• Data Heterogeneity: Extensive variety and heterogeneity of content interms of file formats, structures within the documents (Table, images,Paragraphs etc), various format (Scan & digital,etc)
• Tedious repetitive steps require capturing significant quantity of data points from vast volumes of documents. This involves time to search and time to punch the data on application.
• Operational inefficiencies like Manual referencing to entries to populate data into system, Multiple screen toggles and clicks to update entries, leading to considerably high lead time & cost.
• High volume with inherent domain knowledge for finding relevant information, context based meaning and duplicate information within documents.
• Hierarchical nature of data and complex tabular structures with continuous tabular structures.
• Different & complex entity hierarchy to be mapped with companies structured taxonomy.
In confronting these challenges head-on, Client recognizes the need for a comprehensive solution that ensures efficient data scalability, quality improvement, faster processing, and enhanced adaptability for sustained growth and success.
To address the challenges faced by the firm, a comprehensive solution approach was implemented. This involved meticulous data cleansing and parsing, harmonization, exception handling, and validation processes. The automation of data processing was prioritized to eliminate manual intervention, ensuring efficiency and accuracy. Phoenix automatically extracts relevant information from unstructured or semi-structured data sources using AI and machine learning algorithms. This enabled businesses to efficiently extract, categorize, and analyze large volumes of data from various sources such as documents, emails, images, and web pages.
AI extracts data through Phoenix, which involves several steps:
• Data Capture: AI-powered systems capture data from various sources such as documents, images, emails, web pages, and databases.
• Pre-processing: The extracted data undergoes pre-processing to enhance its quality and prepare it for analysis. This includes tasks like image preprocessing, noise reduction, and text normalization.
• Feature Extraction: AI algorithms analyze the data to identify relevant features and patterns. This step involves extracting key information from unstructured or semi-structured data sources.
• Machine Learning: Machine learning algorithms are trained on labeled datasets to recognize patterns and relationships within the data. These algorithms learn from examples and adjust their parameters to improve accuracy over time.
• Natural Language Processing (NLP): For textual data, NLP techniques are used to analyze and understand the meaning of words, phrases, and sentences. This enables AI systems to extract contextually relevant information from text-based sources.
• Optical Character Recognition (OCR): OCR technology is employed to convert scanned documents and images into machine-readable text. This allows AI systems to extract text-based data from images and scanned documents.
• Validation and Verification: Extracted data is validated and verified to ensure accuracy and consistency. This involves cross-referencing with external databases, comparing against predefined rules, or human validation.
Finally, the extracted data is presented in a structured format suitable for analysis, reporting, or integration with other systems. This output can be used for
various purposes such as decision-making, automation, or further analysis.
