DesignMind Power BI Showcase Airline Analysis скачать в хорошем качестве

DesignMind Power BI Showcase Airline Analysis

Want to learn more? Check out this blog post "Data Visualization with Power BI and R Visuals" http://www.designmind.com/business-in... Also, see the Microsoft Power BI webinar on September 27, 2016 https://info.microsoft.com/US-PowerBI... Transcript: Power BI Showcase Video on Airline Analysis by Angel Abundez and Eric Bragas of DesignMind. http://www.designmind.com Hello and welcome to DesignMind's showcase of Power BI. I'm Angel Abundez, VP of Business Intelligence at DesignMind where we help clients connect transform and visualize their data with Power BI. Today we're taking a look at the airline industry with data downloaded from the U.S. Department of Transportation. In this example, we'll analyze data from the year 2015, for airlines domestically, to look for trends in utilization and fuel costs. On this first page we're looking at a couple of utilization metrics: load factor and destinations. At the top of the report, we see load factor and destinations for one month of my data for JetBlue. In the bar chart on the left I can analyze load factor by originating airport. Or, if I choose, I can look at the number of flights arriving at different destinations. On my right I have an R visual which I've built using R that goes into more detail analyzing load factor by flight route and destination flights geographically, to get an idea regionally how utilization is doing by airline, by airport, or by flight route. Now when we look at airlines with a high number of domestic flights, flight routes on the map can get very overcrowded. To aid with this we added a field to the data model called flight occurrence, which not only helped make the geographic analysis more clear, but also more interesting. By introducing a new variable to the data model, we were able to do quick checks and utilization between airports that aren't flown to very often, to airports that are flown to more than a hundred times a month. Now let's take a look at fuel costs. On the fuel cost page, the KPIs at the top of the report let us see year to date fuel costs for JetBlue, or month to date if we click on the month over month graph. Additionally the month over month graph is actionable, plotting change in fuel costs along with additional tooltip information so airline personnel can address if fuel costs were a function of price or distance traveled. On the bottom we have fuel costs split by aircraft to do a finer analysis on equipment being used and then further drill down to the flight level to see profitability down to the individual flight route. From our findings on the utilization page, we can do a search for those airports in question either by airport code or by airport name. This new search capability within slicers along with our previous filter for flight occurrence narrows down our focus right where we want to be. Thanks for taking the time to watch this DesignMind demo. We hope you realize that Power BI can not only create some powerful visualizations out of the box, but it's extensibility through technologies like R make it a great platform to build data analytics and share those within your team and to the world. If you have any questions about Power BI or any other data related needs, please don't hesitate to contact us at contact@designmind.com. This is Angel Abundez of DesignMind. Thanks for watching.