The Account‑Matching Innovation That Is Transforming Data For Business

Experts Have Developed the Ultimate Machine-Learning Reference Source for B2B Information.

Unreliable data is the biggest challenge facing go-to-market and operations teams. A significant hurdle to building reliable data which avoids duplicates while guaranteeing accuracy and coverage, is data matching when entities must merge together.

In data science, a ‘match’ is only as good as the reference set being used to render it. If a match gets recorded against an inaccurate reference set, then major gaps in business insights arise.

Data vendors have historically matched accounts using a traditional approach called ‘fuzzy matching’. However, there is a new innovation powered by machine-learning and natural language processing technology that is completely revolutionizing the way businesses organize and leverage data across the company.

Below are details about this critical data management topic, including definitions, answers to frequently asked questions, expert commentary on the particularly unique challenges of B2B account matching, and the innovation changing the world of B2B data.