Attorney - Case study

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Data Automation for the Legal Industry

Client

A US-based prominent online legal marketplace.

The Business Need

To overcome the time-consuming and error-prone manual process of profile updates, our client has entrusted us with implementing intelligent agent support. Through quarterly tracking of specified state bar websites, we automated the identification of new records or updates to existing data for attorney database enrichment.

Challenges

State bar websites may have different structures, layouts, and data formats, making it challenging to develop a uniform web scraping approach. The data engineering team needs to analyze and understand the data sources’ complexity to ensure accurate extraction and integration. When scraping data from multiple sources, duplicate attorney profiles may occur. Detecting and removing duplicate entries is vital to maintain a clean and reliable database.

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Our Solution

The project encompassed the following key components:
● Web Scraping: Our data engineering team instilled web scraping techniques which includes site analysis, crawler development, testing and deployment to automate the extraction of attorney data from each state bar website.
● Data Integration: We created a centralized database where the scraped data was aggregated and seamlessly integrated with our client’s existing attorney profiles. This ensured a comprehensive and up-to-date repository of attorney information.
● Data Cleansing and Standardization: We implemented algorithms to cleanse and standardize the collected data, eliminating inconsistencies, and ensuring accuracy and reliability. Then we developed algorithms to compare the scraped data with existing profiles in our client’s database. This helped to remove the duplicate attorney data that was collected.
● Quality Assurance: We established a robust two-tier data quality assurance process to ensure accuracy and compliance. The Process Excellence Group (PEG) employed automated algorithms to assess data accuracy, delivering only compliant batches that meet the agreed Service Level Agreement (SLA) to the clients. In case of any discrepancies, the data underwent rework to rectify the issues and ensure high-quality output.

Highlights

Improved data accuracy: The data accuracy rate increased by 95% after implementing data cleansing and standardization algorithms, ensuring a highly accurate dataset.
Streamlined Operations and Efficiency: The implementation of web scraping and automation techniques reduced data collection time by 80%,
freeing up resources to focus on higher-level analysis and decision-making.
High quality output and compliance: The data quality assurance process achieved a 95% accuracy rate in delivering compliant data batches to the clients, meeting the SLA requirements and ensuring high-quality output.

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