According to Forbes, 59% of executives say their company’s big data would be improved through artificial intelligence. Yet most data vendors are still using traditional methods that pre-date the internet (Yikes!). These older data collection techniques are unable to maintain quality at the scale of today’s data-driven marketplace demands.
Advanced sourcing techniques are rapidly replacing traditional methods. Here’s how it works:
The Truest Source
To get data from the purest source, modern data vendors are leveraging machine learning to crawl the web, extract relevant information, and model that information to produce significantly better data quality, coverage, and depth. Instead of human labor being used to manually verify information, humans are in the loop to provide feedback when the ML system encounters something new.
AI & Humans Working Together
Artificial intelligence is machine intelligence. Yet, how can a machine evaluate the business landscape as intelligently as a human would? The answer comes when humans and machines work together.
Modern data sourcing involves teaching machines the relevant patterns and addressing any corrections as needed. Those machines then scale the ocean of B2B information to source significantly more broad and accurate data than traditional approaches.
Deep Machine Learning
AI can learn from a series of patterns, and develop a nuanced level of knowledge just like a business professional would, but can also scale like a machine. If the machines encounter new information that doesn’t fit the pattern (such as a new trend, term or phenomenon), humans provide feedback that helps machines learn new rules to use when assessing future data.