Data’s value to your organization lies in its quality. Data quality becomes even more important considering how rapidly data volume is increasing. According to conservative estimates, businesses generate 2 hundred thousand terabytes of data every day. How does that affect quality? Well, large volumes of data are only valuable if they’re of good quality, i.e., usable for your organization’s analytics and BI processes.

If you’re working in the data space today, you must have felt the wave of artificial intelligence (AI) innovation reshaping how we manage and access information. One of the areas affected is data catalogs, which are no longer simple tools for organizing metadata. They’ve evolved dramatically into powerful, intelligent systems capable of understanding data on a much deeper level.

Information extraction (IE) finds its roots in the early development of natural language processing (NLP) and artificial intelligence (AI), when the focus was still on rule-based systems that relied on hand-crafted linguistic instructions to extract specific information from text. Over time, organizations shifted to techniques like deep learning and recurrent neural networks (RNN) to improve the accuracy of information extraction systems.

Business leaders find themselves involved in a range of high-priority tasks, most of which require making critical decisions. Let’s say you’re the sales head of a global organization. You’re ready to make an important decision about next quarter’s sales strategy, but you must first look at the right data set. You know it exists somewhere in your organization’s databases, yet it’s not within the arm’s reach.

The financial document processing domain has undergone a 360-degree shift in the past decade. It was at the brink of the 1980s when software providers began releasing document management systems aimed at helping companies save time, money, and effort in regard to financial document processing. What started as simple document management systems using Optical Character Recognition to digitize printed financial documents has evolved into advanced, AI-powered solutions.

What if your document processing system could do more than categorize documents and extract data, no matter the format? That’s exactly what you can do with intelligent document processing (IDP) software. IDP tools adapt to varying structures and formats and understand content to summarize lengthy documents, identify anomalies, and flag errors. The best part? IDP software continuously improves in accuracy the more you use it.

The GenAI revolution is well and truly here. To take inspiration from our favorite comfort show, Gilmore Girls, “It’s GenAI’s world, and we’re just living in it.” In fact, McKinsey reports the number of organizations regularly using GenAI has doubled in ten months between their 2023 and 2024 surveys.

Your docs are a lot like your family—not in the corporate jargony “we are a family” way, but more in the “can’t live with them, can’t live without them” way. Yes, these docs are crucial in more ways than one, but teams that regularly work with them know that the time they spend searching for, cleansing, and prepping their docs can be better utilized elsewhere.

The computer revolution in law took flight in the 1970s with the release of the iconic red “UBIQ” terminal. This innovation completely changed how legal document management was performed. It empowered lawyers to easily browse case law online rather than looking through towering racks of yellowed paper. As the years passed, a wave of new document management solutions emerged.

Let’s be honest – AI can seem like a bit of a mystery, and with this mystery comes myths and misconceptions. Is it actually that good? Can it handle varying document structures? Can it integrate with my existing systems? Because of this mystery, many companies have yet to take the leap and incorporate AI into their data processes. Today, we’re going to play MythBusters, separate fact from fiction, and show how you can use AI document processing to maximize efficiency and save costs.