This week’s roundup discusses the challenges of understanding data preparation, learning to solve for algorithmic bias, Google’s new Dataset search platform, and Leveraging AI to allow marketers to connect between Account-based Marketing and social media targeting.
By Steve Ludwig, Senior Consultant with Lityx
Organizations that understand the distinction between reporting and analytics have a tendency to innovate at a higher rate largely due to the data quality they receive and the analytics they provide. The challenge many firms come across when compiling data is understanding the important distinction between analytics and reporting, analytics is forward-driven, answering what will happen. While reporting usually looks at what has already happened.
by Den Howlett, co-founder at diginomica.com
Are we hindering our own data by inferring a level of bias? Dan Howlett co-founder of diginomica.com dives deep into the problems with recruiting technology and the biases that occur among data recruiters or rather recruiter practices. He points to inevitable suboptimal outcomes in the man-machine interface we call AI-enhanced activities.
by Sam Shead, Artificial Intelligence and Data Journalist at Forbes.com
Earlier this month Google announced a new search engine, DataSet Search that has the potential to help scientists, data journalists, policymakers and other user groups to find the datasets they need for their work. As more data repositories use the site to describe their datasets, the variety and coverage of datasets, the more it will continue to grow.
by Chris Matty, co-founder, & CEO of Versium
Leveraging AI allows marketers to connect between Account-based Marketing (ABM) lists and consumer targeting parameters to extend the reach into social and digital display environments so highly-targeted audiences can be delivered at scale.
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