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# Pivot Tables and Beyond Data Analysis in Excel 2013 - Course Technology Computing Conference

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Pivot Tables and Beyond Data Analysis in Excel 2013 - Course Technology Computing Conference

Presenter: Patrick Carey, Cengage Learning Author

Excel is sometimes called the most popular "database" in the world, not because it's a database but because it makes data so accessible that users often turn to spreadsheets for data entry. Yet for all that, Excel's tools for data analysis and modeling remain largely untapped by the average user. In this, pivot tables may be the most powerful and least utilized tool for data exploration. In this presentation we'll examine some of the new enhancements to pivot tables introduced in Excel 2013. We'll examine how to set up relationships using the Excel Data Model to summarize information across multiple data tables. And then we'll go beyond, exploring the data modeling and data visualizing tools provided by the PowerPivot and Power View add-ins, interpreting data not just numerically but through visual imagery, charts, and interactive maps.

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### Pivot Tables and Beyond Data Analysis in Excel 2013 - Course Technology Computing Conference

1. 1. Pivot Tables and Beyond Patrick Carey
2. 2. Daily Sales from GJC Transactions from Green Jersey Cycles from 2010 through 2013.
3. 3. Products Table Table displaying a selection of sample products sold by GJC
4. 4. Stores Table Table displaying information about 20 brick & mortar stores and the GJC website
5. 5. Count of Product ID’s The COUNT function of Product ID’s gives an erroneous result since the same Product ID multiple times. We can correct this issue using the DISTINCOUNT function available under the Excel Data Model
6. 6. Using Distinct Count Count of Products ID’s Discrete Count of Product ID’s
7. 7. THE EXCEL DATA MODEL Pivot Tables and Beyond
8. 8. The Excel Data Model The Excel Data Model is attached to the workbook file, providing database tools and more compact and efficient data storage Data Model database
9. 9. Data Model Overview Benefits • Ability to Define Relationships between Multiple Tables • Data Compression • Support for DAX • Interfaces with Dashboarding Tools like Power View Limitations • Built-in Tools Missing from Pivot Table Menus • Inability to Group Fields • Supported only in Excel 2013
10. 10. Adding an Excel Table to the Data Model You can add a table to the Data Model when you generate the Pivot Table, by clicking the Add this to the Data Model checkbox
11. 11. Distinct Count of Product ID’s The DISTINCOUNT function available under the Excel Data Model counts each unique occurrence of a Product ID in the Sales table; from this we know that 212,720 units were sold from 35 different GJC products over the past 4 years.
12. 12. Adding a Second Table To use information from a second table, it has to be added to the Data Model and a relation established between the two tables.
13. 13. Creating a Table Relationship
14. 14. VIEWING THE DATA MODEL WITH POWERPIVOT Pivot Tables and Beyond
15. 15. PowerPivot Features • View, edit, sort and filter data in the PowerPivot grid • Perform Calculations using DAX • Set up table relations with a graphic interface • Set default display formats for data columns • Hide Columns and Tables • Create hierarchies of data columns • Define Key Performance Indicators (KPI’s) • Analyze Big Data containing Millions of Records
16. 16. PowerPivot Data View
17. 17. Importing Database Tables into the Data Model Multiple database tables can be imported into the Data Model. Power Pivot will retain any table relations already defined in the database.
18. 18. Table Relationships within Diagram View Diagram is useful for viewing the complete structure of your data model, including all relationships, hierarchies, and KPI’s. Here Sales Date in the Sales table and Date in the Calendar table are linked
19. 19. Setting Column Sort Order Sorting Months by Month Number Sorting Weekdays by Weekday Number
20. 20. Units Sold by Year and Month It’s important to define the sort order for the Year and Month fields so they are sorted in the correct order.
21. 21. PIVOT TABLE CALCULATIONS USING DAX Pivot Tables and Beyond
22. 22. DAX • Data Analysis Expressions • Calculations based on Tables and Columns • Support for Lookup Tables and Table Relations • Optimized Memory Engine supports Rapid Calculations over Large Columns and Tables
23. 23. PowerPivot Calculations Calculated Columns • Calculation context is row-based • Formulas are applied to an entire column • Appear as a new columns within PowerPivot Data View Calculated Fields • Calculation is based on the PivotTable context • Called measures in PowerPivot 2010 • Appear in the Calculation Area in PowerPivot Data View
24. 24. Calculating Revenue from Each Sale To calculate the revenue from each sales use the Unit Price field round in the Products table using the RELATED function to access the field value Revenue:= [Units Sold]*RELATED([Products[Unit Price])
25. 25. Total Revenue by Month and Year Calculated Columns appear in the list of table columns alongside data fields and can be directly used in any Pivot Table combination we wish.
26. 26. Calculating the Total Store Days GJC is not open every day, so we create the StoreDays calculated field to count up the number days minus the number of holiday dates from the Holidays table. Store Days := COUNT([Calendar[Date]) – COUNT(Holidays[Date])
27. 27. Calculating Average Daily Revenue The average revenue collected day can be inserted as a calculated field using the DAX expression: Daily Revenue := SUM([Revenue])/[Store Days]
28. 28. Daily Revenue from the GJC Stores Overall the store earns an average of about \$7,600 per day for the selected products. Individual stores earn anywhere from \$58 up to more than \$440 per day.
29. 29. KEY PERFORMANCE INDICATORS Pivot Tables and Beyond
30. 30. Defining a KPI against an Absolute Standard KPI’s provide a visual indicator of how a base field performs versus a defined standard. Here we set an absolute value for Daily Revenue with “normal” or ”acceptable” ranging from \$80/day to \$160/day
31. 31. Viewing a Key Performance Indicator High performance values are indicated by a green ball, low performance by a red ball, and values within the target area are highlighted with the yellow ball.
32. 32. KPI’s can Lose their Meaning when the Data is Filtered An absolute standard loses its meaning when the data values filtered. In this almost all stores appear to be underperforming when measured against an absolute standard that should apply only when all product categories are included.
33. 33. Calculate the Number of Brick & Mortar stores We want to compare each Brick & Mortar store to the average daily revenue from the Brick & Mortar stores. We can do this by using a calculated field that calculates the average revenue per store. We start by creating an expression to count the number of Brick & Mortar stores
34. 34. Calculate the Daily Revenue from Brick & Mortar stores Next we calculate the Daily revenue from Brick & Mortar stores only. Note that here and in the Brick Count field we use the ALL() function to ensure that we always count over all [City State], [City], and [StoreID] fields regardless of the Pivot Table layout.
35. 35. Calculating Average Revenue per Brick Store The Average Daily Revenue from the Brick Stores is calculated using the expression: Brick Revenue Average := [Brick Revenue]/[Brick Count]
36. 36. Creating a KPI using a Calculated Field We now revise the Daily Revenue KPI so that it uses the Brick Revenue Average calculated field. Low values are cut off at 40% of the target, medium values at cut off at 80% of the target value.
37. 37. Comparing Stores Under Different Filters All Brick & Mortar Stores Brick & Mortar Stores by Market
38. 38. Calculating the Maximum Daily Revenue DAX supports a wide variety of queries and calculations. For example the above expression uses the SUMMARIZE function to return the maximum revenue generated on any particular day.
39. 39. Viewing Maximum Revenue The greatest one-day intake for the company was about \$22,000 from all sources. The greatest one-day result for the website was a little more than \$10,000; from the combined 20 Brick & Mortar stores the greatest one-day take was more than \$14,000.
40. 40. MANAGING YOUR FIELDS AND TABLES Pivot Tables and Beyond
41. 41. Defining a Hierarchy Hierachies are used to group fields that have an inherit ordering. For example the location hierarchy orders stores from Region  State  City
42. 42. Viewing a Hierarchy Hierarchies can be added to a Pivot Table, appearing as nested field. To drill down into the hierarchy, click the [+] box.
43. 43. Hiding Columns You can clean up the clutter of unwanted fields by hiding them from the user; showing only those fields which are directly involved in the data analysis. You can also hide entire tables, such as the Holidays table, that contain only lookup values.
44. 44. Creating Perspectives You can reduce clutter in the Power Pivot view of the Data Model by creating a perspectives, specifying which tables and fields are visible to the Power Pivot user. However, modifying the perspective does not affect the table/field list in Excel.
45. 45. ANALYZING YOUR DATA WITH POWER VIEW Pivot Tables and Beyond
46. 46. Sales Report This Power View report tracks sales for different products category by region with each report element dynamically linked to the others.
47. 47. Clothing Sales Report GJC has been selling an increasing percentage of cycling clothing for women over the past four years as this Power View report demonstrates.
48. 48. Brick & Mortar Stores Report This Power View report examines the Brick & Mortar stores, providing contact information, map location, and the daily revenue.
49. 49. Customer Report This Power View report shows the location of Green Jersey Cycling customers and the products they bought.
50. 50. Contact Information Patrick Carey Carey Associates 8502 Miller Road Verona, WI 53593 (608) 832 – 6313 patrick1@careys.com