Document digitization: Translating physical documents into digital records
Authors: Akash Gupta, Architect; Sarveshwaran Jayaraman, Senior Data Scientist; Ritesh Thakur, Director, Machine Vision, Conversational AI
What should be digitized — and when? Most document digitization solutions focus on situations where the data is being consumed and used in the present day. While these documents may not be critical for decision-making per se, they are important for operational efficiency and the simplification of processes. While document digitization falls within an organization’s overall digital transformation, digitization comes after digitalization (which provides the base for capturing structured data). Transformation journeys can be divided into four phases – 1. Structured data capture 2. Application development for structured data 3. Unstructured data capture 4. Application development for unstructured data Many companies have already exhausted the insights they can gain from their structured data and must focus on unlocking value from unstructured data. That is where document digitization comes into play. In other words, document digitization comes later in phases two & three. Where to start There is no “one-size-fits-all” approach. Digitizing documents can be complex, depending on the document type and the information. The best solutions will rely heavily upon these factors for successful completion. A successful digitization initiative requires careful deliberation of several key considerations. The first and most important is identifying opera- tions-heavy tasks that require large amounts of data entry
This article explores the power of document digitization in modern workflows and business processes, highlighting how digitization enhances efficiency.
Comprehensive data about customers, vendors, and entities must be collected and maintained for businesses to succeed in their operations, including financial and human resources information. However, transforming this data from traditional physical documents into structured data sets requires significant manual effort. Recent advancements in machine vision and natural language processing(NLP) have produced automated solutions that reduce manual efforts in digitization. Despite these advances, several challenges remain. From a business perspective, multiple touchpoints can result in data inconsistencies and processing delays. Managing diversity — different documents that have the same purpose, for example also poses a significant hurdle. From a systems perspective, technology should be deployed judiciously to ensure that it doesn’t negatively impact a process’s efficiency and effectiveness. Before translating physical documents into digital data, careful consideration needs to be given to the who, why, what, and when of digitization.
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