Microsoft Business Intelligence

Surfing Restaurant Inspections with Microsoft Data Explorer and GeoFlow

2013-06-13T16:00:00Z

Father’s Day is approaching and you might be thinking about a good place to have a nice lunch with your Dad… We would like to show you how Data Explorer and Geoflow can help you gather some insights to make a good decision.

In order to achieve this, we will look at publicly available data about Food Establishment Inspections for the past 7 years and we will also leverage the Yelp API to bring ratings and reviews for restaurants. For the purpose of this post, we will focus on the King County area (WA) but you can try to find local data about Food Establishment inspections for your area too.

What you will need:

Data Explorer.
Geoflow.

Access to the following data:
Food Establishment Inspections data from datakc.org (more details):

We will link to this URL (which points to the CSV file version of the data) using Data Explorer: https://www.datakc.org/api/views/pph9-v8tz/rows.csv?accessType=DOWNLOAD

Yelp API (more details):

You will need to sign up for an API key before being able to connect. Please also make sure to read the Yelp API Terms of Use before using this API.

What you will learn in this post:

Import data from the Yelp Web API (JSON) using Data Explorer.
Import public data about Food Establishment Inspections from a CSV file.
Reshape the data in your queries.
Parameterize the Yelp query by turning it into a function, using the Data Explorer formula language, so you can reuse it to retrieve information about different types of restaurants as well as different geographical locations.
Invoke a function given a set of user-defined inputs in an Excel table.
Combine (Merge) two queries.
Load the final query into the Data Model.
Visualize the results in Geoflow.

That sounds like too much for a single blog post, but let’s get started and you’ll see how it is easier than you might think.

Import data from Yelp API

The first thing that we will have to do is click the “From Web” button in the Data Explorer ribbon tab. Data Explorer supports importing data from different kinds of Web sources, including scrapping tables from HTML pages, as well as importing data from Web APIs. Data Explorer supports XML, JSON and OData formats.

Taking a look at the Yelp API documentation, you will find that they offer a Search API which accepts multiple GET parameters to customize your query, including a search term (i.e. restaurants, sushi, seafood, etc.), a location (i.e. “Seattle”, “98052”, lat/long information) and a few others. Finally we also need to provide the API Key that we will receive after registering for this API (wsid parameter).

With that in mind, let’s paste in Data Explorer the following URL which will return Seafood restaurants in Redmond (note that you will have to replace the wsid value with your own key).

http://api.yelp.com/business_review_search?term=seafood&location=Redmond &ywsid=XXXXXX

We will get a result that looks like a single column table with one row, containing text data which, in fact, is structured as JSON. We can interpret this text as JSON by right-clicking in the column header and selecting “Transform -> JSON”.

Now as a result we get a record which we can expand and select which fields to include in the output. Having used the Yelp API before, we know that the “businesses” field is the one that contains the list of restaurants that we are looking for, so we can just expand that one by unselecting “message” in the Expand menu and clicking “OK”.

This will return a single cell containing a list, which we can expand using the same mechanism again and turn it into a bunch of records, one per restaurant. Now we can decide which columns to expand, containing different information about each restaurant such as Name, Lat/Long information, Rating, etc. We will expand the following columns: city, review_count, latitude, address1, name, avg_rating and longitude.

Now we have the list of Seafood restaurants into a tabular shape, and after applying some column renames and reordering the columns, we can get to the following shape.

That was actually easy… At the end, all we’ve done is define a set of data acquisition and transformation steps, which get recorded in the Steps pane on the right side of the Query Editor dialog. This Query Editor dialog is an easy-to-use interface for defining these steps, which get translated into the corresponding set of Data Explorer formulas (you can access them by clicking the script icon to the right end of the formula bar).

Parameterizing our Yelp API query

As we saw in the previous section, we can customize our Yelp Search by modifying the GET parameters in the URL. We can also make our Data Explorer query parameterized by turning it into a function, which can take input parameters and use them within the function body. In order to do this, we just need to add the following first line to our query script and use the parameters in the first step (Source). Finally, we can also rename our query to GetRestaurants.

This was very straightforward, but if you want to learn more about Data Explorer functions you can take a look at the Language and Library Specifications. There’s more cool stuff waiting for you there!

Invoking the GetRestaurants function

Now that we have a function, it would be a good thing to learn how to invoke it… There’re basically three ways in which you can invoke a function:

Invoking the function “inline”: Simply open the query editor for your function query and click Invoke. You will get asked for parameters as needed. This is a very quick way of invoking the function, however it defeats the purpose of reusability since at this point the function will no longer be available from outside this query (given that the result is a table).

Referencing the function: You can use the Reference option in the Query ribbon tab to create a new query in the workbook that references the current query. In this case, we could do this and be able to invoke our function from a new query.
Invoking the function over a table: We can also use the “Insert Custom Column” option from a different query in order to invoke a function across all rows in the table. We can also make the function parameters be based on other columns from the table.

The third method is the one that we will use in this example, so for instance we could create a table with different cities in the King County area and invoke the GetRestaurants function for each row, so that we can get restaurants for each of the cities in the list.

First, we will create a table in the Excel sheet containing a list of cities, such as Redmond, Bellevue, Seattle, Kirkland, Issaquah and Sammamish.

Now we can go to the Data Explorer tab and click the “From Table” option. This will create a new query that references the sheet table. We can insert a custom column into this table by clicking on the table icon on the top-left corner of the table and selecting “Insert -> Custom…”

In the “Insert Custom Column” dialog, we can type in any custom formula using the Data Explorer formula language. In this case, we will simply invoke the GetRestaurants function using the following formula:

At this point, we will be asked for information about the privacy level of my workbook data. This is done so that users don’t accidentally leak data from a private or organizational source and inadvertently send it to a public source (like the Yelp API in this case). We will select “Public” in this case. You can learn more about Privacy Levels in Data Explorer in our Online Help pages.

After providing this information, the new column will be added to the table. Note that we have a new column with nested tables on each row (containing the restaurants for each of the cities) and we can expand these columns similarly to how we did earlier with the JSON records. We can also remove the “Cities” column since we don’t need it anymore.

Once we have expanded all columns, you may find that there are some duplicated rows. This is due to the fact that the Yelp API may return restaurants within a radius up to 25 miles (which can also be customized in the Yelp API parameters) so a given restaurant in Seattle might be returned when searching within Seattle and Bellevue, for instance. We can get rid of duplicated rows by selecting “Remove Duplicates” in the table menu (top-left corner of the table). This will leave us with approximately 60 candidate seafood restaurants, with their ratings and total number of reviews, as well as location information (Address, City, Lat and Long). We can select the Ratings, Review_Count, Latitude and Longitude columns and make sure they are formatted as Number by right-clicking the column headers and selecting “Change Type -> Number”. Finally, let’s also rename the query to “Seafood restaurants in King County”.

With this, we have the list of restaurants in the desired shape… We could just pick the highest rated restaurant in the list and make a reservation for Father’s Day, but of course we want to know first what the King County Food Establishment Inspections records have to say about each of these restaurants…

Import public data about Food Establishment Inspections from a CSV file

The next step in our scenario is to bring in public data from datakc.org (King County’s public data web site). We found this dataset which seems suitable for our needs, since it contains information about food inspections for the past 7 years, as well as the Name, Address and City for these restaurants (which we also have in our Seafood Restaurants query).

We can get to the CSV version of this dataset by clicking the Export option in the top-right corner of the page and we will get this URL for CSV: https://www.datakc.org/api/views/pph9-v8tz/rows.csv?accessType=DOWNLOAD

This link can be used directly in Data Explorer, either using “From Web” or “From File -> CSV”…

Once we have the inspections data loaded as a table, we can reshape it a little bit as we did earlier. The experience in the Query Editor is consistent regardless of what the underlying data sources look like on each case. We’ll do the following:

Remove all columns except the following ones: Name, Address, City, Inspection Type, Inspection Result and Violation Description.
We would like to focus only on the Unsatisfactory reviews, so we can filter the Inspection Result to only include those values.

You will also notice that the Name and City columns contain values on uppercase and lowercase… This is something that we need to “normalize” so that when we combine this query with the Yelp Restaurants list we get matching rows. Let’s select the Name and City columns and use the “Transform -> Uppercase” option in the column menu (right-click in column header).
Finally, let’s rename this query to Inspections and we will end up with a table similar to this one:

The next thing we need to do is go back to the Seafood Restaurants query and also turn the Name and City columns into Uppercase, as a preparation for being able to merge the two queries based on these columns.

Combining Yelp Restaurants list with Food Inspections data

Once we’ve normalized Name and City columns in both queries, we can go ahead and merge the two queries by using the Merge option in the Data Explorer ribbon tab. Before doing that, let’s enable Fast Combine for this workbook. This will turn off the Privacy Levels check for queries being combined in this workbook and we know that, since we already marked both as Public, this is OK. After enabling Fast Combine, we can click Merge.

This will launch the Merge dialog where we can select the two queries to merge as well as the columns to match. We will pick the Seafood Restaurants query as the left query, so that we augment this table with inspections data for those restaurants where the information is available in the Inspections table. We will multi-select (CTRL+Click) the Name and City columns in both queries to specify that both columns have to match.

The text at the bottom of this dialog will tell us that we got some matches, so some of the recommended restaurants in Yelp actually are not as recommendable as they would seem, according to the inspections results… We can click OK at any time and look at the newly created column as result of the Merge.

In this case, instead of expanding the nested columns we will perform an aggregate operation, which can be found also on the same dropdown menu in this column.

We are interested in the Count of Inspection Result values, which basically gives us the number of Unsatisfactory Inspections for each of these restaurants.

With that, we will get a new column like the one in the screenshot below.

Now we can use this information to decide which restaurant we should go to. Interestingly, restaurants with an average rating of 3 or lower are the ones which also got a higher number of unsatisfactory inspections… There are also some surprises with restaurants rated as 3.5-4 and getting a significant number of negative inspection results, so be aware of that the next time you check Yelp if you don’t want to take any risks!

Visualize restaurant locations, reviews and inspections results in Geoflow

To finish this post, we will put the resulting query into a nice Geoflow map. First, we need to load the final query into the Excel Data Model, which you can do by clicking the “Load to data model” option in the Query Settings pane. Once done, it will switch to a confirmation message as displayed below.

Now you can go to the Insert tab in the Excel ribbon and select “Map”. This will launch Geoflow where we can create a new tour with multiple layers to visualize our data.

Let’s create two different visualization layers, one for the food inspections, which we can represent as a heatmap and use the Inspections column for the value field, and another layer for the Yelp ratings, which we can represent as a Column chart and use the Review_Count as column height and the Restaurant Name as categories.

In order to add each of the layers, we will need to pick the Final Report table and select the Latitude and Longitude columns first and tell Geoflow to “Map It” based on these fields.

Then we can pick the Review_Count field and select Column type chart, using the Review_Count as the Height and the Name as Category.

Then, we can add another layer to incorporate the Inspections Count as a heatmap and add the Inspections Count as the Value field. This will give us the final map as displayed below.

Finally, we will get a map similar to this one. The taller columns represent popular restaurants and we should avoid the areas with red in the heatmap (which mean higher number of unsatisfactory reviews).

Hope you liked this blog post and the only thing left for us is to wish you and your family a happy Father’s Day!!

From Data to Insight & Impact: Using Advanced Features of Data Explorer and Building Power View with Bing Maps & Animations

2013-05-23T16:30:00Z

After receiving great feedback about From Data to Insight & Impact: Showing Africa’s Progress with Power View and PPI by Microsoft, the Power View demonstration which showed the progress in Africa, we decided to show how we actually built this data visualization.

In this latest video from Microsoft Finance Director Marc Reguera's series, From Data to Insight & Impact, Marc shows you how to get public data from different web sites using Data Explorer, and then how to shape and link the data to build interactive and dynamic views. As you will see, this can all be accomplished while staying in Excel 2013 and without any advanced technical skills.

To learn more about the powerful tools featured in this video, visit the Microsoft Business Intelligence page.

5 Things You Need to Know about the Microsoft Data Explorer Preview for Excel

2013-05-15T16:00:00Z

Data Explorer Preview for Excel represents a new way to discover, combine and refine data for your self-service BI needs. If you are new to Data Explorer, here are 5 key concepts to get you caught up on how Data Explorer can fundamentally improve the way you work with data in Excel. With Data Explorer you will:

1. Discover the world’s data

Data Explorer brings the concept of data search to Microsoft Excel. Searching for public data using Data Explorer is easy and straightforward. You get rich and interactive previews of data. When you have found the data you need, a single click will import the data into your workbook.

Here’s an example of a search for “largest cities in Europe”.

2. Connect to a wide range of data sources

Data Explorer supports a breadth of data sources, ranging from simple sources such as text, to more sophisticated sources like Hadoop.

File and text based sources supported include:

Text based sources (plain text, CSV)

Excel files

Access databases

Folders (ability to treat a folder full of files as a single logical table)

Web sources supported include:

Tables on web pages

Web APIs / JSON

OData

Relational engines supported include:

SQL Server, Windows Azure SQL Database, SQL Server PDW

Oracle

Teradata

MySQL

PostgreSQL

Other sources supported include:

Windows Azure Marketplace

SharePoint Lists

Active Directory

Hadoop

Windows Azure HDInsight

Facebook

We are also working on a new set of data sources. If there are data sources you would like to see supported, please let us know.

3. Combine data from multiple data sources

Data Explorer makes it easy to combine data from different data sources. The screen below shows the experience for joining data sourced from an OData feed (Products table) and a text file (Suppliers table)

4. Reshape and transform your data effortlessly

Data Explorer makes it trivial to reshape and derive new value out of your data. Everything from simple operations such as filter and sort, to the more complex operations such as Unpivot are all done with just a few clicks.

The UI exposes the most useful transforms – but you can always use the Data Explorer Formula Language to harness the full power and expressivity of the Data Explorer.

Shown below is a screenshot of the menu of table transforms exposed in the UI. The formula bar that is shown above the table preview lets you invoke formulas and custom expressions should you need to drop down into the expression yourself.

5. Refresh your data anytime

Data Explorer queries are simply expressions that the UI creates for you when you go through the steps to import and reshape data from a data source. What this means practically speaking is that your queries can be refreshed on demand. The query pane has a refresh link that you can click to update your workbook with the latest data.

Give Data Explorer a shot. We think you will like it. If you have any questions or feedback, we would love to hear from you. You can reach us via the Data Explorer Forum.

From Data to Insight & Impact: Getting Public Data with Data Explorer and Building an Excel Power View & GeoFlow

2013-05-14T16:00:00Z

Today, we continue our From Data to Insight & Impact blog and video series from Microsoft Finance Director Marc Reguera. The inspiration for this demonstration stems from Marc’s diverse travels and his desire to better understand the crime rate of cities throughout the United States.

In this example, Marc uses Data Explorer to find public data, import it & shape it all within Excel 2013. He then uses Power View to create a 2D visualization, and GeoFlow to create a 3D visualization of the data. Watch below as Marc takes you through this quick and easy demonstration of how you can use these powerful tools to gain insights from publicly available data. Like Marc, in just minutes, you will see which cities have the highest & lowest robbery rates per capita using Power View, Data Explorer, and GeoFlow.

Enjoy Marc’s demonstration and then see how simple and easy these Excel add-ins are to use yourself. The downloads are available here:

Congrats to the Winners of the So You Think You Know Excel Sweepstakes

2013-04-24T16:00:00Z

Last month, we asked, “What do you Excel at?” and after thousands of you took the ‘So You Think You Know Excel’ Challenge on Facebook, we have the answer! You excel at rising to a challenge and digging both feet in to have some fun and learn a little at the same time!

We were very excited to see participants from all over the world play and invite their friends to take this Excel 2013 trivia challenge. And while the scores covered the spectrum, we hope that the sneak peek you received at Excel 2013 through this fun challenge got you excited for the powerful simplicity of our new Excel offering. If you didn’t have a chance to take the Challenge, you can still join in on the fun here. Or, you can cut right to the chase and download the preview of Excel 2013 here. Either way, we know you’ll be impressed with what you see!

Finally, we’ve saved the best for last – we are pleased to announce the winners of the ‘So You Think You Know Excel’ Sweepstakes that many of our players entered after taking the challenge.

Please join us in congratulating these lucky winners in the ‘So You Think You Know Excel’ Sweepstakes!

GRAND PRIZE WINNERS (KINECT FOR XBOX 360 PRIZE PACK)

J. Romero (Modesto, CA)
J. Ort (Buffalo, NY)

PRIZE WINNERS (MICROSOFT BIG DATA/SQL SERVER SWAG PACK, ARV US$150)

A. Kravchik (Raanana, Israel)
A. Budai (Szekesfehervar, Hungary)
R. Black, Jr. (Winter Park, FL)
B. Ridings (Cape Girardeau, MO)
A. Rivera (El Paso, TX)
S. Garber (Madison, AL)
D. Harrington (Enfield, Middx)
A. Guennigsman (Howard Lake, MN)
K. Benepal (Slough, Berkshire)
R. James (Brighton, Sussex)

Get the Pulse on the PASS Business Analytics Conference

2013-04-10T18:00:00Z

Great things are taking place at PASS Business Analytics Conference (PASS BAC) this week in Chicago, and you can keep up with it all! In fact, this new conference is the place to be for business intelligence and data professionals to connect, with sessions featuring dynamic content from five major tracks including:

Data Analytics and Visualization

Advanced Analytics and Insights

Information Delivery and Collaboration

Big Data Innovations and Integration

Strategy and Architecture

Keynote speakers include Dr. Steven Levitt, professor and bestselling author of Freakonomics and SuperFreakonomics, Kamal Hathi, Partner Director, Microsoft, and Amir Netz, Technical Fellow, Microsoft. They are joined by a host of well-known industry leaders and expert community speakers, and together, they are poised to make PASS BAC the perfect place for data and business analysts, data scientists, architects and business analytics/business intelligence practitioners to gather and share about important business topics that are changing the way business is conducted today.

Whether you are at the conference, or you’re just interested in following the event online, you can keep up on the latest and greatest happenings taking place throughout week by:

Visiting the SQL Server blog each day this week to read up on the latest news and announcements coming from the PASS Business Analytics Conference. On the blog, we’ll be spotlighting a special three part series on The Art of Analytics, where we’ll showcase Microsoft Business Intelligence strategies and features, in addition to a few customer spotlights that leverage Microsoft tools.

Following @SQLServer on Twitter for conference updates and highlights from key sessions.

New Article Summarizes Support for Mobile Devices Across Business Intelligence (BI) Technologies

2013-04-09T20:00:00Z

The newly published article, Mobile Support Across Business Intelligence (BI) Technologies, outlines the mobile devices and mobile operating systems you can use with SharePoint 2010, SharePoint 2013, SQL Server Reporting Services, and Office 365. It includes a summary of feature support, as well as requirements and limitations.

We’ll continue to update the article, as support for mobile devices expands to other Microsoft BI technologies. Mobile Support Across Business Intelligence (BI) Technologies is the latest in a series of content that focuses on mobile BI. Here are additional videos and articles to help you work with reports on your mobile devices.

Exciting Times for Business Analytics

2013-04-04T16:00:00Z

With one week to go until the PASS Business Analytics Conference in Chicago, IL, guest blogger and PASS BAC speaker, Marco Russo joins us today to give us his perspective on the dynamic world of business analytics.

Marco Russo (@marcorus) is a Business Intelligence consultant and mentor that has been working with SQL Server Analysis Services since 1999 and with PowerPivot since 2009. Marco regularly write articles on SQLBI and wrote several books about these products, last published are Microsoft SQL Server 2012 Analysis Services: The BISM Tabular Model and Microsoft Excel 2013: Building Data Models with PowerPivot. He achieved the unique SSAS Maestro certification, he is a Microsoft Certified Trainer (MCT) and a Microsoft Most Valuable Professional (MVP).

We live in exciting times from the point of view of data analysis. Nowadays, the problem is no longer how to find the raw data, but how to handle the pressure of data coming in from so many places. At the end of the day, the goal is always the same, extracting useful knowledge from data. This has been the goal of Business Intelligence (BI) since 1958, when Hans Peter Luhn used this term for the first time. In more than 50 years, the technology evolved, increasing the manageable amount of data and lowering the related costs. However, it always required professionals who were able to create and refresh the data model, empowering end users with canned reports and data navigation tools. A common issue in this process was the gap between people who knew the business and BI developers. In the best case, this gap produced long development times. In the worst case, it led to project failure.

Today, this gap can be drastically reduced. Thanks to Self-Service BI products, if you know your business, you can create a data model without having to ask for a BI professional consultancy. However, a common mistake is thinking that these new technologies are meant to kill the traditional data warehouse approach, i.e. the Corporate BI. In reality, self-service BI is an opportunity to improve the ROI of a properly built data warehouse, even if an optimal architecture might require some adaptation to the data warehouse schema, in order to simplify and optimize the extraction of data for self-service BI purposes.

When we use the term Business Analytics we refer to the exploration and investigation of data. This requires the usage of statistical methods and data visualization, and oftentimes needs adapting the data model. The PASS Business Analytics Conference (PASS BAC) in Chicago this April is the right place to go to learn more about tools, methodology and best practices in the Business Analytics area.

I will speak at the PASS BAC in two sessions about the state of the art in self-service BI.

In the session Self-Service Data Modeling I discuss the challenges of creating a proper data model by using Excel 2013 and PowerPivot. Thanks to the DAX language, it is possible to apply few transformations to the raw data. However, preliminary data preparation might be necessary and users that do not have a knowledge of ETL and SQL need other tools and techniques to adapt their raw data to the required model. I will show how to solve these issues in common scenarios, using tools designed for end users and not for BI developers.

The second session, Modern Data Warehousing Strategy, is about changes in the Data Warehouse architecture and modeling required to face the challenges of the self-service approach and the new demand caused by Big Data technologies such as Hadoop (HDInsight). A good data warehouse is still the optimal starting point for any analysis, but we need to update our strategy for data warehouse implementation to fit the requirements of this new era. What kind of data modeling should we use for the data warehouse? What is the role of data marts? Do technologies such as PowerPivot or Analysis Services Tabular affect the way we should model our data? Do columnstore indexes remove the need for an analytical server like Analysis Services? We will discuss these and other questions, offering an updated approach to the data warehouse modeling methodology.

Look at the many other sessions available in the program and join us in Chicago at the PASS Business Analytics Conference!

From Data to Insight & Impact: Showing Africa’s Progress with Power View and PPI by Microsoft

2013-04-03T16:00:00Z

Today, Microsoft Finance Director Marc Reguera returns with a new installment in the “From Data to Insight & Impact” blog and video series. After reading a special report about Africa’s progress in the March 2^nd edition of The Economist, Marc wanted to review that progress by using Power View in combination with PPI by Microsoft.

In this example, Marc walks you through some informative data visualizations that depict progress in Africa by examining a number of factors from each African nation, such as population, GDP, HIV deaths, and number of mobile phones. He interacts with these visualizations by simply touching the screen of the stunning piece of hardware that is PPI by Microsoft.

Watch on as Marc shows you how to get from data to powerful business insights and impact using Power View. You can hear more from Marc in person at the upcoming PASS Business Analytics Conference, from April 10-12 in Chicago, where he will discuss From Data to Insight and Impact – Views from Microsoft Finance.

You can also view more videos in the From Data to Insight & Impact series here:

- Building an Excel Power View from a PDF Document -http://bit.ly/WBsMq1
- Building a Manufacturing Summary with Power View -http://bit.ly/XSEOau
- Using Power View to Review Manufacturing Performance -http://bit.ly/VR2Lkl
- Building a Sales Summary with Power View - http://bit.ly/TsKQAo
- Using Power View to Review Sales Performance -http://bit.ly/YmnS0r

Gartner BI and Analytics Summit Recap: Faster Business Insights for All

2013-03-25T16:00:00Z

Thank you to the nearly 550 attendees from the 2013 Gartner BI and Analytics Summit who visited the Microsoft booth and joined our “Insights for Everyone from Any Data, Any Size, Anywhere” session at the event. Those who visited had a chance to see our Big Data and BI platforms in action, specifically Excel 2013 (with PowerPivot and Power View), Data Explorer and HDInsight. In our Solution Provider Session, the audience joined in to view a new perspective on the journey from data to insight. Together, we explored what Microsoft is doing to make the data platform and tools more simple and agile to accelerate time to insight so you can take action on your data faster than ever.

Herain Oberoi and Marc Reguera’s presentation sparked a lot of interest from attendees – but also delivered on the notion of having fun with data. Through a fun and fast demonstration, Marc showed attendees how PowerPivot and Power View have dramatically changed his job in finance – making it not only more efficient – but more enjoyable.

You can view additional photos from the event on the Microsoft Business Intelligence Facebook page here.

To learn more about Microsoft and our BI solution, please visit www.microsoft.com/bi. To view the demos from our Solution Provider Session and demos from the booth, please view the following videos on Manufacturing and Sales. Additional videos from this series can be found on the Microsoft Business Intelligence YouTube channel including:

- Building an Excel Power View from a PDF Document
- Building a Manufacturing Summary with Power View
- Using Power View to Review Manufacturing Performance
- Building a Sales Summary with Power View
- Using Power View to Review Sales Performance