All of his novels and stories have interconnecting threads that link them into a larger universe, spanning space and time. Some of the links are obvious, while others are very subtle. Some of the events in the stories are based on real life, while others are pure fiction. The distinction between fact and fiction is up to the reader. Having studied witchcraft many years earlier, it had always been in my mind. As such, I saw the potential for violence in anyone.
After being treated rather rudely by the band of bank beauties, I decided to write a story to explain their odd and overbearing personalities. Using newspaper stories and personal experiences, I settled on baby sacrifices and Satanism. While the personalities and physical attributes are based upon real people I knew at the time, their involvement is this story is purely fiction.
About Publish Join Sign In. Readers Benefits of registering Where are my ebooks? Ask it above. Together, these five documents provide the foundation for the Prospectus package to be presented to executive management for project approval. Do not be alarmed if you are not a Project Manager, these tools can be used in other realms, limited only by your imagination. At the very least, this exercise will illustrate the capabilities of Excel and teach some valuable techniques in the process.
For clarity, the key benefits of using Power Pivot vs. 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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The resulting dataset is two million rows. This functionality used to be called PowerQuery, but as of Excel and , 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. 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. Another key advantage of Power Pivot for Excel is the ability to manipulate and work with large datasets to draw relevant conclusions and analysis. 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. This creates a new measure that sums across the Amount column. Similarly, I can type another measure in the cell below. As financial analysts using Excel, we become adept at using convoluted formulas to bend the technology to our will.
However, by using Power Pivot, we can throw much of that out the window. To demonstrate this functionality, I created a small reference table in which I assigned each Category to a Type. 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. Besides measures, there is another type of calculation: calculated columns. Excel users will be comfortable writing these formulas, as they are very similar to writing formulas in data tables. I have created a new calculated column below see Creating a Calculated Column below which sorts the Accounting Data table by Amount.
Or, we can create a chart of Average Sales for each day of the week using the new Calendar table.
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While this chart looks simple, it is impressive that it took less than ten seconds to create a consolidation over two million rows of data, without adding a new column to the sales data. While being able to perform all these consolidated reporting scenarios, we can always still drill down into the individual line items. We retain our highly granular data.
So far, most of the analysis I have shown are relatively straightforward calculations. Now, I want to demonstrate the some of the more advanced capabilities of this platform. Often, when we examine financial results, we want to compare it to a comparable timeframe from the previous year.
Power Pivot has some built-in time intelligence functions. For example, adding just two measures above to the Accounting Data table in Power Pivot enables me to produce the following PivotTable in a few clicks. As a financial analyst, one problem I often have to solve is that of mismatched granularities. To further this mismatch, we will prepare a quarterly budget, even through the sales data is daily. 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.
Further, we can define new measures that calculate the variance between actual sales and budgeted sales as below:. I created the two measures below:. 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. Another benefit is that file sizes shrink.
Building a Spreadsheet Forecasting Model
The original file size was 91MB, and now it is under 4MB. 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.
Power Pivot models can also be scalable to the entire enterprise.
Power Pivot for Excel Tutorial: Top Use Cases and Examples
At this point, you may not want thirty different users refreshing the model or making changes. All the tables and relationships are retained, but now you can control the refresh frequency, assign roles e. The result is that your users could then access the deployed Tabular model with a small workbook, but not have access to the formulas and measures. 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:.
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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.