Building Self-Service BI Solutions with Power Query. Written By: Devin

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1 Building Self-Service BI Solutions with Power Query Written By: Devin

2 CONTENTS PAGE 3 PAGE 4 PAGE 5 PAGE 6 PAGE 7 PAGE 8 PAGE 9 PAGE 11 PAGE 17 PAGE 20 PAGE 23 PAGE 24 INTRODUCTION WHAT IS POWER QUERY WHEN USE POWER QUERY WHO SHOULD USE POWER QUERY POWER QUERY DEMONSTRATION: MAKING SENSE OF CENSUS DATA EXTRACTING DATA TRANSFORMING DATA ADDING ADDITIONAL DATA SOURCES MERGING DATA VISUALIZING DATA SELF-SERVICE, NOT SELF-TAUGHT SUMMARY

3 Introduction INTRODUCTION Demand for data has never been higher. Keeping up with the business needs has become increasingly difficult. Traditional ways of getting the business the data they need often has long planning cycles which make it difficult to adjust when the requirements or needs change. This demand is what launched many new Self-Service technologies, which allow the business to design their own solutions with little need to involve IT. Having this ability to create data solutions on their own enables the business to be much more agile in their decision making process. Business Intelligence has long been the way to visualize how a business can truly be more successful through the process of making decisions. Business Intelligence is all about taking data and transforming it into something meaningful for business purposes. The rise of Self- Service technologies is one of the most significant developments to affect Business Intelligence since the technology s creation. Through Self-Service BI, business units can personalize Business Intelligence to their needs and solve problems at a much faster rate than any traditional BI solution. This is why businesses are looking to Self-Service BI to solve the smaller, but no less significant, problems that individual departments need addressed. Organizations should not see Self-Service BI as an opportunity to completely disengage IT. Self-Service BI solutions may be great for shorter development cycles and getting feedback from the business quicker, but it is not a cure all to solving problems. Corporate, or IT driven BI, will continue to be a better solution around data quality, scalability, and providing a single version of the truth. Imagine a scenario where multiple departments have implemented Self-Service solutions but they all give different answers on the same question. Clearly in this situation, Corporate BI would be better suited to create a Data Warehouse providing the business with a single version of the truth. The lesson here is use the right tool for the job. Analyze the problem you are trying to solve and then determine if it is better solved with Corporate or Self-Service BI. The major components of Business Intelligence is data extraction and manipulation. With traditional Corporate BI this can done through tools like SSIS (SQL Server Integration Services), which is an enterprise ETL (Extract Transform Load) tool. However, the goal of this white paper is to focus on using and understanding one of Microsoft s latest Self-Service BI tools called Power Query. PAGE 3

4 What is Power Query WHAT IS POWER QUERY Power Query is a Self-Service data extraction tool that is a free add-in for Excel 2010 or higher. This allows users that are already comfortable with Excel a smaller learning curve to start enjoying it. Power Query has a vast array of options that it can use as data sources. The types of sources that can be used are: Web page Excel or CSV file XML file Text file Folder SQL Server database Windows Azure SQL Database Access database Oracle database IBM DB2 database MySQL database SharePoint List OData feed Hadoop Distributed File System (HDFS) Windows Azure Marketplace Active Directory Facebook This paper will show you how simple yet powerful Power Query really is by showing you an example of solving a problem that would be fairly complex using traditional ETL tools like SSIS but made simple with Power Query. By no means is Power Query going to replace SSIS, at least not in the current form, but it can be used for solving quick data extraction challenges. Integration Services will still be used for things like complex Data Warehouse loads. You can download the tool from the following URL: PAGE 4

5 When use Power Query WHEN USE POWER QUERY In the introduction of this paper it was discussed that Self-Service BI tools cannot solve all your business problems. So when should you choose to use a Self-Service tool like Power Query? There is no blanket answer to this question. As new initiatives come up they should be analyzed on a case by case basis to determine if Power Query is a good fit or not. There are many factors that you can consider, so if you find it difficult to make a choice than make a list of the challenges you are trying to get past and mark each as better solved with Self-Service BI or Corporate BI. This table shows a basic example of a decision matrix to help guide you through making this decision. NEED IMPORTANCE SCORE (1-5) SELF-SERVICE BI CORPORATE BI DATA QUALITY 4 X SHORT DEVELOPMENT CYCLE 5 X SINGLE VERSION OF TRUTH 2 X USER DEVELOPMENT 5 X SCALABILITY 3 X SCORE 10 9 For any given solution simply adjust the importance score. For example, in some projects scalability may be the most important problem to solve while other projects it may be the least important. This demonstrates why this score may change from project to project. PAGE 5

6 Who should use Power Query WHO SHOULD USE POWER QUERY Within Self-Service BI there are several roles that individuals may identify with. These are by no means formal roles but often business users will align their expertise to one of the following four: Data Wrangler, Data Steward, Power Analyst, or Collaborative User. Business users may align with just one single role or take on all four but it is important that to understand what each involve. These roles are summarized in the image below. As you may guess, Power Query is primarily facilitated by the Data Wrangler role. This role specializes in bringing together and creating meaning out of data. They often pull together disparate data sources and create relationships where they may have not previously existed. They must know each data source well or at least know where to get the right answers to questions when they arise. They can give details such as where data resides, how to access it and how frequently it is refreshed. The data wrangler has one of the most important roles because everything they design impacts the subsequent roles. PAGE 6

7 POWER QUERY DEMONSTRATION: MAKING SENSE OF CENSUS DATA One of the more fascinating things about Power Query is its ability to pull in data from unusual data sources. This give you the ability to tap into data sources that were previously thought of as difficult to analyze. It also makes public data sources much more of an asset when combined with existing data sources. This white paper will use the public data source of United States Census data for targeting high income counties in the state of Florida. Imagine you work for a retail company that is looking to open a new store front in Florida. If you could bring this pubic data source in for the analysis of that choice you could make a much more informed decision on choosing a new retail store location. This example will walk you through using Power Query to do the Following: Extracting Data Transforming Data Adding Additional Data Sources Merging Data Visualizing Data (Using Power View) To follow this example you will need the following functionality on your machine: Internet Access Excel Professional 2010 or higher Power Query add-in Power Pivot add-in (Already installed if using Excel 2013) If you are using Excel 2010 you must have SharePoint 2010 SP1 or higher with SQL Server 2012 for the Visualizing Data section. If you are using Excel 2013 all functionality is built-in. PAGE 7

Extracting Data For our example we ll be pulling census data which can be found on the web. Use the following step-by-step instructions to complete the example: 1. Launch Excel 20

8 Extracting Data For our example we ll be pulling census data which can be found on the web. Use the following step-by-step instructions to complete the example: 1. Launch Excel 2010 or higher. 2. Select the Power Query tab. 3. Click From Web under the Get External Data part of the Office Ribbon. 4. Use the URL then click OK. 5. This will launch the Query Editor query with a sample of the data that will be used during the data transformation process. 6. Now rename the query in the top left of the screen by double-clicking where it says Query1. Name the query Demographics. PAGE 8

Transforming Data Now that the data is inside the Power Query window you will need to apply transformations to the data to make it usable for reporting.

Right-click on the Column header named Column1 and select Split Column -> By Delimiter 2.

9 Transforming Data Now that the data is inside the Power Query window you will need to apply transformations to the data to make it usable for reporting. Use the following step-by-step directions to manipulate the data to fit our needs: 1. Right-click on the Column header named Column1 and select Split Column -> By Delimiter 2. In the Split a column by delimiter dialog keep the default settings of Comma delimiter and At each occurrence of the delimiter then click OK. 3. With the columns now split you can clearly see that the first row of the data is column headers. Rightclick on any one of the column headers and select Use First Row As Headers. PAGE 9

The new Value column is full of metrics that we d like to eventually aggregate on and therefore the data type must be changed to a number.

10 4. The columns here are not properly organized for the other queries we ll pull in later. Select all the columns except fips then right-click and select Unpivot Columns. 5. The new Value column is full of metrics that we d like to eventually aggregate on and therefore the data type must be changed to a number. Right-click on the Values column and select Change Type -> Number. 6. Once this is complete click Done. This will load the data into Excel. This is quickly demonstrates some basic transformations in Power Query. Next we ll pull in some additional data sources to help round out the analysis. PAGE 10

11 Adding Additional Data Sources To make this a more functional example let s add a couple more data sources and then finally merge them together. Use the following steps to add in two new data source: 1. Select the Power Query tab. 2. Click From Web under the Get External Data part of the Office Ribbon. 3. Use the URL then click OK. 4. This will launch the Query Editor query with a sample of the data that will be used during the data transformation process. 5. Now rename the query in the top left of the screen by double-clicking where it says Query1. Name the query Data Dictionary. 6. This file is a fixed width file so when we split the columns it will be based on the number of characters. Right-click on the Column header named Column1 and select Split Column ->By Number of Characters 7. Change the Number of characters property to 9 and change the Split property to At the left-most delimiter. Click OK. PAGE 11

12 8. Right-click on the Column header named Column1.2 and select Split Column ->By Number of Characters 9. Change the Number of characters property to 106 and change the Split property to At the left-most delimiter. Click OK. 10. We now have two columns that provide a data dictionary for our first file we loaded. The Column1.2.2 has data that we re not concerned with for this example so remove it by right-clicking on it and selecting Remove. 11. Just like the previous file our column headers are in the first row of the data so right-click on any of the column headers and Use First Row as Headers. 12. Remove extra spaces that appear at the end of each column by selecting both columns and right-clicking. Select Transform -> Trim to remove the extra spaces. PAGE 12

13 13. Right-click on the Data_Item column and select Rename. Rename the column Attribute. 14. Next, right-click on the Item_Description column and select Rename. Rename the column Description. 15. Click Done. 16. Let s add in one more data source now. Select the Power Query tab. 17. Click From Web under the Get External Data part of the Office Ribbon. 18. Use the URL then click OK. 19. This will launch the Query Editor query with a sample of the data that will be used during the data transformation process. 20. Now rename the query in the top left of the screen by double-clicking where it says Query1. Name the query County. 21. Again we must deal with a file with delimiters. Right-click on the Column1 header and select Split Column ->By Delimiter. 22. Change the delimiter to Space and change the Split property to At the leftmost delimiter. Click OK. PAGE 13

23. Right-click on the Column1.2 header and select Split Column ->By Delimiter. 24. Leave the default delimiter as Comma and change the Split property to At the right-most delimiter. Click OK.

26. Next to remove the state data start by right-clicking on Column1.2.2 and select Insert Column ->Custom. 27. Add the following code to the Custom Column Formula box: if [Column1.2.2] = null then [Column1.

14 23. Right-click on the Column1.2 header and select Split Column ->By Delimiter. 24. Leave the default delimiter as Comma and change the Split property to At the right-most delimiter. Click OK. 25. You will notice this not only includes county data but also states and United States. Let s start by removing the United States value. Select the down arrow next to Column1.2.1 and uncheck UNITED STATES from the filter list then click OK. 26. Next to remove the state data start by right-clicking on Column1.2.2 and select Insert Column ->Custom. 27. Add the following code to the Custom Column Formula box: if [Column1.2.2] = null then [Column1.2.1] else null. Click OK. 28. Click on the fx button in the formula bar to create a custom function. PAGE 14

Select the down arrow next to the column named Column1.2.2 and uncheck (null) values.

15 29. Use the following custom formula to fill in the state name down each corresponding row: = Table.FillDown(InsertedCustom,"Custom") 30. Select the down arrow next to the column named Column1.2.2 and uncheck (null) values. Then click OK. 31. Right-click on the column named Column1.2.2 and select Remove. It will no longer be needed. PAGE 15

32. To ensure all the data has the same casing right-click on the column named Custom and select Transform ->Capitalize Each Word. 33. Finally, let s fix the column names.

16 32. To ensure all the data has the same casing right-click on the column named Custom and select Transform ->Capitalize Each Word. 33. Finally, let s fix the column names. Right-click on the column named Column1.1 and select Rename. Set the new column name to FIPS. 34. Right-click on the column named Column1.2.1 and select Rename. Set the new column name to County. 35. Right-click on the column named Custom and select Rename. Set the new column name to State. 36. Click Done to import this query to Excel. PAGE 16

Merging Data Now that all the data sources have been extracted and transformed separately we must merge them together in order to report on each dataset at once.

17 Merging Data Now that all the data sources have been extracted and transformed separately we must merge them together in order to report on each dataset at once. Finally we will load the resulting table into a Power Pivot model. 1. Navigate to the Power Query tab and select Merge to start combining the queries. 2. Select the Demographics table as the primary table and Data Dictionary as the table to be merged. 3. Click the Attribute column in both tables to simulate a join column. 4. You may be prompted to select a privacy level. If so change the privacy level to Public then click Save. This will return you back to the Merge dialog. PAGE 17

18 Merging Data 5. Notice the matched rows on the bottom that identifies the number of rows that match between each query. While we have an exact match in this example it is not mandatory that the rows match in each table. Click OK. 6. This will launch the Query Editor again. Power Query may automatically convert the fips column to a number so right-click on the fips column and change the select Change Type ->Text. 7. Select the expand button next to the column named NewColumn and select Description. Click OK. 8. Click Done. 9. Navigate back to the Power Query tab and select Merge again. 10. Select Merge1 as the primary table and County as the merge table 11. Click the fips column from the Merge1 table and the FIPS column from the County table to simulate a join column. 12. You may be prompted again to select a privacy level. If so change the privacy level to Public then click Save. This will return you back to the Merge dialog. 13. Click OK in the Merge dialog. 14. This will launch the Query Editor again. Select the expand button next to the column named NewColumn and uncheck FIPS. Click OK. PAGE 18

19 Merging Data 15. Click the down arrow next to the column named NewColumn.County and uncheck (null) then click OK. 16. Rename the new columns by right-clicking on NewColumn.Description and selecting Rename. Set the new name to Description. 17. Rename the new columns by right-clicking on NewColumn.County and selecting Rename. Set the new name to County. 18. Rename the new columns by right-clicking on NewColumn.State and selecting Rename. Set the new name to State. 19. Click Done. 20. In the Query Settings window double-click on the query name Merge2 and rename the query County Demographics. 21. Also in the Query Settings window select Load to data model to bring this table into Power Pivot. This button is only available in Excel If you are using Excel 2010 you can do the same action by launching the Power Pivot window then selecting the Design tab and Existing Connections. PAGE 19

Visualizing Data While this paper has focused mainly on the capabilities of Power Query we will end this demonstration by placing our demographic data in a presentation layer.

20 Visualizing Data While this paper has focused mainly on the capabilities of Power Query we will end this demonstration by placing our demographic data in a presentation layer. Using Power View we will place our data points on a map to finally help make the decision which county in Florida would be best for our retail location. 1. Go to the Insert menu in Excel This demonstration can be done in Excel 2010 also after deploying to SharePoint. Power View was added as a built in tool for Excel 2013 so these steps may vary if you are using Excel 2010 with SharePoint. 2. Select the Power View button. 3. By default a table will be created with all the fields selected. Delete the default table by selecting it and hitting delete. 4. Navigate to the Power View Fields pane on the right and expand the County Demographics table. 5. Click and drag the State field into the filter section of the report. Then select Florida so it will be the only state viewed in the report. PAGE 20

21 6. Next select County and Value from the same table. This will place the data into a flatten table on the report design surface 7. To change this to a map click Map in the Design tab under the Switch Visualization section. 8. Resize the map to take up the entire design surface by grabbing a corner and stretching it to fit the screen. PAGE 21

9. You will notice there are several data points on the map that are outside the state of Florida. This is because those states happen to have counties with the same name as Florida counties. 10.

22 9. You will notice there are several data points on the map that are outside the state of Florida. This is because those states happen to have counties with the same name as Florida counties. 10. Zoom in and adjust the map so the only state that is the focus is Florida. 11. In the Power View Fields pane drag the Description column into the Filters pane. 12. Check Median Household Income, as the filter selection and then close the Filters pane. 13. Give the report a title of Florida Household Income to complete the report. This report now give us the answer of which counties in Florida have the highest average income earners. Using the analysis we can make a more informed decision on creating a new retail store. PAGE 22

23 Self-Service, Not Self-Taught SELF-SERVICE, NOT SELF-TAUGHT The biggest misconception about Self-Service BI is that it doesn t require training. Doing a simple web search on Self-Service BI will render articles warning of the low success rate of Self-Service BI or the difficulty users have grasping the concepts of Self-Service BI. What most overlook in these articles is the reason users have difficulty. The truth is many companies do in fact assume because it is Self- Service that it is also self-taught, which couldn t be further from the truth. In fact, training is pivotal to the success of implementing Self-Service BI as a business solution. While many Self-Service BI tools are designed using interfaces that are familiar to users, like Excel, this does not mean they can be learned without formal instructional lessons. Educating users on interacting with the Self-Service development lifecycle is the biggest differentiator in companies that fail and those that succeed with Self-Service BI. Pragmatic Works has training options for you to both develop and hone your Self-Service skills. Our Virtual Training class is a great option for those who can t travel or take a prolonged period of time off work. This 4-day class is designed to get you up to speed using tools easily accessible to Power Users. First you will learn the basics of creating models using Power Pivot. Then using Power Query you will shape additional data that can be found in external data sources. Finally, you will learn the best ways to present your data by building reports using Excel, Power View and Power Map. You will also learn how to make the Self-Service BI solutions you create scalable across your entire enterprise environment. To learn more about this class please visit: In addition we also hold in person Workshops across the country in Microsoft offices that cover the same course material in two full eight hour days. This gives you the opportunity to participate in a live environment while quickly ramping up your skillset. You can see a complete listing of our currently scheduled Workshops here: BusinessAnalytics.aspx PAGE 23

24 Self-Service, Not Self-Taught Summary If Business Intelligence is used to analyze data to make informed decisions about business operations, then Self-Service BI gives those that need it most the ability to create solutions to answer their own questions. Providing proper training to developers of Self-Service solutions will go a long way to being successful in building these solutions. Power Query provides users with the ability to extract data that previously seemed impossible without lengthy IT driven projects. Once extracted, Power Query can easily manipulate data through many simple transformation commands that can be done with ease. Power Query gave us the ability to get new data sources into a familiar Excel environment where we could then visualize the data through tools like Power View, which was demonstrated in this white paper. PAGE 24

Index A, B, C. Rank() function, steps, 199 Cloud services, 2 Comma-separated value (CSV), 27

Index A, B, C. Rank() function, steps, 199 Cloud services, 2 Comma-separated value (CSV), 27 Index A, B, C Calculations, Power Query distinct customers code implementations, 205 duplicate date and customer, 204 group by dialog configuration, 204 objective, 202 output, 205 Query Editor toolbar,