biggest area for opportunity in 2023 and beyond, Unstructured Data.  There is essentially no predictability as to where data is located on one document to the next.  Contracts, legal documents, doctors' orders, handwritten documents and insurance claims all fall in this category.  Since nothing is positional, the strategy is to create commonality on the type of data you are trying to extract, or else trigger phrases are the way to train these types of models.  Since OCR engines, at this point, are very sophisticated and can read almost anything, the challenge is to find a solution that can perform this type of task.  Indico Data is the top vendor in this space at the moment.

It's my thought that the unstructured data space is where most of the 95% of corporate information exists and where the long-term value add is.  The first step will be to capture this data with close to 100% reliability and then store the data and create machine learning models out of them which can help with AI decisioning.  Beyond that, having sufficient search capabilities can only provide companies with more readily available and useful data.

-Peter Camp, CTO & Founder

Solution:

CampTek Software was able to access and open 10 million files and convert 4 million files and 100,000 encounters from CPSI to EPIC/OnBase. The whole process from migration to hosting and ongoing support is managed by CampTek Software’s experienced managed services team. The estimated manual process takes an FTE roughly a minute for each record. The automation time per file is about half of that at 30 seconds. With no human errors. If an FTE were to complete all this work, the total time working 24/7 would be 5,208 Days or 125K Hours. The FTE gained capacity is $3,125,000.

In addition, CPSI, the legacy system can now be sunset and no further ongoing costs will be incurred by the provider.