Providing intelligent data extraction solutions for an analytics relation specialist

Intelligent data extraction solutions represent a transformative leap in information processing, leveraging advanced technologies to automate the extraction of valuable insights from diverse data sources. By understanding context, relationships, and patterns within the data, intelligent extraction systems can accurately identify and extract relevant information, reducing manual efforts and minimizing errors.

Uncover the journey of a global consulting firm to revolutionize data extraction and analysis, a mission that led them to partner with Netscribes. Explore how this strategic collaboration helped them navigate the competitive landscape, analyze data from varied sources sustainably, and maximize productivity with minimal manual intervention.

Objective and challenges 

The analytical insights provider wanted to optimize and automate its entire data extraction & analyzing process. To be the go-to expert as a leading data solution provider, the consulting firm reached out to Netscribes to develop an AR-based platform to assimilate accurate and informative data from various online media to obtain analytical solutions.

Their challenges:

• It was a manual process to provide holistic data insight at scale.
• Extracting and analyzing data from online media like blogs, podcasts, and videos was not feasible as it involved tangible human efforts and time

These limitations made the data extraction and analysis an exhausting task for this data solution firm’s employees.

Approach and solution

Phase 1: Text analysis

• To develop an intelligent module, multiple components like Data Collectors, Data Extractors, Data Parsers, and NER Models were incorporated
• To extract data from PDF, HTML, and Excel-based reports, the module was implemented
• To control the quality of the mined dataset, they were sent to the QC team to be reviewed
• To increase machine accuracy and decrease manual involvement, the ML module was re-trained based on the QC review

Phase 2: Image analysis

For optimizing the intelligent module we worked on improving the image processing step. Here’s how:

• To revamp the image quality, Stable Diffusion techniques were applied
• To classify images, NS proprietary CNN ML models were implemented
• To annotate images based on Object Detection, proprietary R-CNN ML models were utilized

Phase 3: Video analysis

• To extract data from videos, they were split into image frames and audio
• To revamp image frames, they were refined through the image analysis module
• To convert the audio into text, they were screened through standard transcription techniques. Next, the extracted text was processed through the text analysis module

The output of the intelligent pipeline was an analysis of the reports extracted from multiple sources, like databases, social media, and other publicly-available information. The optimized module added more attributes that offered more insights

Results delivered 

Through our joint effort, the firm witnessed:

• Improved TATs by 50%
• Increased efficiency by 30%
• Human involvement reduced to 20%
• Data accuracy optimized by 70%