Data extraction is the process of lifting unstructured data from your documents so that you can effectively integrate, analyze, and apply it.
What makes DOMA different is that we offer more than a single targeted tool. We integrate multiple types of data extraction tools to create holistic solutions that can address larger challenges within your business. When combined with our business process outsourcing, the result is high impact with minimal disruption.
We assist federal agencies, education institutions, healthcare organizations, and commercial businesses to embrace cloud based automation tools and innovative new processes. We save you time and money by compiling industry-leading tools and pairing them with the expertise and labor required to build and deploy them. We can provide start to finish solutions to extract, index, and deploy your data.
AWS Textract is a service that automatically extracts text and data from scanned documents. This part of the data extraction process uses machine learning to instantly “read” virtually any type of document to accurately extract text and data without the need for any manual effort or custom code.
AWS Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in a text.
AWS Rekognition is always learning from new data; AWS is continually adding new labels and facial recognition features to the service we provide.
Data analysis can open up a host of new opportunities for your business. Once we have transformed your unstructured data into structured data there are limitless options for further processing. Aside from the benefits of improved compliance, visibility, and accuracy, data extraction has many unique use cases. Every industry can benefit from the increased productivity and automation this service offers.
Data Extraction Use Cases:
Data is one of the most important decision-making tools in business. Making data-based decisions can result in higher efficiency and increased profits but it’s not just about money.
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