Indexing. Metadata. Keywords. SharePoint. Capture. Scanners. Documents. ECM. Content Management.
What’s wrong with the list above? It’s just a jumble of related terms—there’s no context, no order, and no structure. Without organization, even meaningful concepts lose their impact. The same goes for your content management strategy. Without a clear structure, you're simply converting a paper mess into a digital mess.
At the recent AI+IM Global Summit in Atlanta I was reminded that Document Capture is still extremely challenging despite great advances in Intelligent Document Processing (IDP) capabilities over the past few years. This is also a cautionary tale for everyone considering ‘just good enough capture’ for your Artificial Intelligence and Large Language Model training because I have seen this nightmare play itself out on many occasions.
I will make a prediction now that many people creating Artificial Intelligence with Large Language Model’s will be novices to the vast difference between just good enough OCR and real, high-quality document capture IDP and they will not realize that their AI is failing, not because of bad AI, but it’s failing because of the poor-quality data input!
Therefore, in this post, we’re not going to dive into the technical nuts and bolts of document capture. Instead, we'll focus on two foundational elements that will either make or break your content management success: taxonomy and metadata.
These are not technologies—they’re philosophies.
What Is Document Capture, Really?
At its core, document capture is about extracting information from documents and making that data available for future use. This might mean instantly triggering a process (like paying an invoice) or retrieving a document weeks later (like pulling up a signed delivery receipt).
No matter the use case, successful document retrieval depends on keywords—those critical pieces of data that uniquely identify a document. If your system doesn’t support smart keywording, metadata, and structure, you're going to have a hard time finding anything when you need it.
Without a strategy, all you’ve done is digitize disorganization.
Taxonomy: The Blueprint for Organization
In biology, taxonomy is the practice of classifying organisms. In content management, it serves a similar purpose: logically categorizing documents based on attributes like department, document type, or use case.
A good taxonomy pays off in several key ways:
- Security – Documents like HR files can be segmented and protected based on category, while general documents like the office café menu can be left open.
- Search Speed – Search engines and ECM platforms typically "crawl" for new content to index. A logical taxonomy speeds this up by narrowing the crawl to relevant content areas.
- Retention Policies – Organizing documents by type and date helps you enforce automated retention and deletion schedules—especially useful for legal and financial compliance.
Here’s a simple example of a taxonomy:
├ Accounting
├ Accounts Receivable
├ Checks
├ Statements
├ Accounts Payable
├ Invoices
├ Receipts
├ Human Resources
├ Applications
├ Resumes
├ W2 Forms
Establishing this structure from the start—and revisiting it as your organization grows—can save you massive time and cost down the line.
Metadata: The Key to Relevant Search
Let’s talk about another common misconception: the belief that making documents fully searchable (like creating Searchable PDFs) is always best. While this can work in some scenarios, it can also create noise.
For instance, if an insurance company scans 100 single-page documents as Searchable PDFs and someone searches for the word "claim," they may get dozens of hits—many of them irrelevant. Why? Because every word on every page is indexed.
Now imagine instead that you only indexed relevant fields—such as claim number, policy ID, or customer name. That same search now returns a clean, targeted list of results. This is the power of meaningful metadata: it filters the noise and highlights what matters.
Even better, modern ECM systems often let you apply business rules (like data formats or required fields) directly to metadata fields—creating continuity and validation right at the point of capture.
A Real-World Analogy
Think of it like searching online. Type in “taxonomy for document capture,” and you’ll likely get thousands of general articles, some helpful, some not. Now search for “document taxonomy for insurance processing” and the results become much more specific.
That’s the difference between irrelevant and relevant search—and it all comes down to how well your documents are tagged with metadata, and nowadays ‘contextual understanding’ of documents and data using Natural Language Processing.
Final Thoughts: Structure Equals Success
Organized taxonomy + relevant metadata = efficient document capture.
Carefully planning your document capture strategy pays dividends. Take time to:
- Build a logical taxonomy.
- Define what information is critical on each document.
- Avoid over-indexing and irrelevant search results.
Yes, document capture technology has become incredibly advanced. But even the best tools require structure and planning. Don’t let the lack of strategy undo your investment in digital transformation.
What are your thoughts on taxonomy, metadata, or document classification? I'd love to hear your perspective—drop a comment below.