Conventional software works just fine for date based analysis if your fiscal year starts on January 1^st. Excel will happily let you group dates placed in pivot tables as row or column fields into months, quarters or years. You’ll have nice looking and accurate summary tools in no time – if your fiscal dates match standard calendar periods.

But what about the rest of us whose fiscal years don’t line up with that? Or our fiscal periods aren’t all the same number of weeks in duration? How do we get the same results?

To calculate if July 2^nd in a particular year was actually fiscal June or fiscal July, or in which fiscal quarter it belongs, we typically had to resort to all manner of formulas and devices. And if your quarters are comprised of a four period followed by a five week period and then another four week period, well then you’re on your own buddy. Yup, good luck with that.

If this sounds like a challenge that you constantly have to combat, then you’ll love day 20 of the 30 Days to Become a Better Monarch Modeler series, because the topic today is Monarch’s time interval feature. New to v10, this brilliant feature offers us special abilities to handle dates related to both atypical calendar years and fiscal years. I like this feature so much I’d swear that it was devised just to appease me.

By configuring your Monarch models with your specific date needs, you’ll give your models the kind of easy to implement analytical abilities that would make even the most skilled Excel modeler tremble in fear.

Manipulate Space and Time with Monarch

We access the Time Intervals dialog, the master control of this feature, under the Options menu. It shows samples of how various date styles will be displayed in the Table and Summary windows, including:

• Calendar year
• Calendar half-year
• Calendar quarter
• Calendar month
• Calendar week
• Day
• ISO8601 year
• ISO8601 week
• Fiscal year
• Fiscal half-year
• Fiscal quarter
• Fiscal period, and
• Fiscal week

If you’d prefer to display a value in a different you change its “mask”.

If you need to customize the manner by which you define calendar weeks, you do that here. As make changes, Monarch shows you some dates to confirm the impact of your selection.

To make changes to the default fiscal year and its related periods, you do that here too.

You can choose to have your fiscal year consist of 12 calendar months. With this option, Monarch will calculate that your year begins on the first day of any month you choose.

Alternatively, you can define a year that contains 52 or 53 weeks. If you do, Monarch wants to know that your year starts or ends with reference to any day of the year that you specify. We’ll refer to this as “Day 1”.

Once that’s configured, you pick the day of the week that starts your fiscal week. We’ll refer to this as “Week Day”

The last requirement of this section is to tell Monarch that your year:

• Starts on the first Week Day on or after Day 1, or
• Starts on the Week Day closest to Day 1, or
• Ends on the day before the Week Day on or before Day 1, or
• Ends on the day before the Week Day closest to Day 1.

With that set up, Monarch wants to know if your year is divided into four 13-week quarter, or 13 four-week periods.

If you choose the former, you then select from either of a 4-4-5, 4-5-4 or 5-4-4 weekly pattern, and tell Monarch how to handle the extra week in 53 week years.

If you choose the latter, you simply tell Monarch which period gets the extra week in 53 week years.

And that’s it: you’ve defined custom date controls in your Monarch model.

Now it’s time to have some fun.

New Date Functions

Fourteen new functions have been added to Monarch to let us easily access proper date information according to the fancy fiscal year and calendar year customization. They are:

• CalendarWeek
• FirstDay
• FiscalDayOfYear
• FiscalHalfYear
• FiscalPeriod
• FiscalQtr
• FiscalWeek
• FiscalYear
• HalfYear
• IsoDayOfYear
• IsoWeek
• IsoWeekDay
• IsoYear and
• LastDay

I’ll let you refer to the online help for full descriptions, but the intended purpose of each function is pretty apparent just by the name. The ISO functions offer a good compromise between customized calendars and a predefined standard methodology.

As we’ve seen with calculated fields, any time that we utilize Monarch’s functions we introduce all kinds of possibilities, and these new functions add to our creative toolbox nicely.

Using the Intervals in Summaries

To take advantage of the new date powers at your disposal, first add a date field to your summary as a key field. Double-click its name in the key field list to display the dialog box and activate the Matching tab. The last option is labeled Time Interval. Select this and then pick one of the date styles (see the list above).

Monarch will give you a preview of the values it calculates based on your selection on the right side of the dialog.

Once again, Monarch shelters us from all kinds of complexity and gives mere mortals effective and easy to use tools that let us get back to doing what we’re supposed to be doing instead of forcing us to be some kind of expert technology master.

Your Task for Today

Make today the day that you master Monarch’s customized calendars. Test the effects of defining a fiscal year, building some calculations using the new functions and define a few summaries that employ different time intervals on the Matching tab of the field definition.

Now revise the calendar customization and review the effect of your changes to your calculations and summaries.

Becoming a better Monarch modeler means that you know what to expect your outcomes to be before you implement them, and that best way to know that is to practice, practice, and practice.

Master the Space-Time Continuum

Well that’s a bit of a stretch, but with a little up-front effort you’ll find that there’s little that you can’t analyze where dates are involved.

If today you fight with summarizing date based activity, fight no more. Instead, just excel with Monarch.

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Continue your commitment to Become a Better Monarch Modeler with Part 21 of the series, or review Part 19.

Using the Report window we’ve built templates and taught Monarch how to extract our data. In the Table window we’ve added new fields and new values, but even when using filtering and sorting the data, it can be difficult to see the big picture; we’re often just too close to the details.

To give us a new perspective, on day 14 of the 30 Days to Become a Better Monarch Modeler, we’ll begin to explore Monarch’s ability to summarize data using Monarch third and final viewing mechanism, the appropriately named Summary window.

For those unfamiliar with Monarch’s summaries, they’re very similar to Excel’s pivot tables, which aggregate data by grouping data either horizontally (in rows) or vertically (in columns). By this description, you’d think that they’re not much different than a regular tabular spreadsheet, but they’re fundamentally different.

Summarizing and aggregating data is a critical step in converting what often started out as a simple and unassuming report into a management tool that provides Key Performance Indicators (KPIs) and other metrics. KPIs are indispensible for evaluating and managing an endless array of topics.

How to define a new summary

The process begins by assigning the new summary a unique name, and by associating a particular filter to the summary. Only data which satisfies the filter criteria will be reflected in the summary.

It’s important to note that the default filter is literally that: “Default filter”. What it really means though is “Active filter”; the filter that is currently controlling the display of records in the Table window.

The next commonly selected item is choosing whether to include a grand total line at the bottom of the summary.

To define the content of the summary navigate to the Fields tab. When the summary is complete, it will display data beginning with the left edge and continuing to the right edge. On the left edge will be “key” fields, followed (optionally) by “item” fields, and finally “measure” fields.

The exception is that we can set a single key field to display it values across the top of the measures. This is particularly useful for calculating periodic KPIs, whether for a weekly, monthly or quarterly basis or some other timeframe.

When fields have been added to the various key, item and measures categories, double-clicking on a field name displays yet another dialog window that is used to determine the presentation of that field, and depending on the field category.

Key fields offer five tabs:

• General, which allows for customizing the name of the field (which doesn’t affect the original name of the field in the Table window), and the number of characters displayed, or width.
• Matching, which allows for controlling which values will be displayed in the summary.
• Sorting, which allows whether we want to sort the field (either ascending or descending), or to sort the key based on the values in another field.
• Display, which controls how, and what, data is presented onscreen.
• Coloring, which further customizes the display characteristics of the data.

Item fields offer the General, Sorting and Coloring tabs.

Measure fields are subtly and yet significantly different. They have the now familiar General tab, but the Calculation tab is new, and the last tab is Coloring and Limit, which permits for presenting special values of interest in different presentations than other displayed measure values has a different nature than the Coloring tabs available when fine-tuning key and item fields.

The Calculation tab allows us to define which aggregation methods we want to employ, or to define other customized formulas which enable deriving for management metrics. The thing that significantly differentiates formulas for measures from formulas used in calculated fields built in the Table window is that any fields used within summary formulas must be used within aggregating functions, such as SUM(), MAX(), and MIN(). Of course we can still use conventional mathematical operators and constant values in addition to aggregated fields.

The final tab on the summary definition is named Chart. The approach here is to select a chart type, define how you want to cluster the data points on the chart, define a title for the chart, select which of the various display options you want to employ in your chart.

While there are numerous options that produce a range of effects on your charts, for one specific reason I won’t describe them in detail here. And what’s that reason? In my opinion, you shouldn’t spend your time creating charts in Monarch.

Monarch’s charting abilities, which haven’t significantly changed in years, just don’t compare with what you can produce by yourself with Excel, especially if you use MicroCharts by Bonavista Systems. The best use of your time devoted to charting would be spending it building useful visual tools in Excel.

If you don’t have much experience with Excel’s charts or want to sharpen your charting skills then I’d recommend reading Jon Peltier’s excellent blog regularly.

Even Summaries are About the Details

Monarch’s ability to create summaries that you, or your model user, can interact with without changing the structure (key, item and measure fields) that you’ve built into the summary is probably it’s biggest advantage over Excel pivot tables. Even Excel 2007 pivot tables and their inherent ability to expand and collapse fields doesn’t compare to the simple usability of Monarch’s drill up and drill down buttons.

When you define multiple key fields in a summary, Monarch automatically activates the toolbar icons to drill down and drill up, hiding or displaying lower level key fields as you go.

As much as I enjoy the work in the Table window that lets us add new fields via calculated fields and external lookups and such, it’s really when we build and, more importantly, use summaries that we can see just how far we’ve come.

You don’t agree? After you’ve added a summary or two, go back to the report window and look at that basic report that you started out with. Odds are that unless you’re working with the most basic data needs, you’ve added something of value to the data. Maybe it’s new calculations; maybe it’s connect that data to an external source and importing a few related fields. Maybe it’s just as simple as adding subtotals in the summary that weren’t included in the original report. Maybe you’ve spun things around and have created not just a new way of looking at the data, but a new way of thinking about the data.

And why stop there with only one summary? In the dialog used to manage existing summaries, you can click the Duplicate button and make some minor changes, say re-sequencing the key fields, so that you can easily switch between the two summaries for different perspectives, possibly for different audiences. Using an outstanding accounts receivable report, a collections manager wants to see customer totals, while the sales manager needs amounts invoiced by salesperson. It’s the same source report but they’re completely different business applications of the data.

Does the original printed report do that? Nope, not even close.

With such a robust and important feature, there’s much to discuss concerning summaries, but today isn’t about delving into every possibility and feature available to a summary, but rather is about getting us accustomed to building more summaries and really pushing ourselves to take advantage of what’s readily available, but often overlooked.

In Summary Then…

I’ve seen many models come my way that don’t have any summaries defined. That’s like buying great audio equipment and only ever listening to AM radio. Monarch isn’t just an extraction, transformation and loading (ETL) tool. When you combine it with your knowledge of your business and its varying information needs then it can be a fantastic analytical tool. But you have to elect to use it in that manner.

By introducing fresh thinking and new perspectives, simple summaries will demonstrate why it is that you continue to choose to excel with Monarch.

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Continue your commitment to Become a Better Monarch Modeler with Part 15 of the series, or review Part 13.

Auto maker Chrysler recently announced their intent to close 789 dealerships (PDF) in the US as part of their recovery plan.

While it’s unfortunate, and will clearly have an impact on individuals and local economies, we can use their PDF list as an example of what can be done with Monarch Pro in terms of:

1. extracting data from a PDF file,
2. automatically handling address data, and
3. freeing the data from the constraints of the report in which it appears to derive further insights

Additionally, with Monarch Pro we can integrate some data from another source that may provide some background into how the Chrysler management team made decisions.

Handling the PDF

The first page is just a cover, so view the second page and “Auto Adjust” to convert the PDF to text and proceed with creating a detail template.

Select a single line for the template sample, including the dealer name and number. Don’t worry that all of the records appear on more than a single line. I used four numeric traps to capture the dealer numbers (not all of them are five digits).

Paint the fields as follows:

• Dealer Name: 31 characters – advanced properties: End Field On End of Left Justification
• Majority Owner: 23 characters – advanced properties: End Field On End of Left Justification
• Dealer Address: 38 characters – advanced properties: End Field On Blank Field Values: 1 (the record for Massey-Yardley Inc. doesn’t behave with End of Left Justification)
• Dealer Code: 5 digits
• Lines: 4 characters

Save your template, and remember to use the Verify feature to make sure that you haven’t painted the fields improperly.

That’s it. We’ve built the basics of extracting the data. Now we’ll have Monarch some more advanced work for us.

Separate the address data

In the Table window, use Monarch’s Address Block feature to automatically isolate regular address lines, city and state names, and zip codes. You’ll need the first three address lines, as well as City, Region, Postal Code and remember to always use the Error Code when working with Address Blocks. If Monarch thinks that it couldn’t handle an address properly, it’ll alert you by supplying a non-zero error code.

Count the product lines

To isolate which products will be affected by the closures, we need to work with the Lines field. The footer within the PDF instructs us that C is for Chrysler, J is Jeep, D is Dodge, and T is Dodge Truck. To track the individual lines, we need four calculated fields.

The report displays multiple combinations of character sequences when multiple letters are used in the Lines field, so we can’t count on a particular character appearing in the same column position. So how do can we tell which lines are affected at a particular location? We’ll use the Instr function to test if any of the specific characters appear anywhere within the field. Instr() returns a zero if the character does not appear, and returns the position of the character within the field if it is included in the field. So our new numeric fields become:

• Chrysler_Line: if(instr(“C”,Lines)>0,1,0)
• Dodge_Line: if(instr(“D”,Lines)>0,1,0)
• Jeep_Line: if(instr(“J”,Lines)>0,1,0)
• Truck_Line: if(instr(“T”,Lines)>0,1,0)

Finally, we may want to review the average number of lines affected by geography, so for a “Number of Lines” field, we can either add the four line counts, or, knowing that the line count is already reflected in the Lines field, we can use the length of the field to indicate the line count. Simply use

Len(Lines)

as the formula for a Number of Lines field.

Now we’ve completely freed the data elements from the constraints of the report, and we can examine the impacts a little more closely.

Build summaries to analyze data

Let’s determine which states are likely to be hit the hardest. Create a new summary named State, and use the state abbreviation as the only key field. Add count (a default field Monarch creates for summary purposes, a second instance of count (we’ll come back to it), and each of the product line counts.

Double click the key field, and set the sort order to be based on the count measure, descending.

Double click the second count field instance, and on the Calculation tab, choose the Percent of Total option, and set the field to display as a two decimal percentage. Double click each of the line counts in turn, setting an upper limit of 10 for the state count. Display the values in a bold red font. This will format all the values greater than or equal to 10 so that they stand out. Click OK to save the summary definition.

It looks like Pennsylvania, Texas, Ohio and Illinois will be the most affected, with 53 closures in Pennsylvania alone (6.72% of the total closures). More Truck and Jeep lines will be closed in Texas than any other state. The Jeep line gets hit the hardest, with 531 total closures, while the Dodge line takes the fewest, at 430. That’s still a significant number, though.

Let’s get a little more granular and build a city-based summary. First, to avoid grouping any potential identical city names in different states, we’ll build a new field in the Table. “City State” gets this formula:

DlrCity+", "+DlrRegion

I used the Dlr prefix for my address block. This formula ensures that we get two entries for Burlington; one in Iowa and one in Washington.

Now create a new summary named City. Our new City State field is the sole key, and again add the count and a Percent of Total count as the measures. Overall, we see that there’s a pretty good distribution of closures at the city level, though Elyria, Ohio will see eight dealerships close.

Creating a summary using the Number of Lines field as the sole key field and count as the measure reveals that most of the closing dealerships will have two product lines affected.

Monarch transforms static data into dynamic information

In just a few minutes and with minimal effort, we used Monarch Pro to convert a pretty standard list into a real information source. In the next post, we’ll look at how we can integrate what we’ve derived here with some additional data from another source to see what other insights might be available to us.

Building even concise summaries, which take advantage of just few calculated fields, can quickly provide practical tools which assist in understanding the information available, and showcase your ability to excel with Monarch.

Buying a T-shirt, in North America at least, means that you pick a shirt that has a label that that reads either Small, Medium, Large, Extra Large, or depending on the manufacturer, maybe Extra Small or Extra-Extra Large. This simplifies the choices rather than forcing an exact measurement.

It’s not about precision; it’s about being in the right ballpark. Estimating and approximating.

When it comes to numerical analysis, just about any metric – revenues, costs, receivables or payables balances, etc. – can be slotted into one of these familiar categories, and a useful and meaningful overview of the topic can be achieved.

Here’s how to implement this approach quickly and easily with Monarch.

We’ll assume that we simply want to analyze the distribution of revenue generated by our customers. First, create a new calculated Character field named Size, with an expression similar to this:

if(Revenue<3000,"1 X-Small",
if(Revenue<10000,"2 Small",
if(Revenue<250000,"3 Medium",
if(Revenue<3000000,"4 Large",
if(Revenue<10000000,"5 X-Large","6 XX-Large")))))

By adding the 1 through 6 prefixes to our Size labels we can easily use the Size field as a key field in a Monarch summary which will be sorted in the familiar manner, small to large, rather than being based on the normal alpha sort.

The amounts are arbitrary of course, and you should change them to suit your needs. You could even go so far as to create runtime parameter fields for each category level. Users of Monarch Pro could extend that functionality easily into other models by simply importing the new parameters, and the Size calculated field, into other models, and enhance a number of models in no time flat.

The expression for the calculated field using runtime parameters (RP) 1 through 5 could become:

if(Revenue<RP1,"1 X-Small",
if(Revenue<RP2,"2 Small",
if(Revenue<RP3,"3 Medium",
if(Revenue<RP4,"4 Large",
if(Revenue<RP5,"5 X-Large","6 XX-Large")))))

RP6 wouldn’t be needed as values larger than RP5 would default to the last label.

The expression could even be revised to incorporate the threshold into the label:

if(Revenue<RP1,"1 X-Small: < " + trim(str(RP1/1000,9,0)) + "k",
if(Revenue<RP2,"2 Small: < " + trim(str(RP2/1000,9,0)) + "k",
if(Revenue<RP3,"3 Medium: < " + trim(str(RP3/1000,9,0)) + "k",
if(Revenue<RP4,"4 Large: < " + trim(str(RP4/1000,9,0)) + "k",
if(Revenue<RP6,"5 X-Large: < " + trim(str(RP5/1000,9,0)) + "k","6 XX-Large: < " + trim(str(RP6/1000,9,0)) + "k")))))

Next, build a new summary using the Size field as a Key field, and simply using the count as the only measure. This will show the distribution of the customers, and instead of having a gut feel for what the distribution would be, you’ll know exactly. To make the summary even more useful, add a second instance of the count field and set it to display a percent of the total. Now you’ve got a real handle on where things are: what needs attention, and what’s doing just fine.

Does the T-Shirt concept fit?

How will you take advantage of this technique to benefit your organization?

T-shirt size estimating can be done very easily with Monarch, thanks to the summary feature, and provides powerful and flexible insights with minimal effort. It’s a great way to XL (sorry, couldn’t help myself) with Monarch.

Once you’ve built the templates and calculated fields required to extract all of the data within a given report with Monarch, do you save your model, close it, and carry on to the next project, or do you spend some additional time with the report to transform your model from being perfectly functional and workmanlike to being the high performance analysis tool that it can be?

Adding some filters to the model often lets you make sense of your data, revealing both the gems and the lumps of coal that are contained in your reports. To create truly powerful filters, you’ll want to master the Boolean logic (a fancy term with simple impacts) that Monarch offers with its operators.

Those familiar with filtering data with Excel know that you’re not only limited to data found on a single worksheet, but that defining and altering a filter that involves multiple fields is a clumsy exercise at best. On the other hand, the process of defining, changing and activating filters with Monarch is not only easier, but far faster.

Since multiple summaries can be built to be based on specific defined filters, you can not only create multiple views of a particular data set, but your filters can provide multiple data sets while you’re working with a single set of reports, or even just a single report.

Now that special, custom views of your data are available, you can export those and re-purpose them as inputs for one or more additional Monarch process too. Monarch Pro users can use the exported files as database sources for new inputs and/or data that can be used in other external lookups to provide even more analytical power to static reports.

By exporting summaries without totals, you can build what are essentially multiple data tables. Make sure that you do not suppress duplicate values for your key fields when building those summaries, though.

Shine on, you crazy diamond

By using more than just the regular data extraction tools when building Monarch models, you can flush out everything that is hiding in those plain Jane reports. Have a good look at what sort of content is in your next report and think about what you might be able to do with it that might be a little unconventional. You might just find that you can stop working so hard to find the treasures, and instead can have those treasures work for you.

When you provide the insights that allow for truly informed decision making – most especially when what you’re generating doesn’t already exist in another system – those on your team that count on you will know that you really do excel with Monarch.

Everybody that uses Excel regularly has their favorite functions that seem to appear in most of their work, and I’m no exception. Working as I do with data extracted by Monarch, I use Excel’s various lookup functions frequently.

That said, I rarely use the Indirect function. I just never really thought that I needed to take advantage of it.

Quoting from Excel’s online help, you “Use INDIRECT when you want to change the reference to a cell within a formula without changing the formula itself”. The little example that is supplied in the help certainly didn’t do much to convince me that I need to include Indirect in my list of regularly used functions.

But that opinion has changed.

The old way is too slow

I recently found myself working with an Excel file that had dozens of sheets, each of which had the same structure but reflected data for different business lines. Within each sheet there were column groups for actual results, budgeted amounts, and forecasted amounts.

I wanted to make it easy to graph the various amounts for different topics and metrics for each business line by grouping the values to facilitate developing the graphs. Since I had quite a number of graphs to develop for each of the worksheets, I decided it was best to create a sheet for each business line that summarized all of the graphing values for that business line.

I’ve done similar work previously by building the first sheet using various lookup functions to retrieve values from other sheets, then duplicating the master sheet and, using the find and replace dialog, replacing references to the worksheet names first used with other names. This can be a tedious and time consuming job that requires some care.

Develop off-sheet references quickly and correctly

Instead, this time I used the Indirect function to handle the changing sheet names for me and I couldn’t believe how quick and easy the process of developing the values for my graphs became. I was honestly shocked.

I began the work by making it easy to build all of the range references I would need by separating the components. One cell held the name of the worksheet from which I wanted to retrieve the necessary values. Then three cells would contain the range to be used within that worksheet for each group of actual results, budget values, and forecast values.

That’s really all of the information that the Indirect function needs, but there’s one important if not critical point to discuss regarding the Indirect function.

Odds are that you’ve been using Excel for awhile, and are familiar with how Excel builds off-sheet references. For instance, if you need cell A5 on Sheet1 to be ten percent of the value in A5 on Sheet2, the formula you get is:

Sheet2!A5*0.1

If Sheet2’s name is “Budget Data” however, what you get is:

‘Budget Data'!A5*0.1

This distinction is important if we want our Indirect function to work reliably. Sheet names containing spaces must be enclosed with single quote marks, whereas sheet names without spaces must not include quote marks.

With the sheet name keyed into B1, the budget values range listed in B2 as $AQ$16:$BH500, the actual values range listed in B3 as $E$16:$V$500, and the forecast values range listed in B4 as $X$17:$AO$500, I set the formula for C2 as

=IF(ISERROR(FIND(" ",$B$1)),$B$1&"!"&B2,"'"&$B$1&"'!"&B2)

C3 becomes

=IF(ISERROR(FIND(" ",$B$1)),$B$1&"!"&B3,"'"&$B$1&"'!"&B3)

and C4 becomes

=IF(ISERROR(FIND(" ",$B$1)),$B$1&"!"&B4,"'"&$B$1&"'!"&B4)

Finally, I included the Indirect function in a VLOOKUP formula similar to

=VLOOKUP(KeyValue,INDIRECT($C$2),ColumnOffset,FALSE)

Combine the INDIRECT function with Monarch v10 exports

One of the great new features in available in Monarch v10 is its new ability to export Summary in a different manner. Not a different file format, but a new level control has been introduced with which you can export data.

Experienced Monarch users will know that Monarch’s summaries offer the choice to group, and provide subtotals for, related data. Monarch v10 now allows you to export these groups individually. Well, not quite individually, per se, as you must export the entire summary, but where the exported data for each group goes is up to you.

You have two available options:

• 1) You can create individual files for each group, or
• 2) You can create different tables for each group within a single file.

What does this really mean? Let’s say that you’ve got data summarized for 26 products listed: Product A through Product Z, and you want to export to the Excel xls file format.

Option 1 will give you 26 files named “Product A.xls” through “Product Z.xls”.

Option 2 will give you 26 sheets within a single file, with a name of your choice, each sheet named “Product A” through “Product Z”.

You can export to any of the following file formats: accdb, db, dbf, htm, html, mdb, pdf, txt, wk3 (Seriously, who’s still using that? ), xls, xlsm, and xlsx.

I tested this new exporting technique with the model I discussed recently concerning using Monarch for organizing folder and file names. In about five minutes Monarch created almost 350 xls files for me.

Let’s think about this for a moment. Do you need to share data electronically, but not everything should go to everyone? Do you often customize your data packages for your audience? How long does that take you?

Remember as well that these aren’t raw data dumps from the Table window. These are fully formatted custom summary views, complete with Excel’s outlining enabled and all the other bells and whistles that Monarch summaries offer, if that’s the format you use for exporting.

Your ROI for upgrading to Monarch v10 might just be met within a couple of hours by using this one single feature alone.

Just try doing that work without Monarch. Instead, do it purely with Excel. Oh, and I’ll be generous and even give you a whole ten minutes. Start now. I double-dog dare you.

Nothing handles data like Excel paired with Monarch

Once you master Excel’s Indirect function I’m confident that you’ll envision all manner of opportunities to capitalize on Monarch v10’s new exporting abilities. Finally, here’s a bonus for you for reading this far: an Indirect function demo file (25kb).

Upgrade to Monarch v10 and now more than ever, you’ll excel with Monarch.

With the end of the calendar year fast approaching, this also means that the end of the fiscal year is right around the corner for a number of companies too.

If that means that it’s time to develop budgets for next year, I have an idea that might save you some time. If your budget is already complete, no worries, you can always do this next time!

In these turbulent times especially, prudent managers will be looking wisely determine how to allocate for various expenses over the year. Certainly there will be certain mandatory, or fixed, costs, but there are always a number of areas wherein spending is optional, otherwise known as variable costs.

One way to assess what might be coming next year, especially if the manager is new to the business, or even the industry, is to review what the expenditures were in the past fiscal year – each and every one of them.

That could be a huge undertaking, and that’s where you come in. You want to make it easy for this person to be able to get a handle on where the dollars are going, regardless of the size of the business or the number of transactions.

Run a detailed general ledger report from your accounting system that displays all of the transactions posted to all of your GL expense accounts.

Now get yourself a fresh beverage and a fresh pad of sticky notes and set aside a couple of hours to meticulously review each of those hundreds or thousands of pages for the big expenditure items, using a different color highlighter pen for each of fixed and variable costs, taking notes of what you need to tell your budget manager.

Right…

Let’s try that again.

Open your Monarch model for that report. You do have one handy, don’t you? You’ll be able to create one in just a few minutes if you don’t have one already. Be sure that the model captures all of the fields and every detail line.

If your report doesn’t include the fiscal period for each expenditure item as part of the record, create a fiscal period field with a calculated field.

Now whip up two summaries. The first one has the GL account number and name as the leftmost key value. The fiscal period is an across key displayed at the top of the summary, and sums the net expenditure amount.

This gives the mile high view that lets your budget preparer see where the money went, and when.

Now duplicate that summary, and add the expenditure item description as the next key level. Sort the descriptions on a descending expenditure amount order.

Export each summary to a single Excel file, naming the sheets Summary and Detail. This facilitates the review and makes it easy to quickly copy and paste certain bits into an email message if the manager has to follow up with the accounting group. It also allows for a collaborative tool when the users sharing the work can add things like comments to specific cells in the workbook. Make the most of the digital tools available at every turn, and make it easy to use those tools.

Now in addition to being able to quickly see where most of the money went in a given account, you also have at least a partial answer to the question “How did we spend that much on…?” If nothing else, you’ve supplied enough information to be able to easily find more details should they be needed.

Your budget manager will be thrilled as a daunting task just became easier, and just might be more inclined to find room in the budget to accommodate your healthy pay increase.

Save time. Save money. Heck – maybe even make money. Excel with Monarch!

I was recently contact by a regular ExcelWithMonarch.com reader, Nigel Winton of England, who relayed how he used Monarch to create an interesting solution to a challenge given to him by a co-worker.

Here’s his story…

I have one person who is always requesting something new. You know the type: flexible goal posts with no fixed abode.

Recently he asked for a report which would include the required data, along with a summary of the data at the bottom of the report. He did not want the data and the summaries on separate pages, as would be the easy way with Monarch. He also wanted to be able to email the report, and asked that the report print on one page.

It would be possible to create this report with Excel if you have the patience to create many formulae using the SumIf function, but it would be time consuming and somewhat messy. Plus, as the data being summarised could change almost daily, the SumIf approach was difficult to keep up to date. Also, he did not want to see a line in the summary if there weren’t any non-zero values, so constant maintenance would be necessary.

So out comes the trusty old Monarch V9 tool now and I set to work.

I found that, providing I used the same data type, I could append summaries to the end of the exported table. The only proviso is that you must have the same title for the column you are appending to in your summary. That’s not a problem; use Monarch’s ability to change the column title in the summary from the default and it works.

You can set up calculated fields in the Table for things like the word Total that might not be anywhere in your data. Hide them in the table and use them in the Summary to make the presentation easier to read.

It is also useful for putting a total at the bottom of a table when exporting it. Just create a single line summary to total the spreadsheet and append this to the exported table. Make sure you have a character field available to the left of your numeric columns that you can use to put the word Total in, and there you have a table with totals.

If you have numeric fields that you are not totalling, you could do some calculations in a summary and append this under the total line. None of these exported fields will have formulae so they can keep the Monarch Table export look.

In the supplied example, you can see the way in which I have set up the various summary appends, then changed the column titles to make them fit onto the table. The colours are not compulsory, but they can be pleasing to the recipient of your work, who will think that you are being really nice by doing all of this extra formatting work for them.

You can append data from different reports and models providing you keep to the rule of same data type and field name. I have experimented with exporting with the Drill Down and Formula options set for the Monarch summaries, but will leave you to find your own way with those.

This set of files (please see the links below) is by no means up to the limit you can use, but will start you on a path that can bring a feeling of satisfaction that you have produced a meaningful and useful report.

You will need to save the files on your C: drive in a folder named ‘Monarch Demo’ for the Monarch project file to work properly.

Please contact me via Sandy or a Private Message on the Monarch forum if you require further details.

Shake It Up

Nigel’s solution is a terrific example of what you can do when you use Monarch creatively. While the desired layout of the report he described could be created with Excel or by other means, by using Monarch he can regularly publish his report much more quickly and easily.

How will you apply his techniques to your own work and excel with Monarch?

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Links (Right click and select Save Target As):

Append Demo Report

Append Demo Monarch Model

Append Demo Monarch Project

Append Demo Spreadsheet

Append Spreadsheet

I was recently required to review a thousand page PDF file. This file contained hundreds of financial summaries for specific topics. The challenge was, and the need for the review, was that just because certain numeric values were permitted by the online system, the updates the users provided might have been incorrect, incomplete or otherwise in need of some adjustments.

For instance, as it was a financial system, during the review one could check for specific business rules or conditions that the online system was not programmed to flag, such as a higher than normal accounts receivable balance as compared to total revenue, or that the gross margins didn’t make sense for that specific type of transaction.

Well, rather than print the document and mark it up with comments and calcuations, or reviewing on-screen and taking copious notes, I wondered how I could make the most of Monarch’s ability to extract data from PDF files.

A few hours later, I had one of the largest, most comprehensive models I’d built in quite some time.

Not All PDF Files are Created Equally

Unfortunately, the challenge that Monarch users often see when using PDF reared its head once more for me. On the one hand, the software used to create the PDF file itself did create a document that Monarch could read, but Monarch didn’t quite create the exact image you’d see if you opened the file with, say, Adobe Reader. It tended to shift the values somewhat within the line. All of this made the exercise of creating the templates, regularly incorporating many of the advanced field properties, some fun in and of itself.

Then I needed to use some of the “slice and dice” techniques to separate four fields that I had no choice but to initially capture as a single field, because of the way the values were shifting within the line, and because of how many occurrences of those values that I treated as a single field appeared differently throughout the report. It just wasn’t consistent at all.

Finally, after much more effort that was initially foreseen, it was time for the real work: to do something useful with all of this extracted data and save myself what would likely be an enormous undertaking.

1 or 0

If you read that heading as “true or false” instead of “one or zero”, congratulations: you know what’s coming next!

Since a major portion of the review was to essentially judge the integrity of the data (do these values make sense?) and to alert for specific trouble spots, all of which were based on numeric data, why not have Monarch perform all of these calculations for me, and let me know where the trouble spots are?

What I determined was that not only did I need to know where the trouble spots were, but I also had to know just how good, or bad, the data set was. Were the users supplying proper values overall, or did we need further training to reinforce our expectations of them? The only way I’d really know that would be to count how many problems I could find. Further, could I use this approach to determine more precisely where our main challenges lied, and where things were basically on track.

Finally, there were some business rules that wouldn’t generate error conditions so much as warnings, like margins were beginning to erode unexpectedly, but the user supplied values were just fine in and of themselves.

To help Monarch point out all of these indicators to me, I determined that I needed a few calculated fields that would return a value of 1 (true) when an error or a warning condition existed, and a zero value (false) when the data was acceptable.

To get started, I built about 20 of these calculated fields to model the various business rules. Then I added two more: one to indicate when any individual error exists within a record, and another to indicate when any warnings exist. Both of these return only a one or a zero value, not the count of the error or warnings.

Now with all of these indicators in place, I could begin some high level analysis.

First I built four simple filters which narrowed the table to show only those records:

1. with errors,
2. without errors,
3. with warnings, and
4. without warnings.

All of this made the final step both very easy and very powerful.

Putting Monarch’s Aggregations to Work

I concluded the model by building four summaries, each using one of the four filters. Within each of the summaries, I included the appropriate error or warning field as measures. In the case of the “Without” summaries, I use the “Count” aggregation instead of the “Sum” aggregation used in the other summaries.

The summaries included the ability to roll up to a high level department number, and to drill down to each level of business case within the department, grouped by the manager responsible. In this way we could review which managers and overall departments were maintaining the system well, and which needed a bit more guidance.

These few steps saved many hours of meticulous manual, likely error-prone review, just for this single review. If I did nothing else with Monarch all year long, as this is a monthly review process, this savings in time and effort alone would be worth investing in Monarch.

Do you have a similar review process with which you could save time and money, and would demonstrate how you excel with Monarch?

When modeling a report that includes grand totals, always take an extra minute or two to build a summary that totals all of your extracted values to ensure that you’ve modeled the data as accurately as you think you have. You’ll be happy to see that the totals balance to the report.

But you’ll be thrilled to see that they don’t, when you thought you got it right the first time.

That little extra bit of development time is nothing when weighed against the thought that you might have started working with incorrect data, had you not checked your work.