A Quick Guide to Document Classification and Why AI is Changing It

Document classification in the digital world depends upon Artificial Intelligence / AI

Some might argue that society didn’t exist until the emergence of the written word. Scholars suggest the earliest form of writing appeared almost 5,500 years ago in Mesopotamia – a region now known as Iraq (source). Artifacts with early symbols transitioned to a complex system of characters, and ultimately the first written language. As languages spread, society got better at telling stories, recording activities, and sharing wisdom. Today, the number of documents with information has exploded at an exponential rate. This has created enormous challenges with how best to manage document classification – and why AI is now so important.

What is Document Classification?

In the paper world, document classification is a simple task. Get a filing cabinet, buy some folders, put labels on them, and file. Today, document classification still involves the act of labeling – but the label is a “tag” or “meta tag.” The reason for classifying documents has remained the same. How do you quickly, easily find the information that you are looking for?

Early libraries faced this challenge first, which led to the creation in 1873 of the Dewey Decimal System, a system for organizing books based on the division of all knowledge into 10 groups, with each group assigned 100 numbers.

The Dewey Decimal System transformed how books were classified.

For example, all content related to philosophy and psychology was coded with an initial numerical tag of between 200–299. This system served as a great document classification strategy. Then the digital world changed everything.

DATA in the Digital World

Today, it is estimated that Internet users generate about 2.5 quintillion bytes of data every day! Add this activity to the existing 40 zettabytes of data that exists, and it is easy to see why a different information management system was needed. This challenge was effectively solved by Google founders Larry Page and Sergey Brin, who were rewarded very well for their efforts. At the time of this writing, each has earned a staggering net worth of 100 billion dollars (source).

Given the largest repository of documents now exists on the worldwide web, it is helpful to understand how search engines classify and understand documents. This background will then help you to best plan your company’s document classification strategy. Here are three important factors that search engines utilize in helping us to find documents on the Internet.

  1. The information must be converted into digital format to be searchable. With a website, most pages are written in HTML or Hypertext Markup Language. This is a language that search engines can read, which includes not only the text, but meta tags, such as page titles, subheadings, and descriptions. Once a document is in a digital format, search phrases can then be used to identify relevant documents, based on this classification hierarchy.
  2. Given the volume of data that exists, filters must be applied to retrieve relevant results. When searching on the Internet for a restaurant to have a meal, Google will limit the results to just those establishments that are local to you. In this case, they are applying a geographic filter to deliver better, more relevant information at the top of your list.
  3. Third-party validation of relevancy helps to curate better search results. As more sites link to a specific page, these pages are deemed to have greater importance and relevance, so are treated as such when search queries are performed on the data contained on those pages.

AI and Document Classification

A business seeking to implement a best-in-class document classification strategy should not only embrace the above search engine strategies but should also take advantage of Artificial Intelligence (AI) and machine learning technologies. This way they can better leverage one of their most valuable resources – their customer and business data. Just like the value that Google co-founders created with their better search algorithm, your business can also reap big rewards, albeit not quite at the $100 billion level!

The first step is to ensure all your business data is in a language that can be readily searched. This means converting unstructured data into something searchable. All of your systems need to interoperate such that this information can be found regardless of the location. Alternatively, you can embrace a data warehouse strategy to compile all of your data and documents into a single repository to then ease searchability and data access.

The second step is to apply filters whenever possible to improve the performance of the business processes and systems that work with this data. That means grouping information with meta tags that keep the search volumes to being more manageable. For example, if you are scanning title documents such as a Deed of Trust, you need to be sure that property addresses, descriptions, and other relevant data are grouped to improve the quality of search requests.

The third step is to validate the documents and their associated data by third-party review whenever possible. Here is where Artificial Intelligence and machine learning technologies are changing the playing field. These learning algorithms can be used to validate information or spot inconsistent trends that may not be readily visible to the staff managing the program. But these technologies will only work when the right data has been made available for the advanced algorithms to be performed. This technology can also ease the digital transition of paper-based documents to digital versions with greater accuracy and speed. Over time, these insights can be leveraged to not only improve performance but also identify future process improvement.

A strong document classification program is a foundation to build a data extraction program that converts your company to becoming a data-driven business. This data can then drive AI and other technologies that can lead to higher customer satisfaction, improved efficiency, and higher financial performance.