In this article, I will show you how to use Power Pivot basics to overcome common Excel issues and take a look at additional key advantages of the software using some examples. This Power Pivot tutorial is meant to serve as a guide for what you can achieve with this tool, and at the end, it will explore some sample use-cases where Power Pivot often proves invaluable.
As previously alluded to, one of the major limitations of Excel pertains to working with extremely large datasets. Fortunately for us, Excel can now load well over the one-million row limit directly into Power Pivot.
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Using the Data tab on the ribbon, I created a New Query from the CSV file (see Creating a New Query below). This functionality used to be called PowerQuery, but as of Excel 2016 and 365, was more tightly integrated into the Data tab of Excel.
From a blank workbook in Excel to loading all two million rows into Power Pivot, it took about one minute! Notice that I was able to perform some light data formatting by promoting the first row to become the column names. Over the past few years, the Power Query functionality has vastly improved from an Excel add-in to a tightly integrated part of the Data tab on the toolbar. Power Query can pivot, flatten, cleanse, and shape your data through its suite of options and its own language, M.
One of the other key benefits of Power Pivot for Excel is the ability to easily import data from multiple sources. Previously, many of us created multiple worksheets for our various data sources. Often, this process involved writing VBA code and copy/pasting from these disparate sources. Fortunately for us, though, Power Pivot allows you to import data from different data sources directly into Excel without having to run into the issues mentioned above.
Further, multiple data sources can be combined either in the Query function or in the Power Pivot window to integrate data. For example, you can pull production-cost data from an Excel workbook and actual sales results from SQL server through the Query into Power Pivot. From there, you can combine the two datasets by matching production-batch numbers to produce per-unit gross margins.
Excel junkies will no doubt agree that PivotTables are both one of the most useful, and at the same time, one of the most frustrating tasks we perform. Frustrating particularly when it comes to working with larger data sets. Fortunately, Power Pivot for Excel allows us to easily and quickly create PivotTables when working with larger sets of data.
In the image below, entitled Creating Measures, notice how the Power Pivot window is separated into two panes. The top pane has the data, and the bottom pane houses the measures. A measure is a calculation that is performed across the entire dataset. I have entered a measure by typing in the highlighted cell.
As financial analysts using Excel, we become adept at using convoluted formulas to bend the technology to our will. We master VLOOKUP, SUMIF, and even the dreaded INDEX(MATCH()). However, by using Power Pivot, we can throw much of that out the window.
I also created a date table to use with our dataset (see Creating a Date Table below). Power Pivot for Excel makes it easy to create a date table quickly in order to consolidate by months, quarters, and days of the week. The user can also create a more custom date table to analyze by weeks, fiscal years, or any organization-specific groupings.
With Power Pivot for Excel, this inconsistency is easily solved. By creating two additional reference tables, or dimension tables in database nomenclature, we can now create the appropriate relationships to analyze our actual sales against the budgeted amounts.
Notice how the calculations are performed at both the category and seasonal type level. I love how quickly and effortlessly these calculations are performed on such a large dataset. These are just a few examples of the elegance and sheer computational power of Power Pivot.
How does this happen? Power Pivot uses the xVelocity engine to compress the data. In simple terms, the data is stored in columns rather than rows. This storage method allows the computer to compress duplicate values. In our example dataset, there are only four regions that are repeated over all two million rows. Power Pivot for Excel can more efficiently store this data. The result is that for data that have many repeating values, it costs much less to store this data.
One thing to note is that I used whole-dollar amounts in this sample dataset. If I had included two decimal points to reflect cents, the compression effect would lessen to a still-impressive 80% of the original file size.
One of the constant requests of my clients is that I create reporting that conforms to a strictly defined layout. I have clients that request specific column widths, RGB color codes, and pre-defined font names and sizes. Consider the following dashboard:
How do we populate the sales numbers without generating PivotTables if all of our sales are housed with Power Pivot for Excel? Using CUBE formulas! We can write CUBE formulas within any Excel cell and it will perform the calculation using the Power Pivot model we have already constructed.
The first part of the formula, highlighted in yellow, refers to the name of the Power Pivot model. In general, it is usually ThisWorkbookDataModel for newer versions of Power Pivot for Excel. The portion in green defines that we want to use the measure Total Sales. The part in blue instructs Excel to filter for only rows that have a Sales Date with a year equal to 2016.
Behind the scenes, Power Pivot has constructed an Online Analytical Processing (OLAP) cube with the data, calculated columns, and measures. This design allows the Excel user to then access the data by fetching directly with the CUBE functions. Using CUBE formulas, I have been able to construct full financial statements that conform to predefined layouts. This capability is one of the highlights of using Power Pivot for Excel for financial analysis.
Another advantage of Power Pivot for Excel is that you can quickly take any Power Pivot workbook you build and quickly convert it into a Power BI model. By importing the Excel workbook directly into the Power BI Desktop app or Power BI Online, you can analyze, visualize, and share your data with anyone in your organization. Essentially, Power BI is Power Pivot, PowerQuery, and SharePoint all rolled into one. Below, I have created a dashboard by importing the previous Power Pivot for Excel workbook into the Power BI desktop application. Notice how interactive the interface is:
One great thing about Power BI is the Natural Language Q&A. To demonstrate, I uploaded the Power BI model onto my online Power BI account. From the website, I can ask questions and Power BI constructs the appropriate analysis as I type:
Another benefit of Power BI is that the developers at Microsoft are constantly releasing updates to it. New features, many user-requested, are pushed out monthly. Best of all, it is a seamless transition from Power Pivot for Excel. So, the time you invested learning the DAX formulas can be deployed in Power BI! For the analyst who needs to share his analysis to many users on varying devices, Power BI may be worth exploring.
Another best practice is to remember that Power Pivot is not Excel. In Excel, we are accustomed to creating calculations by constantly expanding our worksheets to the right. Power Pivot for Excel most efficiently processes the data if we limit this desire for manifest destiny. Instead of continuously creating calculated columns to the right of your data, learn to write measures in the bottom pane. This habit will ensure smaller file sizes and quicker computations.
Finally, I would suggest using plain-English names for measures. This one took me a long time to adopt. I spent the first few years making up names like SumExpPctTotal, but once other people began to use the same workbooks, I had a lot of explaining to do. Now, when I start a new workbook, I use measure names like Expense Line Item as Percent of Total Expenses. While the name is longer, it is much easier for someone else to use.
With the use of CUBE functions, Power Pivot for Excel seamlessly blends into your existing Excel workbooks. The computational efficiency gain cannot be overlooked. Assuming a 20% faster processing speed, which is conservative, the financial analyst that spends six hours a day within Excel can save 300 hours a year! 152ee80cbc
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