IBM Planning Analytics

IBM Planning Analytics Tips & Tricks: Code Char

August 3, 2021 by Lee Lazarow Leave a Comment

Have you ever written a TurboIntegrator script and wanted to check the first character of a value to see if it was a number or a letter? Have you ever needed to insert a single or double quote into a string without “breaking” your code? Have you ever wanted to easily convert a lower case letter into an upper case letter? All of this can be done by referencing the ASCII code associated with the character.

ASCII is an abbreviation for American Standard Code for Information Interchange and is used as a standard for electronic communication. ASCII codes represent text for computers. Every key on your keyboard has a defined code number. Tables associated with these codes can be found via a simple Internet search.

There are two functions in IBM Planning Analytics / TM1 that allow you to reference the ASCII code:

CHAR returns the character associated with a numeric ASCII code. For example, CHAR(76) returns a capital L.

CODE returns the numeric ASCII code associated with a character. For example, CODE(‘Lee’, 1) returns 67 since the first character is a capital L.

These functions can be used in conjunction with other functions and logic to perform various tasks:

If the first character of a value has an ASCII code in the range of 48-57, then the value is a number.
If the first character of a value does not have an ASCII code in the range of 48-57, 65-90, or 97-122 then the value is not a number or a letter.
ASCII code 39 is a single quote and ASCII code 34 is a double quote.
All capital and lowercase codes have the same ASCII code value difference, so you can add or subtract 32 to convert between uppercase and lowercase.

These functions allow you to check and manipulate data without having to ignore the rules of TurboIntegrator coding.

IBM Planning Analytics, powered by TM1, is full of new features and functionality. Need advice? Our team here at Revelwood can help. Contact us for more information at info@revelwood.com. We post new Planning Analytics Tips & Tricks weekly in our Knowledge Center and in newsletters.

IBM Planning Analytics Tips & Tricks: Depreciation

July 27, 2021 by Lee Lazarow Leave a Comment

P&L planning models consist of a variety of inputs, including revenue planning and various forms of expense planning. In addition to these standards, we are often asked to develop other planning components. One of these components entails a depreciation process, which leads to the question: what is depreciation?

Depreciation is defined as “a reduction in the value of an asset due to wear and tear or obsolescence.” In business terms, this typically entails a calculation to determine your current asset’s value.

There are various methods to calculate depreciation, all of which are taught in an Accounting 101 class. Most organizations, however, use a simple approach called “straight line” that spreads the reduction evenly over the course of a pre-defined asset life.

There are typically four components to a depreciation process:

Asset value – the original cost of the asset
Asset life – how long will it take for the asset to stop reducing its value
Salvage value – the amount you expect the asset to be worth at the end of the asset’s life (e.g., a resale value)
In service date – the time that you begin depreciating the asset, which is not always the same time as the purchase date

The calculation is relatively simple: subtract the salvage value from the asset value to determine the amount to be depreciated, then divide that amount by the asset life. Remember to ensure that the asset life uses the same level of time periods as your model … months, quarters, or years.

Once the depreciation calculation is performed you can easily view the original asset amount, the total allocated amount, and the current asset value. Want to learn more about depreciation details or recommended best practices? Contact us and we’ll help you.

IBM Planning Analytics Tips & Tricks: Scatter Chart vs Bubble Chart

July 20, 2021 by Lee Lazarow Leave a Comment

A scatter chart is used to show relationships within your data. This type of chart is great to see patterns and groupings over a large set of values. A bubble chart is also used to show relationships and it also great to see patterns. So what is the difference between the two types of charts?

A scatter chart is used to show the correlation of two data sets that have different ranges of values. The IBM Planning Analytics Workspace (PAW) example below shows the correlation of revenue and gross profit.

IBM Planning Analytics Tips & Tricks: Scatter vs Bubble

The values associated with gross profit range from approx. 50,000 to 250,000. The values associated with revenue range from approx. 350,000 to 850,000. The scatter chart allows you to put these two different data sets onto the same chart and therefore see patterns. In this case, we see a positive correlation between larger revenue and larger profit … thereby telling us that our indirect costs are somewhat symmetrical to our direct costs.

But what if we also want to know how the number of units impacts these numbers? This cannot be done on a simple two dimensional chart, but it can be done by adjusting the size of the dots within the chart. This adjustment approach is the purpose of a PAW bubble chart.

Here is the same set of data with the inclusion of units:

In both situations you can see the correlation in the data by looking at the patterns. However, the bubble chart also gives you information on the units by adjusting the size of the dots. This gives us another positive correlation between larger unit sales and larger revenue … thereby telling us that we may have the opportunity to introduce regional pricing.

As you can see, the type of chart makes a difference when reviewing your data. So how do you decide which chart to use? The simple answer is to ask yourself how many details do you want to see at once? Are you looking for two correlations or more? As long as your picture answers that question, you can never go wrong!

IBM Planning Analytics Tips & Tricks: Planning Analytics Workspace (PAW) Marker Shape

July 13, 2021 by Lee Lazarow Leave a Comment

A line chart can either be a simple line or it can include markers for each data point. Most people associate a marker as a simple dot, but did you know that IBM Planning Analytics Workspace (PAW) allows you to configure the shape of the markers?

This is done by enabling the property called “Show markers’ and then defining a value for the property called “Marker Shape.” Both of these settings are found within the Chart settings of the Visualization area.

IBM Planning Analytics Workspace Marker Space

In addition to a circle, PAW offers a variety of shapes that you can use on your chart.

IBM Planning Analytics Workspace Marker Shape

This flexibility allows you to customize your line charts and enhance your end user experience.

Technical Bulletin: End of Support for IBM Planning Analytics Local, IBM Planning Analytics Advanced and IBM Planning Analytics Express Versions 2.0.0 – 2.0.8

July 6, 2021 by Lee Lazarow Leave a Comment

IBM recently announced End of Support (EOS) dates for older versions of IBM Planning Analytics Local, IBM Planning Analytics Advanced and IBM Planning Analytics Express.

Here are the important dates to know:

April 30, 2022 – End of support for versions 2.0.0 – 2.0.7
September 20, 2022 – End of support for version 2.0.8.

In order to keep your Planning Analytics environment working properly, we recommend that clients upgrade to Planning Analytics version 2.0.9. Upgrading to a newer version of Planning Analytics will give you access to new fixes and features, such as:

Multi-threaded loading of cubes for faster startup
Data protection by ensuring that a major error in a TurboIntegrator process rolls back instead of committing any changes
Various new configurating settings, formatting options and functions for TM1 Websheets
Support for Windows Server 2019

Revelwood Can Help

Let us make this upgrade as easy as possible on you and your team. We can assist you with all aspects of the upgrade, including:

Downloading and installing Planning Analytics 2.0.9
Assisting with the testing process, and if needed, creating a test plan
Reviewing your existing Planning Analytics environments
Optimizing your migration approaches (Development, QA and Production)
Training for new features and configuration settings
Defining a Road Map for you for future changes to the Planning Analytics environment.

If you would like Revelwood’s assistance with your upgrade, please contact John Pra Sisto.

Learn more about IBM’s announcement:

IBM Planning Analytics Tips & Tricks: 445

July 6, 2021 by Lee Lazarow Leave a Comment

Time is an important aspect when implementing planning models. Calculations often use time to spread an annual amount throughout the year. However, the definition of “spread” does not always entail a simple approach.

Sometimes clients want to spread an annual value evenly through the year, which leads to an easy “divide by 12” calculation. But sometimes clients want to spread the values based on weeks. This is where calculations get a bit more complicated.

A standard year includes 52 weeks. However, not all months contain the same number of weeks and we do not want the spread to include any decimals. As a result, we cannot simply divide 52 by 12 months. We can, however, easily divide 52 by 4 quarters and calculate that each quarter has 13 weeks.

But we again face the same challenge since 13 weeks divided by 3 months results in a value with decimals. This is where accounting standardization comes into play. There are three standard methods to determine how to spread the number of weeks throughout a quarter.

4-4-5
4-5-4
5-4-4

Each of these methods define how many weeks are in the first month, how many weeks are in the second month, and how many weeks are in the third month. For example, the 4-4-5 method ensures that a calendar based year always has 4 weeks of data in fiscal January, 4 weeks of data in fiscal February, and 5 weeks of data in fiscal March.

There are advantages and disadvantages to this approach:

Advantages:

Comparisons are easy since the end date of the period is always the same day of the week
- Every period is the same length which makes quarter vs. quarter comparisons useful
- Comparisons can easily be made to the same period in the prior year
- Comparisons can easily be made to a similar period in a different quarter
Since the number of weeks in a month are consistent, it is very easy to calculate weekly averages

Disadvantages:

Month by month comparisons are flawed because one month is 25% longer than the other two
The year only includes 364 days (7 days x 52 weeks), so a 53rd week will need to be added every five or six years … which can make year over year comparisons difficult
This approach can also lead to other complex accounting practices such as accruals

IBM Planning Analytics Tips & Tricks: PAW Chart Padding

June 29, 2021 by Lee Lazarow Leave a Comment

Did you know that IBM Planning Analytics Workspace (PAW) offers a feature calling “padding” which defines lines are separated within a chart? The concept of padding is the equivalent of defining white space in between the lines (e.g., no padding) or making the lines touch the lines above and below (e.g., padding).

The setting is located within the chat area of the visualization properties. It is defined as a binary option so you can choose to enable it or disable it.

Here is an example of the same chart via both options. The chart on the top has padding enabled (e.g., the option is not checked) and the chart on the bottom has padding disabled.

By defining whitespace around your charts, you make your data more readable.

IBM Planning Analytics Tips & Tricks: Pie Chart Sizing

June 22, 2021 by Lee Lazarow Leave a Comment

Did you know that IBM Planning Analytics Workspace (PAW) allows you to “split” your pie chart widget to define how much of the space is allocated to the legend and how much of the space is allocated to the chart?

While in edit mode, you can view and modify the split by moving your mouse over the widget. You will then see a bar appear that can be used to resize the split.

Here is an example of a chart with a legend:

Pie Chart Sizing in IBM Planning Analytics

Here is the same chart with my mouse hovering in the legend area of the widget:

IBM Planning Analytics Tips: Pie Chart Sizing

And here is the result of dragging the bar up:

IBM Planning Analytics Tricks: Pie Chart Sizing

You can see that the chart is now larger since less area was allocated to the legend.

Interested in learning more about charts and graphs in IBM Planning Analytics Workspace? Watch our webinar, “Best Practices When Using IBM Planning Analytics Workspace Charts.”

IBM Planning Analytics Tips & Tricks: Planning Analytics Workspace Tab Icons

June 15, 2021 by Lee Lazarow Leave a Comment

Have you ever created an IBM Planning Analytics Workspace (PAW) book and wanted to include images within each tab? PAW version 57 introduced settings that allow you to control details about the tabs. Some of these settings allow you to insert icons into the tab name. This is done via two steps.

The first step is to define the icon you want to use. This setting is found within the Individual Tab Properties and is defined independently for each tab.

An icon can be used in addition to the text or it can be used as a replacement. Here are examples of tabs that show text-only, text-with-icon, and icon-only:

The second setting defines where the icons will be positioned in relation to the text. This setting is found within the Dashboard Properties and is defined once for the entire book.

Here is an example of a tab which uses red text, includes the light bulb icon (called “idea”) and has the icon located above the title:

These new settings give you more control of the tab details within your PAW books and can be used to further optimize your user experience.

IBM Planning Analytics Tips & Tricks: Code Char

Read more IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: Depreciation

Read more IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: Scatter Chart vs Bubble Chart

Read more IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: Planning Analytics Workspace (PAW) Marker Shape

Read more IBM Planning Analytics Tips & Tricks:

Technical Bulletin: End of Support for IBM Planning Analytics Local, IBM Planning Analytics Advanced and IBM Planning Analytics Express Versions 2.0.0 – 2.0.8

IBM Planning Analytics Tips & Tricks: 445

Read more IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: PAW Chart Padding

Read more IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: Pie Chart Sizing

Read more IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: Planning Analytics Workspace Tab Icons

Read more IBM Planning Analytics Tips & Tricks:

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