6 Essential Factors to Shape Your Document Capture Strategy

Being able to leverage crucial information to make informed business decisions is a must for organizations looking to stay efficient and competitive. In fact, according to McKinsey Global Institute, data-driven organizations are not only 23 times more likely to acquire customers, but they’re also six times as likely to retain customers and 19 times more likely to be profitable! But for many organizations, data is stuck in difficult-to-leverage formats such as paper and PDF. This is where document capture technology comes into play. By transforming images into computer-legible characters, this technology allows organizations to eliminate time-consuming manual transcription while making complete strategic use of their data. But like all business solutions, a solid strategy is needed for document capture that keeps the conditions and goals of your own business in mind.

In this blog, we’ll explore six essential factors that shape most document capture strategies. By the end, you’ll have a clearer picture of how to optimize your approach and ensure that your business’s data capture strategy aligns with your goals.

1. Control of Document Format and Layout

One of the first things to assess is who has control over your document layout and format. Are the documents created internally, or are they coming from vendors, customers, and external partners?

When you can control the layout, the use of template-based document capture can be both an accurate and cost-effective method for leveraging data. Complex elements like unusual fonts, special characters, and words embedded in logos or images can make it difficult for template-driven optical character recognition (OCR) tools to capture data accurately, making control of these factors essential for this method. Meanwhile, document types coming from multiple external sources, such as invoices, are rarely, if ever, laid out identically, greatly complicating this method with a need for multiple templates. 

In situations where these factors cannot be mitigated, other methods may prove more effective. These methods include AI-assisted capture, a relatively new OCR method that captures data highly accurately and template-free, or OCR validation tools in scenarios where AI methods may not be cost-effective or available.

2. Expected Document Capture Volume

The volume of documents being processed impacts both the speed and the accuracy of capture. High document volumes, for instance, mean that documents need to be processed more quickly to keep up with demand. Higher volumes of documents also mean that there will likely be more errors. For example, a 99.7%, a near-ideal scenario for most document capture methods, will still produce 3 errors for every 1000 characters. This can quickly add up in large document batches.

In high-volume environments, advanced capture solutions that use accuracy validation tools and workflows can help balance the need for speed with the accuracy required, but with the availability of AI-assisted capture methods, the efficiency gained from this method in a high document-volume environment will almost always yield a higher return on investment. The accuracy of AI-assisted methods can also exceed the 99.7% ideal of most methods, even in suboptimal conditions. Try this method for yourself here.

3. Time-Sensitive Need for Data

Data capture for time-sensitive documents often requires a balance between accuracy and speed. When time is a factor, there’s less opportunity for thorough quality checks, which can affect the precision of the captured data. An implementation strategy that minimizes the need for manual quality assurance may be ideal for optimizing document capture for time-sensitive processes. Both AI-assisted capture and machine learning-based capture can ensure highly accurate data at a fast pace, but the cost of these methods should be balanced against the need for accuracy. 

For instance, organizations using OCR to index documents for immediate archival may find that less emphasis on accuracy is needed as these documents can typically still be retrieved quickly through at least one index field. This brings us to the next factor on our list.

4. Required Accuracy of Captured Data

Not all data requires 100% accuracy. Depending on the purpose of the document, varying levels of accuracy might be acceptable. Documents that aren’t referenced often, for example, may not need as many quality checks since employees can simply locate and verify the document later, if necessary, through other index fields.

For documents where accuracy is crucial, such as contracts or financial records, that will leverage data in other systems, implementing a quality assurance strategy is essential. Using automated validation checks or advanced capture methods involving AI and machines can help ensure accuracy with minimal manual validation.

5. Availability of a Pure Data Source

Integrating pure data sources such as financial systems with your document capture solution can significantly improve the accuracy of your information. A pure data source provides accurate data that can be used to validate or supplement the information captured by OCR. For example, capturing one key field can allow you to reference it against the records in another system and even pull additional information, reducing the need for OCR to capture every detail.

This strategy is especially useful for data that needs to be 100% accurate, such as customer records or inventory details, as it reduces reliance on OCR alone and enhances overall data quality. For this method to be effective, capture solution providers must have experience integrating with these data sources. You can view Square 9’s most popular integrations, here.

6. Expected Frequency of Document Retrieval

As previously discussed, the frequency of document retrieval is another important factor in shaping your capture strategy. If certain documents are rarely retrieved and are not sharing data with other systems, then a lower capture accuracy may be acceptable. For these documents, the occasional error in data capture will have minimal effect on retrieval as additional index fields to search by are typically available. This method is typically best for archived documents such as those needed for audits and compliance.

For documents that require frequent retrieval, however, ensuring a higher level of capture accuracy can reduce the time spent on corrections and improve searchability. Examples of this include documents needed for customer service or daily operations. Square 9 customer, Towne Properties, for example, needed to frequently retrieve financial records for each of its 800 properties’ board meetings. The property management firm found AI-assisted capture in the form of Square 9’s TransformAI to be the most effective method.

In Summary

Creating an effective document capture strategy involves understanding the unique needs and workflows of your business. By considering these six factors, you can build a capture strategy that balances speed, accuracy, and efficiency.

Whether you’re just getting started or looking to optimize your existing capture solution, Square 9’s intelligent information management solutions are designed to meet the diverse document capture needs for a smarter, faster, and more efficient business.

How Square 9 Can Help

Square 9 is a leading provider of AI-powered intelligent information management solutions that take the paper out of work and make it easier to get things done. With digital workflows that automate many aspects of how you work today, we make it easy by extracting information from scans or PDFs, storing documents in a searchable archive, and building digital twins of your current processes through graphical workflows.