More and more countries are imposing requirements on organisations to provide Standard Audit Files for Tax purposes (SAF-T), including the UK. HMRC requires businesses to keep their records digitally and provide their VAT returns through Making Tax Digital (MTD) functional compatible software as of April 2019. So this seems like an opportune moment to continue my series of blogs about Yellowfin partners by introducing the brilliant 4apps – a business helping tackle the VAT software problem.

The best piece I read this month was about how to build a recommendation engine in R by Data-Mania. It’s a little bit tangential to the BI space but it was interesting to me personally because I’m not a data scientist.

Ethereum and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Earlier this year, we made the Bitcoin dataset publicly available for analysis in Google BigQuery. Today we’re making the Ethereum dataset available.

This quote from Niccolò Machiavelli – an Italian politician, historian, and philosopher from the Renaissance period, has profound relevance to emerging technologies.

In this article, I’ll walk you through the process of building a machine learning model using BigQuery ML. As a bonus, we’ll have the chance to use BigQuery’s support for spatial functions. We’ll use the New York City taxicab dataset, with the goal of predicting taxi fare, given both pick-up and drop-off locations for each ride — imagine that we are designing a trip planner.