Azure Machine Learning Studio vs Microsoft BI (MSBI)

• User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
• Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
• Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
• Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
• Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.

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• The layout of Power BI is very intuitive. Someone that is familiar with Excel and working with Charts and Graphs in that environment will find the learning curve a rather short one to start using Power BI.
• I like the way Power BI fits an assortment of users and how the functionality that you engage is replicated in Excel, that being Power Query and Power Pivot. So what you learn in one tool can be readily applied towards the other which allows you to more effectively apply your training.
• I appreciate how Microsoft is working to develop tools that go a long ways to empowering the end user. Prior to Power BI I would have had to consult with a "BI" professional to develop a dashboard. With Power BI I don't have to consult with anyone, I can work to put together the dash board I want and using a tool set that is really robust and allows me to engage an enormous amount of data. It's provides a great deal of flexibility and the types of data I can connect to.
• Updates...Microsoft is working diligently to keep Power BI current with monthly updates. They do a really good job of listening to the end user, if there is functionality not currently present just give them a month or so.
• Just to be clear, even though it's easy to get going right out of the gate with Power BI it provides plenty of opportunities to create some really sophisticated reporting solutions. With DAX in Power Pivot and M language in Power Query, you are provided with plenty of head room to do some really amazing things in Power BI.
• Training...there are resources across the web for learning and growing your skills and Power BI. And what's even better is the majority of those resources are free.
• Data engagement, when presenting the data to the end user Power BI goes a long way to allowing that end user to engage the data and begin to identify root cause by simply interacting with the graph/chart/data set. It allows for really fluid engagement. Prior to Power BI so many times during the presentation of data we often times ended the engagement with that data with more questions than what were answered. With Power BI, more often than not, the end user is able to get answers to the questions by simply clicking on the data in the graph/chart/dataset to see the details. This tool really does have the capacity to make you look like a rock star.

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• It would be great to have text tips that could ease new users to the platform, especially if an error shows up
• Scenario-based documentation
• Pre-processing of modules that had been previously run. Sometimes they need to be re-run for no apparent reason

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• The race to perfect gathering of Non-Traditional datasets is on-going; with Microsoft arguably not the leader of the pack in this category.
• Licensing options for PowerBI visualizations may be a factor. I.e. if you need to implement B2C PowerBI visualizations, the cost is considerably high especially for startups.
• Some clients are still resistant putting their data on the cloud, which restricts lots of functionality to Power BI.

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• Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
• Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
• Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details

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• As a SaaS provider we see being able to provide self-service BI to our client users as a competitive advantage. In fact the MSSQL enabled BI is a contributing factor to many winning RFPs we have done for prospective client organisations.
• However MSSQL BI requires extensive knowledge and skills to design and develop data warehouses & data models as a foundation to support business analysts and users to interrogate data effectively and efficiently. Often times we find having strong in-house MSSQL expertise is a bless.

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