Gaps in unstructured data strategies: Takeaways from the Gartner Data and Analytics Summit 2022

Whether seeking to engage customers, investigate legal action, assess risk, uncover marketing intelligence, or manage and enforce regulations, every industry wants its data readily available for actionable insight. They must be able to identify and extract data and insight from the billions of documents stored across content repositories, file shares and email servers.

For unstructured data the status quo is insufficient

This year’s Gartner Data and Analytics Summit brought together more than 5,000 data leaders and over 150 exhibitors from all over the globe. Nearly every exhibitor at the Summit spoke to their tool’s ability to work with data, including unstructured data.

Great news — sort of.

What we learned is organizations make working with unstructured files slower and more difficult than it has to be.

In fact, the most prevalent approach to working with unstructured files shared by exhibitors and attendees was to ask customers to convert all of their unstructured files to PDFs and then run those files through optical character reading (OCR) engines. Relying on this approach is inefficient, incomplete, introduces OCR-base errors, and is certainly not fast.

Related articles

• What is Optical Character Recognition (OCR)?
• What is Case Management?
• What is Document Scanning and Indexing Software?

Imagine the time it takes a large financial institution or global commercial enterprise to convert billions of emails, spreadsheets, PowerPoint presentations, Word documents and more to PDF. Then, run them through an OCR engine. Then, the process is back to the beginning of the identification and data preparation process necessary for downstream cognitive processes.  

The fast path to unlocking unstructured data

The good news is a few innovative data and analytics firms are embedding document processing engines. This offers a better way to use unstructured content that is highly efficient, fast and 100% complete.

Document processing engines — like Hyland’s Document Filters — offer simple-to-use SDKs embedded in existing workflows that identify and extract all the text and metadata from over 600 document types. Working with the files in their native format, Document Filters extracts all the text, including embedded files and attachments, metadata and even “hidden” information like tracked changes, annotations and comments.

All the data is output to Unicode, making it available and easily consumable for any process without wasting expensive CPU cycles converting documents, only to send them to an even more expensive OCR process.

Organizations looking to evaluate contracts, claims and reports for insights can save thousands of dollars per investigation by leveraging a document processing engine. They can automate ESG compliance, and even Right to Be Forgotten claims. 

Making all unstructured data part of your strategy

Based on our experience at the 2022 Gartner Data and Analytics Summit, the current market is not well positioned to meet unstructured data needs with the level of accuracy, speed and efficiency necessary. This is a gap.

For a data and analytics solution to remain competitive, organizations must leverage technology that can process all forms of unstructured data.

Start working natively with electronic documents and gain a significant competitive advantage.

Learn more about Document Filters.