Unlocking the Power of Unstructured Data with Base64 and MongoDB Atlas

 

 

 

 

January 21, 2025

Intelligent Document Processing (IDP) is revolutionizing how businesses manage their documents. Base64, a leader in the IDP space, uses AI to extract and structure data from a wide range of documents. This structured data can then be seamlessly stored in MongoDB Atlas, a multi-cloud developer data platform optimized for managing unstructured data.

 


Why Atlas for Unstructured Data?

Atlas is a powerful multi-cloud developer data platform that gives you the versatility you need to build a wide variety of applications. Power transactional processing, relevance-based search, in-app analytics, and more through an integrated and elegant data architecture. Atlas provides a first-class developer experience through a flexible document data model and unified query interface, while meeting the most demanding requirements for resilience, scalability, and data privacy.

Atlas’s flexible document data model allows data to be stored in JSON format, preserving the hierarchical relationships often found in documents. Its automated index and schema design suggestions simplify querying and analysis, and Atlas Charts enable rich data visualization. With compliance features and high performance, Atlas is a top choice for organizations seeking to unlock the value of their unstructured data.


Base64: Bridging the Gap Between Unstructured Content and Structured Data

Not all unstructured content is created equal. While spreadsheets, text documents, and other data formats lack a predefined structure. Handling these types of unstructured data is as simple as storing the content to Atlas. However, these types are still inherently different from the binary unstructured data found within documents and images. 

This distinction lies in the complexity of extracting and interpreting meaningful information from binary data, which often requires advanced techniques like optical character recognition (OCR) and intelligent document processing (IDP). Base64 specializes in this latter category, leveraging AI-powered models to unlock valuable insights from the unstructured content embedded within documents and images. By focusing on this specific type of unstructured data, Base64 addresses a unique challenge and provides tailored solutions for extracting, structuring, and analyzing information that would otherwise remain inaccessible. 

Base64’s AI-powered document processing capabilities make it the ideal onramp for organizations looking to leverage Atlas. Base64 can extract unstructured data from various document types and convert it into a structured JSON format ready for storage in Atlas. This seamless integration empowers businesses to combine Base64’s document processing prowess with Atlas’s ability to store, query, and scale unstructured data intelligently.


Use Case: Transforming Lab Reports into Actionable Insights

Base64 partnered with a global pharmaceutical company to transform their unstructured lab records into actionable intelligence. The company had already selected Atlas as their integrated data platform for building applications and sought a vendor who could seamlessly integrate with Atlas. 

The lab records contained data in tables that were highly unstructured and difficult to query. Base64’s AI solution interpreted the table rows as keys and columns as values, creating a hierarchical structure that mirrored the relationships in the original table. This structured data was then stored in Atlas. 

This global pharmaceutical company researched many products before selecting Base64 for its powerful ability to use AI to transform their archives of lab records in the most efficient and accurate manner possible. Base64’s ability to directly integrate with Atlas to store the results was the final differentiating factor. Atlas’s flexible document data model allowed the lab data to be stored in JSON format, preserving the hierarchical structure and making it easy to query and analyze.



Why Base64 for Converting Unstructured Documents for Storage in Atlas?

Base64 has three means to convert any unstructured data to structured data with little to no configuration, making it uniquely qualified to migrate large amounts of documents into MongoDB.

  1. Over 2,800 pre-built AI models can recognize and extract key data from your content without any configuration.
  2. Base64’s Semantic AI quickly classifies and extracts data, even from unfamiliar content, delivering structured output effortlessly.
  3. Build Custom Models in minutes for any content to create your own schema for the extracted data.

Key Features of Base64

  • Advanced AI for Unstructured Data: Base64’s AI models can handle everything from basic key-value pairs to complex inferences, checkboxes, and handwritten text recognition.
  • Accuracy and Scalability: With high accuracy rates and the ability to handle large document volumes, Base64 ensures efficient and reliable data extraction.
  • Seamless Integration with Atlas: Base64’s output can be directly exported to MongoDB Atlas, simplifying the process of storing and analyzing unstructured data.
  • Flexible Deployment Options: Base64 offers both cloud-based (hosted in GCP) and on-premises deployment options to meet organizational needs.


Unlock the Value of Your Unstructured Data

Many organizations struggle to manage and derive insights from their unstructured content. Base64, in combination with Atlas, offers a powerful solution. By automating the extraction and structuring of data from documents, Base64 enables businesses to leverage the power of Atlas for intelligent data management and analysis.


Conclusion

Base64 and MongoDB Atlas provide a comprehensive solution for unlocking the value of unstructured data. With Base64’s advanced AI and seamless integration with Atlas, businesses can revolutionize their document workflows and turn unstructured content into actionable insights. Learn more about Base64 at https://base64.ai/.

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