AI and Predictive Analytics: Myth, Math, or Magic

| February 10, 2020

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We are a species invested in predicting the future as if our lives depended on it. Indeed, good predictions of where wolves might lurk were once a matter of survival. Even as civilization made us physically safer, prediction has remained a mainstay of culture, from the haruspices of ancient Rome inspecting animal entrails to business analysts dissecting a wealth of transactions to foretell future sales. With these caveats in mind, I predict that in 2020 (and the decade ahead) we will struggle if we unquestioningly adopt artificial intelligence (AI) in predictive analytics, founded on an unjustified overconfidence in the almost mythical power of AI's mathematical foundations. This is another form of the disease of technochauvinism I discussed in a previous article.

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Starburst

Starburst gives analysts the freedom to work with diverse data sets wherever their location, without compromising on performance. Using Presto, the Starburst Distribution of Presto provides fast, interactive query performance across a wide variety of data sources including HDFS, Amazon S3, MySQL, SQL Server, PostgreSQL, Cassandra, MongoDB, Kafka, and Teradata, among others. Founded by the largest team of Presto committers outside of Facebook, Starburst is the only company providing enterprise support for the Presto project. Originally invented and open sourced by Facebook under the Apache License, Presto is the fastest growing SQL engine driven by a community of users --- from small companies to the Fortune 500.

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