Power BI: What We’re Reading

This week we found some great Power BI articles. Topics include PowerApps, Flow, Calculated Table, Visualizations, and Azure Analysis Services.

Microsoft PowerApps Now Available

We introduced the public preview of PowerApps in April 2016. In the six months since, over 124,000 users from 46,000 organizations in 143 countries have created web and mobile apps using PowerApps. We thank all of our preview users for their invaluable feedback, which has helped shape PowerApps into a better product.

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Microsoft Flow Now Available

Microsoft Flow is a cloud-based service that makes it simple to automate common tasks and business processes across your applications and services, such as Office 365, Slack, Dropbox, SalesForce.com, Dynamics 365 and many more.

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Use Calculated Table to Figure out Monthly Subscriber Churn

Power BI Desktop 2015 SU09 introduced a really cool new feature called calculated tables. Those of you who are already familiar with calculated columns will share my excitement since we now have a flexible, intuitive, and visual way of solving common business problems which were very hard, if not impossible, to tackle previously…

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The benefits of using data visualisation in marketing

Marketers and advertising professionals are in the business of condensing a company’s key message to its very essence. Nike’s ‘just do it’ slogan and Apple’s call to ‘think different’ are among the most well-known examples of what it means to say things simply, but with maximum impact.

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Why a Semantic Layer Like Azure Analysis Services is Relevant

Fundamentally, Analysis Services serves as a semantic layer (see below for further discussion of a semantic layer). Because the business intelligence industry now embraces an array of technology choices, sometimes it seems like a semantic layer is no longer valued like it once was. Well, my opinion is that for many businesses, a semantic layer is tremendously important to support the majority of business users who do not want to do their own data wrangling, data prep, and data modeling activities.

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