Challenges and Gaps in the Advanced Data Capture Process—And How You Can Fix Them
The amount of unstructured data within most organizations is growing rapidly. Organizations are seeing 20% year-over-year increases in the volume of their unstructured content. With the rise of digital transformations and digital content, files such as emails, images, word processing documents, faxes, and scans are becoming more prevalent—but no more accessible.
Most organizations don’t know how to handle all this content, meaning that valuable business data is being buried within your enterprise content management systems (ECM) —and that you may be storing content that isn’t as valuable, wasting time and resources trying to sort through it.
Plus, although data capture processes are common, they frequently exist in a vacuum outside other business processes. Information is scanned in, stored, and then… nothing, meaning that potential benefits from this data are being lost.
That’s why it’s past time to start integrating your advanced data capture into your other day-to-day business processes—and in doing so, determining just what data you have available.
The history of the data capture process
Traditionally, capture was a front-end batch process that was kept somewhat separate from other business workflows. Now, however, given the sheer volume of data that organizations are contending with, this needs to change.
Requirements for data capture solutions are also changing. Your organization moves quickly—so should your data capture. Organizations need to identify a data extraction process which works in real time, is dynamic, and can be easily integrated into both business processes and business systems.
Additionally, it needs to address not only the ingestion of information (such as scanning in paper documents), but also the digestion of electronic data (such as extracting metadata from a document to update an ECM, or transforming emails into a format that can more easily be stored).
Check out this short video with capture specialist Harvey Spencer for more details on the challenges and gaps in the capture process.
The role of high-fidelity content in capture
One of the other challenges in the capture process is the quality of the content. Ideally, your content should be readable by both machines and humans, and in a format that can be stored for generations to come, such as PDF/A rather than an image format.
It all comes down to accuracy. The goal is for all of your content—whether unstructured, semi-structured or even structured—when captured to be as clear and accurate as possible, so that you can see which content you need to store and which can be eliminated.
It’s important not to use a low-fidelity PDF conversion engine to process these documents in the initial stage, since this will have adverse effects on the accuracy of the content and could even create dirty data—making it impossible to know which of your files need to be saved in years to come.
Plus, if your content is captured poorly, Optical Character Recognition (OCR) software won’t be able to identify the characters as well. OCR is another critical part of the data capture process, since it allows your image-rich content to be transformed and become searchable… but if your content wasn’t accurate to begin with, it can become further distorted.
Having inaccurate content can also lead to your metadata rules engine not being able to identify the next step in the document process, meaning that the metadata in your documents can become corrupted or simply lost.
To learn more about the importance of high-fidelity rendering in the data capture process, check out this short video with Harvey Spencer.
Learn how Advanced Rendering can enhance capture processes, helping digest information to support document-centric business processes.