Data Science in Power BI: Introduction

Today, we're going to start a new series about performing Advanced Analytics and Data Science using Power BI.  For those of you who are avid readers of this blog, you'll know that we try to showcase the ways where we can derive business value from easy-to-use tools.  This series will be no different.  We'll be focusing on a number of relatively simple ways to deliver advanced analytic solutions using Power BI.

Power BI has come a long way since our last Power BI series.  We can happily say that Power BI is now one of the best analysis and visualization tools on the market.  If you're interested, the previous series started in February 2016 and the introductory post can be found here.

For those that aren't familiar with Power BI, there are two main components that we will cover throughout this series.  The first piece that we will cover is the Power BI Desktop application.  It's a free desktop application that you can use to explore data from many different types of sources, as well as manipulate and visualize the data in almost any way imaginable.  If you wish to follow along with the series, you can download Power BI Desktop here.

The second piece that we will cover is the Power BI Service.  It's an inexpensive online service that allows people to share their Power BI Datasets and Reports within their organization.  It also has some functionality for sharing with users outside of your organization.  Depending on your needs, you may even be able to develop your Power BI Reports directly in the online service.  The service has limited capabilities compared to the Desktop application, but it does have a few interesting capabilities that we may touch on.  You can access it here.

Before we jump into the series about Data Science, let's have a short conversation about what types of solutions we'll be looking for.  In other words...what is Data Science?

What is Data Science?

The above picture is Gartner's Analytics Maturity Model.  It shows the different phases of analytics that organizations commonly navigate.  We can see that organizations start by asking "What happened?".  Then they start asking "Why did it happen?".  Next, they start asking the more complex questions of "What will happen?" and "How can we make it happen?"  In our minds, the last two questions are obviously Data Science.  However, we also think that there are major Advanced Analytics opportunities to be found in the "Why did it happen?" realm.  Notice that we use the terms Data Science and Advanced Analytics interchangeably, as they do not seem to have a formal distinction at this time.  Here are the types of problems we hope to touch on in this series.

Show me something interesting or unusual in my data.

Are there any relationships in my data that I can't see?

 Use my data to make the decision for me.

 What information is important or impactful?

 I have too much data.  Can you reduce it down to a manageable amount?

There are a lot of very cool use cases that we plan to cover in this series.  Hopefully this series will give you a few ideas that you can use to provide easy value to your organization.  Perhaps you could even leverage one into a full-fledged solution and snatch that new position you've been eyeing.  Stick with us.  We're sure you'll find something interesting along the way.  Thanks for reading.  We hope you found this informative.

Brad Llewellyn
Senior Analytics Associate - Data Science
Syntelli Solutions
@BreakingBI
www.linkedin.com/in/bradllewellyn
llewellyn.wb@gmail.com