Revolutionizing data collection automation through technology-enabled operation augmentation

Uncover the journey of a UK-based investment data leader as it transforms its data collection approach, revolutionizing financial insights in the alternative assets market. Faced with challenges in reliance on manual processes and sources, the firm sought to overcome errors, delays, and quality issues impacting customer experience. Dive into how Netscribes strategically imbibed automation in the data collection process, achieving remarkable efficiency gains, a 70% reduction in turnaround time, and a significant enhancement in data quality.

Challenge

The firm heavily relied on public news sources and forums for research purposes. The manual and intensive process of collecting data from these forums posed challenges such as errors, prolonged turnaround times, and resultant quality issues, ultimately leading to a subpar customer experience.

The manual process of gathering data from public forums was error-prone, time-consuming, and detrimental to data quality. The client faced challenges in delivering timely, accurate, and high-quality financial insights, impacting their customer experience negatively.

Approach and solution

Netscribes collaborated closely with the client to identify key data points and outline a strategic approach to automate the data collection process.
Key data points identified:

• Contacts
• Company Profile
• Fund Managers
• Fund Performance
• Investors
• Service Providers
• ESG Profiles
• Deal News
• Company Financials, etc.

Data automation process:

• Data source identification: Identified third-party aggregators for data collection, achieving approximately 50% data automation.
• Data collection: Leveraged web crawlers to scrape content along with metadata, achieving nearly 100% data automation.
• Data categorization: Implemented Natural Language Processing (NLP) modeling to classify events in articles, achieving around 50% data automation.
• Data quality check (QC): QC verified the data and approved it, achieving approximately 70% automation.
• Data capture: Human agents qualified tagged data, entering them into a workflow system where validation and rules were applied according to the front-end data format, achieving around 40% data automation.
• Data identification: Utilized Named Entity Recognition (NER) model to tag entities of interest, achieving approximately 35% data automation.
• Data update: Implemented automatic updates using API push to the Preqin backend, achieving nearly 100% automation.

Results delivered

Through our collaboration, the firm witnessed:

• ~70% reduction in turnaround time
• Significantly improved data quality, reducing manual intervention and enhancing customer experience
• Implementation of NLP modeling allows continuous improvement in automation, ensuring sustained efficiency gains