Big Data and scanning, do they mix?

?

The unstructured digital gap.

The data landscape is changing every day, every hour, every second. ?From a Chief Data Office perspective, enterprise big data strategies such as data meshes or data fabrics can provide a consolidated approach for managing data across diverse environments, relying heavily on the ability to efficiently discover and identify relevant data from a myriad of sources. By consolidating data from various sources, these approaches create a rich dataset that is ideal for advanced analytics, artificial intelligence and machine learning benefiting the entire organization and the ecosphere it belongs to for more effective decision making.

?Within these strategies, the rapid advancement and evolution of AI models such as Generative AI rely on vast amounts of data to learn, semantically map and understand the nuances of human language increasing the demand for accessible data. The quality, diversity, and representativeness of the collected data can directly impact the model's ability to comprehend, interpret, and generate language accurately.

Strategy aside, the consensus from leading market research reports tabs 70-80% of an organization’s mountainous data exists within inaccessible unstructured and semi structured formats. This data doesn’t exist in organized tabulated rows and columns; instead, it’s buried in emails, pdfs, notes, transcripts, documentation, handwriting, free text and images.

The inaccessible data threatens to undermine the effectiveness and quality of data within these strategies and models further complicating the value proposition and trust in any derived outcomes.

?Compounding this problem, current industry collection and digitization approaches apply a strictly rules-based approach only capturing a few fields of information. This presents challenges and limitations in mapping and adapting data formats without developing numerous custom scripts, queries, templates, and ingest synchronizations related to changes, which in turn slows down the processing of the information and associated business workflows. A rules-based approach presents limitations with data inconsistencies such as human handwriting, inconsistent formatting, or poor scanning. In addition to multi-modal information, extraction challenges are proliferated relating to the semantic and contextual relationships between data fields and nomenclatures from different systems or providers.

Evolved and effective digitization, extraction, tagging and semantic collection methods of unstructured information is absolutely critical to the development and effectiveness of Big Data architectures and advanced AI tools and needs to be addressed at the very intake of information.

Just scanning information and proliferating the core challenges of accessibility, discoverability and interoperability of all the data locked within is no longer effective and is irreconcilable with Chief Data Office and enterprise big data objectives.

DOMA Technologies is a software led company with over 22 years of experience delivering digital transformation services to Fortune 500, Commercial and Government customers nationwide. DOMA’s groundbreaking 70,000sq ft secure intake and digitization processing center in Virginia Beach, VA infused with leading edge NLP, Computer Vision and AI SaaS tools has enabled DOMA to rapidly address the unstructured challenge for any size organization.

?

?

?

?

?