Systems | Development | Analytics | API | Testing

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.

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.

As we transition into the next era of information technology, organizations face increasing pressure to deliver value to customers more rapidly. As well as higher quality, flexible access to data and applications and uncompromised security. Concurrently, they must manage growing complexity, including increasingly distributed hybrid cloud architectures and data sprawl – and accompanying costs – in order to remain competitive.

Healthcare data is set to soar, with projections showing that it will grow from 2,300 exabytes in 2020 to an impressive 10,800 exabytes by 2025. To put that in perspective, that’s like having enough data to fill over 2.5 billion DVDs! What’s more is that a large portion of this data is unstructured—scanned documents, handwritten notes, and PDFs that don’t easily integrate into traditional systems. This is where Intelligent Document Processing (IDP) comes in.

In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI. One of the most important innovations in data management is open table formats, specifically Apache Iceberg, which fundamentally transforms the way data teams manage operational metadata in the data lake.

Organizations generate vast amounts of data daily during various business operations. For example, whenever a customer checks out out at a retail outlet, data such as the customer identifier, retail outlet identifier, time of check out, list of purchased items, and the total sales value can be captured in the Point of Sales (PoS) system. Similarly, field sales staff may record possible sales opportunities in spreadsheets.