Validating data quality is one of the few remaining ways of taking market research to the next level.
Even though lots of progress has been made over the years to produce clearly defined requirements for data annotation (which means labelling/tagging data), these efforts tend to come with a trade-off: they’re either accurate (completed by humans) OR they’re scalable (predicted by an algorithm) – but rarely is the data cleansing accurate AND scalable.
And to make matters worse, trying to do both manual data annotation and implement AI fraud detection tend makes for longer waiting times, higher costs, and ultimately a clunky, slow process.
But what if this could be done differently?
At Attest, we’re on a quest to achieve an unassailable level of audience quality. To do this, we’re combining the depth of our domain knowledge with the breadth of our Machine Learning solutions.
The result is an AI-assisted human-in-the-loop learning framework, allowing us to continuously learn and adapt our scalable approach, and provide real-time validation as soon as the data reaches our servers.
Join Attest’s Senior Data Scientist, Dr. Gokhan Ciflikli, as he leads this unmissable session taking you through the state-of-the-art machinery that powers our data quality algorithm.
- Learn more about the fundamental problem of data validation at scale
- Understand what constitutes a cutting-edge data infrastructure built
- Discover how Attest employs a multi-faceted fraud detection model that identifies more than just ‘anomalies’ in the data