Handling unstructured data has become a pressing problem for organizations, especially document-centric enterprises. Data digitization using the power of AI capabilities and automation is the only way to make crucial information available on time to cater to data-driven decision-making. Enterprises, therefore, need to step beyond RPA and OCR technologies to address the data availability challenges.
Digitization of data refers to converting text, images, audio and video clips, and other forms of manual records into digital formats and stored in the cloud. This analog to digital data conversion is essential for businesses that handle bulks of paper-based and digital documents, including PDFs and emails, daily. These documents are goldmines of valuable information, which, if harnessed on time, can help with impeccable decision-making and, at times, provide an edge over the competition.
With the help of modern technologies like AI and Automation, data is not only converted into its digital version; granular insights are extracted automatically, processed, and classified into specific datasets and presented in structured, consumable formats. Moreover, since such datasets are shared in the cloud, authorized departments or persons can quickly leverage them in their operations.
Here, AI and Automation solve the unstructured data challenge effectively. For instance, OCR or Optical Character Recognition is a dominant solution to convert scanned documents into machine-readable texts. But OCR has its own set of limitations. Thankfully, other data digitization solutions such as CMR or Cognitive Machine Reading are available to enterprises today.
CMR or Cognitive Machine Reading easily overrides the various deterrents of the unstructured data digitization process and fosters a seamless extraction of granular insights even from credentials. Furthermore, unlike the rule-based OCR, this data digitization solution is created out of proprietary pattern matching using methods focused on content-based object retrieval, which renders the level of precision in unstructured data extraction.
Therefore, it is more reliable than OCR and other related tech-based solutions.
OCR is inherently inadept at handling unstructured documents like contracts where there is no template guiding the OCR in the right direction. In order for OCR to understand where to look for data in documents, the latter must be consistent and structured, following common document standards.
On the other hand, RPA goes beyond OCR with its ability to integrate applications with legacy systems to accommodate seamless process flow. But even this technology is not enough for data digitization projects. Most importantly, RPA is not designed to read data. It acts on data.
Enterprises can embrace Intelligent Document Processing from the initial point of entry, via upstream processing, and through to the desired output.
Automation, AI, and ML are the best bet for unstructured data digitization challenges. CMR incorporated with the above-mentioned tech capabilities is presently the only solution available for enterprises. This new approach matches the digital ecosystem gradually building around us. Machine Learning-based models know what data they need, where to find it, and how to process it, and they have the power to overcome the limitations of rules-based approaches to data extraction.
The timely availability of data not only empowers enterprises with data-driven decision-making, but it has also been proven that companies that can better mine customer data can aptly predict shifts in demand and meet emerging needs. But unfortunately, due to the unstructured nature of such data or the absence of proper strategies to address it, data digitization and availability continue to bother enterprises. The advent of CMR has solved many of their concerns, yet it is a new technology that tags along with a new set of challenges.