[A version of this post appears on the O’Reilly Radar.]
While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data.
In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the data community about the many interesting opportunities and challenges in data itself. Much of the focus of recent press coverage has been on algorithms and models, specifically the expanding utility of deep learning. Because large deep learning architectures are quite data hungry, the importance of data has grown even more. In this short talk, I describe some interesting trends in how data is valued, collected, and shared.
Economic value of data
It’s no secret that companies place a lot of value on data and the data pipelines that produce key features. In the early phases of adopting machine learning (ML), companies focus on making sure they have sufficient amount of labeled (training) data for the applications they want to tackle. They then investigate additional data sources that they can use to augment their existing data. In fact, among many practitioners, data remains more valuable than models (many talk openly about what models they use, but are reticent to discuss the features they feed into those models).
But if data is precious, how do we go about estimating its value? For those among us who build machine learning models, we can estimate the value of data by examining the cost of acquiring training data:
Continue reading “Data collection and data markets in the age of privacy and machine learning”