Database Hygiene for Business Information Platform

Robust data hygiene ensured a clean and accurate B2B database.

Project Overview

Business Needs

The B2B Data Management Company aimed to fortify its 50 million-record database by eliminating data decay and enhancing comprehensiveness. While scaling up new profiles was crucial, continuous validation and updates were equally vital. They sought an experienced partner in data management to facilitate omni-channel sourcing, validation, and data enrichment for their database operations.

Challenges in Database Enrichment and Validation

  • To keep up with the rapid changes in company data due to new CXOs, location changes, M&As, etc., updates and validity checks were to be conducted swiftly.
  • Additionally, capturing data from privately held smaller and medium-sized companies, including unregistered startups, poses challenges.
  • Bringing together public and private data from multiple sources while adhering to privacy norms is essential.
  • Managing large volumes of ongoing omni-channel data acquisition and validation is crucial to maintain b2b data hygiene.

Solutions and Results

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Maintaining database hygiene for business information platform, a dual approach was devised:

  • Acquiring and validating new company profiles from diverse sources to expand the existing database.
  • Continuously updating and enhancing the current data through validation, enrichment, and regular cleansing formed an integral part of our data hygiene solution.


  • Scheduled bots and crawlers were deployed worldwide, gathering raw data from public and private sources, both structured and unstructured.
  • A rule-based validation process authenticated key details like founder name, acquisition amount, IPO launch, revenue, and employee growth using trusted online sources.
  • Programmable bots enriched records with missing or outdated contact-level and firmographic data.
  • Robotic process automation driven by machine learning ensured the regular addition of verified company records to the database.

Quality check and audit:

  • Multiple layers of validation checks ensured the reliability of firmographic, technographic, and behavioral data points.
  • Data quality audits were conducted in integrated environments to identify missing records, which were then appended to ensure a complete and authentic dataset.

Business Impact

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