Those paper documents and digital images contain untold volumes of valuable information; information that is essential for decision-making, recordkeeping, marketing and a variety of other functions. By digitizing these files and images, you will be empowered with access to important business information that will allow you to gain a better understanding of trends, demographics, geographic data and other important insights that will allow you to connect with customers more effectively and make data-driven decisions.
But converting paper documents and images into digital form isn’t as simple as it may seem, especially when you consider the many rules and regulations that exist surrounding personal data and other potentially-sensitive information. Not only are you tasked with organizing and managing your data in a way that is intuitive, but you also need to ensure that you are meeting regulatory requirements. The EU’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA) are two regulations that strictly govern how sensitive data is managed. This includes regulations surrounding data collection, transmission, encryption, storage and deletion. Failure to fully comply with these and other regulations can result in tremendous fines — fines that hold the potential to send a business into financial ruin. And this says nothing of the regulatory burdens that are faced by health care and insurance sectors, where HIPAA sets forth some very strict data handling guidelines.
Actually finding the data you need can be a challenge too, especially when you are dealing in large volumes of information. Perhaps you have a digital archive that lacks structure, making it essentially impossible to find a specific piece of data in an expedient, efficient manner. This is a common challenge.
Fortunately, there is a solution that allows you to digitize your documents, while simultaneously keeping them well-organized and allowing you to manage your data in a way that is compliant with GDPR, CCPA and other data management and data privacy regulations. Enter: document identification and indexing automation.
- 0.1 What is Document Identification and Indexing Automation?
- 0.2 Using Machine Learning for Document Indexing Automation
- 0.3 How Do Machine Learning Algorithms Aid the Document Indexing Process?
- 0.4 Outsourcing Your Automated Documented Indexing Project to a Trusted Partner
- 1 Reach out to our team today!
What is Document Identification and Indexing Automation?
Document indexing refers to the process of scanning a paper document using optical character recognition (OCR) technology. Once in digital form, the information from the document is linked to a specific file or tag, thereby making the data searchable. The data is then compiled into a document management system (DMS) database. This allows a user to query the database as needed, easily locating a specific bit of information and its associated document.
Document indexing automation is a very effective option for digitizing and organizing document data in a way that aligns with your unique specifications and requirements. This is useful whether you are digitizing files for posterity as part of a recordkeeping effort or require easy access to your data for a more comprehensive analysis project.
Using Machine Learning for Document Indexing Automation
Machine learning can dramatically improve the efficiency of the document indexing automation process. Yet it is a solution that is frequently overlooked due to the cost associated with machine learning technology. Speaking from a practical perspective, machine learning for document indexing automation is an expensive solution that is financially beyond reach for the small or midsize business.
But there is another option: outsourcing to a service provider that offers machine learning-powered automated document indexing. When you outsource the project to an experienced and trustworthy vendor that maintains well-developed machine learning capabilities, this technology becomes remarkably affordable. In fact, machine learning-powered document indexing is far more cost-effective, accurate and faster than what you would typically see if you were to rely upon humans to perform the project.
How Do Machine Learning Algorithms Aid the Document Indexing Process?
Automated document indexing is performed rapidly, on a real-time basis, with machine learning algorithms guiding the way. Once you have a digitized document, the algorithm is programmed to open, review, and identify specific data points; then, the program performs a range of indexing functions to organize that data in a way that is queryable. This all happens in a matter of moments — far faster than what you could achieve using manual processes.
This entire process uses two different processes. One option is to use a set of pre-defined machine learning algorithms for rules-based indexing which locates identifying data points. The other option is to use rules associated with machine learning cluster mapping to identify and sort like-images.
What’s more, machine learning technology gradually improves over time. You will see greater speed and more complex decision making, with less and less human intervention required. This is a key characteristic of machine learning that makes it a top pick for this type of work. As a result, you typically see improved returns — that includes return on investment and returns by way of the output quality.
Outsourcing Your Automated Documented Indexing Project to a Trusted Partner
If you are ready to outsource your automated document indexing project, you will want to ensure you select a reputable partner with the most modern machine learning technology. At iTech, machine learning is amongst our specialties. We deal in a wide range of next-generation technologies, including machine learning-powered OCR, data capture, machine learning-driven indexing, RPA-driven auditing and more. Contact the iTech team today to discuss your document indexing project.