We take you through 10 best practices, considerations, and suggestions that can enrich your Microsoft Teams deployment and ensure both end-user adoption and engagement.
My colleague Sean Roy just put up a great post about Gartner’s predictions for Advanced Analytics in 2015:
This is of obvious interest to us in the Microsoft universe, as we perennially end up being in the “happy” part of Gartner’s magic quadrant, and since Microsoft’s ongoing data and analytics story is called out here. I will assume you can click through and read the post, so I’m not going to repost content. But I do want to spin off of that mention and make a note of exactly what Azure Machine Learning is, and where it fits into the overall landscape of the Microsoft Data Platform.
Azure ML is a cloud-based Predictive Analytics offering, currently in preview. It is fully managed from the get-go (meaning no downloads or installs), integrates simply with a basic drag-and-drop interface, and contains algorithms developed by Microsoft for Bing and Xbox — although it also supports coding with R (the statistics programming language). Essentially, Azure Machine Learning allows you to create advanced predictive models directly from a browser, and to make them operational with a few clicks.
Once you have established your model, you can collect basically unlimited data in Azure Storage, and easily connect to that data using Azure’s data services such as HDInsight (cloud-based Hadoop), Azure SQL Database (a PaaS model version of SQL Server) , and Azure Virtual Machines running SQL Server 2012/2014.
And then, from the user perspective, any of this data is available for consumption via Power BI both on the desktop and as part of Office 365. Users can connect to any of those sources directly from Excel, allowing them to use a friendly interface that has been enhanced with some powerful data tools.
This is where we see the vision of the Microsoft Data Platform coming together on the cloud side, with a combination of PaaS and IaaS offerings linking up to provide infrastructure-free Advanced Analytics (including elements of Big Data and Predictive Analytics). So, while it has been difficult to see it develop in-process, the Microsoft Data Platform becomes more compelling by the day.