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Power BI Blog

  • London's Metro Bank uses Microsoft’s Power BI to help improve customer service

    While we love talking about the latest out of Microsoft, the best part of talking about the latest and greatest is hearing how our customers are able to use our products to meet – and beat – goals, increase efficiency, or get a leg up on competitors. This last benefit – gaining a competitive edge – was of particular importance to Metro Bank, which in 2010 became the first new retail bank in Britain in more than a century.

    Metro Bank prides itself on being different. From round-the-clock call centers staffed by people (not machines) and “Magic Money Machines” to entertain kids, to the ability to open accounts and issue debit cards within minutes, Metro Bank’s priority is unrelenting customer service. This approach paid off, resulting in thousands of customers joining the ‘banking revolution’ over the last four years. As the bank continued to grow, it needed a business intelligence solution that could help it understand how customers used all of its services, from in-store to mobile and online. This information would help Metro Bank fine-tune its services and move toward its goal of 1 million customers by 2020. Instead of a third-party BI system, Metro Bank went with Power BI for Office 365.

    Over the last year, Metro Bank put Power BI through its paces, creating a variety of dashboards to track bank operations, including the launch of a mobile banking service. Features such as Power Q&A enable executives and colleagues alike – regardless of previous experience with business intelligence – to ask questions in natural language, accelerating adoption throughout the company.

    For more information on how Metro Bank is using Power BI for Office 365 to reach a million members, check out the brand new case study.

    If you’re interested in trying Power BI, do it for free today.

    If you’re an Excel power user or are simply interested in growing your analytics skills, check out the free “Faster Insights to Data with Power BI” training

    #Power BI#Customer Story#Office 365#Metro Bank

    Thu, 11 Sep 2014 17:00:00 GMT byPower BI Team0 Comment

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  • Three Steps To Gleaning Actionable Insights Using Power BI

    By Pedro Ardila; Designer at Microsoft, Ironman competitor, Data Geek

    Triathlon is a changing sport. Over the past twenty years, there has been a tremendous amount of change to equipment, training methodology, and racing strategy. No detail is too small for those athletes whose main goal is to qualify to a world championship event. There is no bigger stage in triathlon than the Ironman World Championships in Kailua-Kona, Hawaii.

    What goes into qualifying to this event? In most cases, it is months—or even years—of training, tens of thousands of dollars in equipment and entry fees, and of course, lots of support from family and friends. One aspect that no athlete should leave out is strategy; and every athlete’s strategy starts with a couple questions:

    1. What race should I enter in order to have the best chance to make it to the big dance in Kona?
    2. What times do I need in the swim, bike, and run portions to have a chance to qualify?

    I asked myself these questions, and decided to use my Microsoft Power BI knowledge to get some insights into what is needed to qualify. Here are the steps I followed:

    Step 1: Get Data

    I wanted to get some general demographic information to understand who enters these races. I also needed some recent race results to understand the characteristics of each race, and to get a better feel for the competition. I gathered the demographics data from USA Triathlon followed by a few thousands of rows worth of results for different races from ironman.com, and this is a lot of data. What made working with this data simple was using Excel and Power Query.

    Let’s focus on how I gathered results from the Ironman website. Ironman does a great job storing the results for all its races. However, the pagination system makes it hard to export results for further analysis. Here is where Power Query becomes essential. Without Power Query, I would have to manually copy each page to an Excel spreadsheet, which is extremely tedious and time consuming. I decided to write a couple of queries that would go through each page and collect all the results for a race.

    Query 1: GetData

    Collects the results for a page given the page number. The second query, called KonaResults2023 (see below) will call GetData once for each page of results. We want to disable auto-loading to the worksheet for this query because KonaResults2013 will ultimately be responsible for getting the complete results into my model. Here is the code for the query:

    let    #"a"= (page) =>let    Source = Web.Page(Web.Contents("http://www.ironman.com/triathlon/events/americas/ironman/world-championship/results.aspx?p=" & Text.From(page))),/*replace this with the url for your desired race. Make sure to trim the url to the same spot: “http://...?p=” and leave out the other variables*/    Data0 = Source{0}[Data],    ChangedType = Table.TransformColumnTypes(Data0,{{"Name", type text}, {"Country", type text}, {"Div Rank", type number}, {"Gender Rank", type number}, {"Overall Rank", type number}, {"Swim", type time}, {"Bike", type time}, {"Run", type time}, {"Finish", type time}, {"Points", type number}})in    ChangedTypein    a

    Query 2: KonaResults2013

    This query calls GetData n times, where n is the number of pages we need to go through. In this case, n = 107. The query also expands the results from GetData into a table and adds some formatting. I made sure to load the results straight to the data model as I was planning to visualize it using Power View. Once I ran this query –and a few more for the other races I wanted to analyze—I was ready for some analysis. Check out the code:

    let    Source = {1..107},/*replace 107 with the last page on your desired race.*/    TableFromList = Table.FromList(Source, Splitter.SplitByNothing(), null, null, ExtraValues.Error),    InsertedCustom = Table.AddColumn(TableFromList, "Custom", each GetData([Column1])),    #"Expand Custom" = Table.ExpandTableColumn(InsertedCustom, "Custom", {"Name", "Country", "Div Rank", "Gender Rank", "Overall Rank", "Swim", "Bike", "Run", "Finish", "Points"}, {"Custom.Name", "Custom.Country", "Custom.Div Rank", "Custom.Gender Rank", "Custom.Overall Rank", "Custom.Swim", "Custom.Bike", "Custom.Run", "Custom.Finish", "Custom.Points"}),    RemovedColumns = Table.RemoveColumns(#"Expand Custom",{"Column1"})in    RemovedColumns

    One great thing about the pattern above is that it can be used for any paginated table as long as the page number is passed through the URL. All of the transformations in the query (renaming columns, etc.) can be done through the Power Query UI, so the amount of code I ended up typing was minimal.

    Step 2: Visualize

    Next step was to visualize the data. I used Power View to create a series of charts. By using these charts, I could begin to not only see the results, but also begin to gather insights.

    Step 3: Gather Insights

    Here are some of the interesting things I found, as well as pictures for the different Power View report above.

    The first insights came from the demographics of triathlon in the United States:

    -          There is a heavy concentration of triathletes in the east coast and California

    -          Triathlon participation is higher in Michigan than Colorado

     

    The next set of insights came from slicing and dicing finisher data for a few different Ironman races. First of all, let’s look at Kona:

     

    Here we can see the size of each age group participating, as well as the average completion time for each age group. To dig deeper into the data, I clicked on the ‘M30-34’ group in the horizontal “Average of OverallInHours by Division” chart, which cross-filters our data. This step shows us that the wining participant for the  Age Group of males between age 30 and 34 finished in an astonishing time of 8.62 hours (or 8:37 minutes for those of us without a calculator).

    Now, let’s look at some of the races I am considering signing up for, and compare their 2013 results against one another. We will also include Kona for to get some perspective, even though it is a qualifying-only event.

     

    Here we start getting clues about the relative ‘toughness’ of each race. For instance, it is evident that Ironman Lake Tahoe favors strong climbers. It is a tough race overall. Here we can see that 20% of participants DNF’d (did not finish) at Lake Tahoe. Here are some more insights:

    -          Lots of people sign up but don’t start at Ironman Cozumel. My theory is that lots of people pull out of this race due to its proximity to Thanksgiving.

    -          The average finishing times for Ironman Canada and Ironman Cozumel are pretty close despite having very different elevation numbers. This doesn’t mean that elevation is not a factor. Instead, it tells us that there may be other challenges at Cozumel not accounted for in our data. In this case, those challenges are the added winds and humidity of Cozumel.

    -          There is a huge gap between Kona and the other races as far average finish times goes. This is primarily due to the selective nature of a World Championship event.

    -          Ironman Canada offers a balanced ride and a challenging run. This course seems to suit me, given that I am relatively light—meaning I don’t need to exert lots of effort while climbing on the bike—and I can run well in challenging courses.

    With this information at hand, we can now look at some specifics for Ironman Canada, such as the time breakdowns for swimming, biking, and running, and average finishing time for each age group. We will focus in on my Age group (25-29) and compare the average bike vs average run times using a scatter plot.

     

    The scatter plot has a clear trend. It is that most people are able to balance their efforts on the bike and the run. There are, however, some outliers. These could be people who were slow on the bike (perhaps due to mechanical or nutrition issues) but pulled together a great run, or people who had a really strong bike, but faded on the run. Some additional insights:

    -          Ironman Canada’s fastest age group was different from Kona’s (M35-39 in Canada vs. M30-34 in Kona)

    -          Age groups M35-39 and M30-34 were faster on average than the Female Pros.

    -          The first four people finished in under 10 hours, and were all in the top 100 overall. This means that to have a reasonable chance to qualify, I would have to finish in about 9 hours and 45 minutes.

    This last insight is extremely important, and it will help me set up objective goals for my next training season.

    Actionable Insights: How Should I Move Forward with my Training?

    Using Power BI I was able to quickly gather use big data, and then slice it and dice it until I found the answer to my original question: What time do I need to qualify to Kona? The answer is 9:45 or better. Time to start training.

    Ready to uncover your own insights?

    #power query#Power BI#Excel#data visualization#Ironman#insights

    Thu, 11 Sep 2014 16:00:00 GMT byPower BI Team0 Comment

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  • 3 Great Examples: Data Done Well

    It’s no secret that big data has big potential – here at Microsoft, we work with hundreds of customers using technology in new ways to get the most out of data in every shape and form. While each of these companies defines “success” differently, there are a few that are top-of-mind for us. The following are just a few examples of how real companies from a variety of industries across the world are seeing real benefits from working with data differently: increased efficiency, improved performance, and using resources more effectively, all with insights from their data.


    Grameen Foundation

    The Problem: The need to quantify the impact of the Ghana Mobile Technology for Community Health (MOTECH) initiative. With this information, the Grameen Foundation can eliminate ineffective programs and expand those making the biggest impact.

    The Solution: Accessible, customizable reports and data visualizations no matter the staff’s location or device. This enables employees to better understand program data, resulting in better justification of program expenses, data-driven program management and community engagement.

    The Benefits:

    -          Increased efficiency: report creation in minutes instead of hours
    -          Easy use by anyone in the company without formal training or added costs
    -          Increased awareness of MOTECH, and expansion of humanitarian efforts

     

    MediaCom

    The Problem: The need to easily measure and represent the health of an ad campaign despite vast number of diverse data sets.

    The Solution:
    A “health check” that captures the various facets of a multi-platform media campaign in one single score, incorporating paid media effectiveness, earned media effectiveness, the ratio of earned to paid media, realized client value, and longitudinal performance. The “health check” is built on Power BI for Office 365, and provides a unified campaign dashboard, as well as a collaborative site where the account team can ask questions and instantly receive answers in chart and graph form to share across the team and with clients.

    The Benefits:

    -          Increased optimization checks for campaigns: from weekly to daily
    -          High adoption of BI across the company using the Excel tools that teams already know
    -          Increased campaign productivity

     

    Carnegie Mellon University

    The Challenge: Optimize energy and operational efficiency in buildings worldwide.

    The Solution: Carnegie Mellon worked with OSIsoft (also the Microsoft Business Intelligence Partner of the Year!), to install a PI system, which integrated all of the building automation systems, as well as lights, ventilation, air quality, weather, and security data sources. Then, they added Power BI for Office 365 to provide custom-reporting capabilities for all the real-time data generated by the various systems, equipping employees with maps, visualizations, and dashboards showing information such as building types, geographic locations, and energy consumption. This information allows employees to zone in on problems such as faulty equipment, and identify places to cut back on energy consumption.

    The Benefits:

    -          The ability to present relevant information to diverse users of any analytics skill level
    -          30% less energy consumption by using data to see where equipment is faulty

     

    How are you using data and analytics to change the way you do business?

    If you’re interested in trying Power BI, do it for free today.

    If you’re an Excel power user or simply interested in growing your analytics skills, check out the free ‘Faster Insights to Data with Power BI’ training.

    #Power BI#MediaCom#Carnegie Mellon University#Power BI for Office 365#Grameen Foundation#Data Done Well

    Wed, 10 Sep 2014 16:00:00 GMT byPower BI Team0 Comment

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