Based in USA, the company is one of the topmost specialist advisory firms in BFSI industry that collects and delivers key financial business data to a leading group of Economic Development Organizations (EDO) across the globe.
To attract Foreign Direct Investment (FDI), the financial advisory company provided EDOs with exhaustive and accurate investment intelligence. They collected, verified, collated and structured huge volumes of unstructured financial data into cohesive customer-centric investment portfolios. Business decisions based on invalid or inaccurate data could lead to major financial losses.
Given the sheer volumes and their heavy dependency on manual methods and complex processes, the company faced difficulties in:
- Standardizing and updating the data
- Filling in missing details
- Removing inaccuracies
To maintain an accurate, updated and reliable database, they were looking out for a data verification services partner to process records efficiently and deliver a flawless database.
- Verification of thousands of data records with details spread across 80+ fields like employee count, revenue, phone numbers, addresses, foundation year, head quarter location, etc. was complex and time-sensitive.
- Checking data for accuracy involved deploying extensive web research skills and logical thinking for missing details identification.
- Dependency on different verification sources often resulted in data discrepancies for the same record; zeroing on the right source was a tough task.
- Rapid decay of customer information made verification of datasets difficult.
HitechDigital curated and verified huge and chaotic financial datasets with duplicate and inconsistent data to build a high-quality, validated and integrated database. Macros, customized bots and rule-based scripts enhanced accuracy and pace.
- Structured workflow was designed which split the data specialists team into two, effectively mapping task complexity with resource competency.
- Based on the complexity and ease of its verification, various fields were classified and routed to different teams.
- Deployed manual methods and ML-based models to identify and weed out duplicates from records and verify and validate details including company name, locations, phone numbers, revenue, employee count, etc.
- Developed custom scripts, macros, bots and crawlers to scan multiple websites, determine exact address of company’s global headquarters and capture the data.
- Rules-driven algorithms also helped to verify/identify genuine records from multiple information sets of same company across different sources.
- Consolidated the verified and validated data into a structured database and applied automated rules to cleanse and standardize it.
- For quick and easy view, the final data was delivered in a visually attractive, interactive and customer friendly investment portfolio format to the financial advisory firm.