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Six years of Datashift, three lessons learned

Datashift’s story started on January 15th, 2015. From the get-go, it was clear we wanted to do great things with Datashift. But building a company from the ground up is never a straight line. There are bumps in the road, highs, and lows, but most importantly: lessons learned. As we celebrate six years of Datashift, I'd like to take a look back and share exactly some of those lessons.

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How to build a churn prediction model that actually works

The truth is, predicting churn is easy. The hardest part is making it actionable. With this approach you’ll retain only your valuable customers that are about to churn, with a personalized retention action at the right time.

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5 ways to leverage your existing data with minimal effort

Do most data blogs make you feel like everything you should do is expensive, time consuming and requires complex skills? Well, this blog is different. Here are 5 practical recommendations (in no particular order) to leverage your existing business data with less effort than you think.

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How to auto scale your Azure SQL database in Azure Data Factory

One of the advantages of running databases on Azure SQL Database is the ability to dynamically manage them to adapt to changing workload demands. Autoscaling databases is the most cost effective way to increase performance of data operations. In this guide we'll show you how to auto scale your database using Azure Data Factory.

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