IBM Planning Analytics

IBM Planning Analytics Tips & Tricks: Conditional Feeders

September 11, 2018 by Alex Needham Leave a Comment

Did you know IBM Planning Analytics can minimize the amount of feeder data via an approach called a “conditional feeder?” A conditional feeder is exactly what it sounds like – it only feeds when a certain condition is met.

In a previous blog post, I provided an example of how to use an attribute to turn on/off data flow from another cube. We left off with the following rules:

High-Level:

Detailed:

Learn about conditional feeders in IBM Planning Analytics

The cube “High-Level” should only pull from “Detailed” if there is a “Y” in the attribute called “Use Detailed”, but we are currently feeding no matter how the attribute is populated. We can add efficiency by incorporating an IF() statement into the feeder. Here’s an example of how the feeder should look:

Detailed:

Understanding conditional feeders in IBM Planning Analytics

The IF statement will generate one of two results: a valid element to use in the feeder or an invalid result. Planning Analytics will not process an invalid feeder, so any invalid element (including a blank) in the false area will prevent the feeder from occurring.

By writing it in this way, we can turn off/on the feeder by using the same attribute that determines whether to use that line or not.

IBM Planning Analytics is full of new features and functionality. Not sure where to start? Our team here at Revelwood can help. Contact us for more information at info@revelwood.com. And stay tuned for more Planning Analytics Tips & Tricks weekly in our Knowledge Center and in upcoming newsletters!

IBM Planning Analytics Tips & Tricks: Using Attributes

September 4, 2018 by Alex Needham Leave a Comment

Did you know that element attributes in TM1 and IBM Planning Analytics can be used for more than just making report aliases? They can be utilized by the system itself to better manage data flow, and to make it easier to trace where your data is coming from without having to use calculation tracer so often.

Let’s imagine that we have 2 cubes – detailed and high-level. The detailed cube has the same dimensionality as the high-level cube, but it also has expenses by vendor.

How can we tell the system when to pull from the detailed cube vs. the high-level cube, while simultaneously restricting the ability to enter into the detailed cube?

By setting an attribute on the account dimension, maybe something like “use detailed,” we can not only easily turn this on and off, but we can also visually see where our data is coming from. With this set up, here’s what our rules look like:

High-Level:

Detailed:

Understanding attributes in TM1 & IBM Planning Analytics

There’s a problem with that feeder, though. It is overfeeding. Tune in next time to see how to set up a “conditional feeder.”

IBM Planning Analytics Tips & Tricks: String Feeders

August 28, 2018 by Alex Needham Leave a Comment

Did you know feeding based on string values can be tricky? Anyone that has developed in TM1/IBM Planning Analytics before knows full well the importance of a well-written feeder statement.

An example of this is when we have a cube with string measures that are dimensions in another cube. The pull side is quite simple:

The issue, however, is how Planning Analytics registers feeders:

Numeric feeders only trigger once… during the first time the string in the cube “Input” is filled in. Therefore, the feeder above will initially work perfectly well. However, if that string ever changes, the new value will not be fed since TM1 doesn’t reprocess the feeder.

We need to instead have the string value feed the cell. We can do this by altering our feeders:

Learn about string feeders in TM1 & IBM Planning Analytics

By adding the second feeder statement, it ensures that any changes in the string-based “Location” measure will trigger the new location. Don’t forget to include a FEEDSTRINGS command at the top of your rule!

Working With Windows Directory Junctions in IBM Planning Analytics & TM1

August 21, 2018 by Revelwood Leave a Comment

This is a guest post from Revelwood’s Shane Bethea.

Did you know you can create a new path to an existing Windows folder without breaking the original path when working in IBM Planning Analytics and TM1? An example of this is when you have a TurboIntegrator (TI) process that writes log files to a path that is several folders deep, but you want to simplify the path.

Windows allows you to create something called a directory junction that points to the original path. This works similar to a shortcut, but Windows and Planning Analytics/TM1 treat it like a regular folder.

Let’s say I have a log folder within C:\Clients\Lightspeed\TM1Server\Logs and I want to create a directory junction directly under the C:\ drive which points to that path. This can be done by using the mklink command within a Command Prompt. The format of the command is:

mklink /J <Directory Junction Name> <Path to Reference>

I can create a directory junction called LightspeedLogs that references the file path via the following command:

mklink /J LightspeedLogs “C:\Clients\Lightspeed\TM1Server\Logs”

Working with Windows Directory in IBM Planning Analytics

The result is a directory junction called LightspeedLogs that is located directly under the C:\ drive. The junction points to the full path of the Logs folder.

Now your TI processes can write log files to C:\LightspeedLogs and they will be written to the same path as any other log files within C:\Clients\Lightspeed\TM1Server\Logs.

If you need to remove the directory junction, you can use the remove directory (rd) command below.

Working with Windows Directory in IBM Planning Analytics and TM1

IBM Planning Analytics Tips & Tricks: Creating a Simple Dashboard in Planning Analytics Workspace

August 14, 2018 by Nina Inverso Leave a Comment

Did you know it’s easy to create a simple dashboard in IBM Planning Analytics Workspace? IBM Planning Analytics Workspace dashboards allow you to provide quick and effective information to the end user. The following steps demonstrate how to create a simple dashboard.

Navigate to your instance of Planning Analytics Workspace (PAW). Select New > Book from template.
Note: By selecting New > Book, PAW will automatically open a new tabbed workbook. The single page option is only available when you select New > Book from template.
Name your view, select Tabbed and Freeform, and click Create to create a new tabbed workbook.
Expand your TM1 service in the list of available TM1 services in the navigation pane. Then expand Cubes, expand a cube from your list, and expand Views. Drag and drop a saved view onto Sheet 1, and position it on the left-hand side of the screen.
Click on the exploration to activate it. Then click on the Properties button in the upper right-hand corner of the screen. From here, select Synchronize > Synchronize dimensions. This will keep the exploration in sync with the rest of Sheet 1.
Click on the exploration to activate it. Then display the options by clicking on the blue rectangular button located in the top, center portion of the exploration. Select the Duplicate option, which is fifth from the right.
Drag and drop dimensions to/from the context, row, and column sections to change the data shown in the explorations. Display the options and select the Change visualization option, which is second from the right. Select Bar to display the data as a bar chart. Display the options again and click the Show/hide overview button all the way on the right to complete the dashboard.

You can add other objects, explorations, and customizations to create unique dashboards. For more information on Planning Analytics Workspace, refer to the IBM Planning Analytics 2.0.0 documentation.

IBM Planning Analytics Tips & Tricks: Workarounds for Unsupported Excel Functions in TM1 Web

August 7, 2018 by Nina Inverso Leave a Comment

Did you know there are a number of workarounds for unsupported Excel functions in TM1 Web?

Have you ever published a TM1 Web report, only to see repeating #NAME errors when you access the report online? These are typically caused by unsupported Excel functions. While TM1 Web does support most Excel functions, there are some that it does not. Here, I’ll demonstrate ways to work within these limitations by providing examples of workarounds for commonly-used unsupported Excel functions.

Function: ISBLANK

Description: Returns TRUE if the value is blank, FALSE otherwise

	A	B	C	D
1	Input	Original	Result	Compatible with TM1 Web
2		ISBLANK(A2)	TRUE	IF(A2=””, TRUE, FALSE)

Function: ISERROR

Description: Returns TRUE if the value is any error value, FALSE otherwise

	A	B	C	D
1	Input	Original	Result	Compatible with TM1 Web
2		IF(ISERROR(A2/A3), 0, A2/A3)	0.5	IF(A3=0, 0, A2/A3)

Function: ISEVEN

Description: Returns TRUE if the number is even, FALSE otherwise

	A	B	C	D
1	Input	Original	Result	Compatible with TM1 Web
2	1	ISEVEN(A2)	FALSE	IF(MOD(A2,2)=0, TRUE, FALSE)

Function: ISODD

Description: Returns TRUE if the number is odd, FALSE otherwise

	A	B	C	D
1	Input	Original	Result	Compatible with TM1 Web
2	1	ISODD(A2)	TRUE	IF(MOD(A2,2)=1, TRUE, FALSE)

Function: TRUNC

Description: Truncates a number to an integer

	A	B	C	D
1	Input	Original	Result	Compatible with TM1 Web
2	1.75	TRUNC(A1,1)	1.7	ROUNDDOWN(A1,1)

IBM TM1 Tips & Tricks: When to Build Multiple Cubes

July 31, 2018 by Lee Lazarow Leave a Comment

When working with clients to design a TM1 system, I sometimes have clients ask about an approach of building one cube for a model vs. building multiple cubes for a model. What do I mean by that? A cube consists of a single table/database of data whereas a model consists of a series of cubes that are linked together.

TM1 is designed to quickly and efficiently link a series of small cubes together, but this style of thinking contradicts the approach that you may be used to within Excel. An Excel spreadsheet is a single table of data that offers the ability to link together more data via multiple tabs and multiple workbooks. This approach easily gives you the ability to start with a single set of data and then expand it.

What happens though, when you take this approach, is that you end up with your TM1 cube looking like your attic might if you just toss all your stuff in it. You might have old skis, camping gear, baby keepsakes, and more all together in one room. It makes it hard to find what you need and, in some cases, hard to know what you even have up there.

When you build multiple cubes, it’s like having an organized system to store all your stuff. You might have a closet dedicated to your sporting equipment. Camping gear may live elsewhere in a storage unit. Your family heirlooms and keepsakes are kept in water-proof containers in the garage. It’s much easier for you to find what you are looking for.

Revelwood’s approach to building TM1 models is to build small, efficient cubes that each have a separate role and purpose. These cubes link together to make a model. For example, say we are going to build a TM1 model to calculate compensation costs for a mid-sized national technology business. That model has three main components:

Data that gets entered by the planning team
Lookup tables for various rates and dates
Calculated results

Each cube stores only the data that is needed for its specific function and task. For example, there may be one rate cube for Federal tax rates and another cube for state tax rates. Everyone in the company has the same Federal tax rate for a single year whereas each state has a different rate. If you were to have one large cube for all tax rates – Federal and individual states – it would be hard to navigate and may require workarounds to make the data fit (for example, creating a “federal” state).

Building smaller cubes versus one large cube has benefits beyond those that are just about organizing your data. In fact, the organization part is really just a side benefit. When you have a model that consists of smaller cubes, you have a system that is not wasting space and provides you with a flexible foundation for your TM1 applications. Because your cubes are smaller, the entire system also performs faster.

Chances are you bought TM1 because you weren’t happy with Excel. Many Excel “systems” grow into multiple workbooks with many linked spreadsheets, which turns into a messy behemoth. Why would you want your TM1 environment to look like that?

IBM Planning Analytics Tips & Tricks: Upgrading from TM1

July 24, 2018 by John Pra Sisto Leave a Comment

Did you know the window of time for upgrading from TM1 to IBM Planning Analytics is shortening quickly? You need to upgrade to IBM Planning Analytics in order to complete your 2019 budgeting and planning activities. While that is likely the biggest reason to begin your upgrade now, there are many additional reasons to move to IBM Planning Analytics.

A new whitepaper from IBM outlines the powerful new features and enhancements you’ll experience when upgrading from TM1 to IBM Planning Analytics. Here’s a quick look at some of the compelling aspects of IBM Planning Analytics:

IBM Planning Analytics Workspace – the interface that revolutionizes TM1

IBM Planning Analytics Workspace is a customizable user interface that enables users to combine data from any cube in any TM1 database into a single view using a viewer, TM1 websheets, charts or cell widgets. You’ll gain productivity and save time by eliminating time-consuming data conversions.

Additionally, Planning Analytics Workspace (PAW) gives you:

Three forms of data selectors: tiles, lists, and sliders
The ability to have report authors and report consumers filter data-rich “books” with easily
Simple and streamlined navigation through multi-dimensional cubes
The ability to convert “data exploration” into charts you can use for presentations and to share with others (see our blog post IBM Planning Analytics Tips & Tricks: Planning Analytics Workspace Chart Types)

More flexible analytics with hierarchies

IBM Planning Analytics has a new “hierarchies” capability that delivers both technical advantages and end-user benefits. Hierarchies expands the traditional definition of cubes, letting you perform more in depth, granular analysis using “attributes” to describe the characteristics of customers, products, projects, and more.

Hierarchies enable you to create “virtual dimensions” that gives you:

Faster calculations, thanks to smaller cube sizes
The ability to create ad hoc hierarchies, which you can immediately introduce into Excel, web or mobile, for quick and convenient analysis
The ability to see the intersections of several hierarchies within the same dimension, enabling you to drill down to find data points never before possible

IBM Planning Analytics Tips & Tricks: Cleaning Up Your TM1 Environment

July 17, 2018 by Lee Lazarow Leave a Comment

Did you know there are some simple steps you can take to clean up your TM1 environment? A quick clean up gives you easier navigations for ad-hoc analysis, as well as smaller dimensions.

In today’s world, memory is cheap. Hard drives that used to be measured in KB quickly became MB, GB and TB. RAM chips have become larger and computers have become virtualized. This means that it’s easier to quickly expand the environment where your TM1 model is stored.

This also means that it’s easier to become lazy in terms of cleaning up your TM1 environment. You may often have “discussions” with your children about cleaning their room, so why not take the same approach with your TM1 environment?

There are many ways to quickly clean up your model. Some simple approaches can include:

A reduction in the number of scenarios/versions. Do you really need to save weekly forecasts from five years ago?
The removal of old data within a cube. Do you really need to reference your 2010 budget?
The removal of old cubes. Are there cubes that had a one-time purpose that are no longer used?
The removal of old processes. If you initially named a process “TEMP – xxx” then it was meant to be a temporary process.
The removal of “one off” reports. We worked with one client that trimmed a set of 1,000+ reports down to under 100 after analyzing which reports were being used.

Cleaning up your model will make your TM1 environment faster and easier to use. Tasks that take minutes can easily be transitioned into seconds. A smaller set of cubes can simplify the navigations for ad-hoc analyses and smaller dimensions can simplify your reports.

All of this leads to a better user experience… and happier users!

IBM Planning Analytics Tips & Tricks: Conditional Feeders

Read more blog posts with IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: Using Attributes

Read more blog posts on IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: String Feeders

Read more blog posts with IBM Planning Analytics Tips & Tricks:

Working With Windows Directory Junctions in IBM Planning Analytics & TM1

Read more blog posts with IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: Creating a Simple Dashboard in Planning Analytics Workspace

Read more blog posts with IBM Planning Analytics Tips & Tricks:

IBM Planning Analytics Tips & Tricks: Workarounds for Unsupported Excel Functions in TM1 Web

IBM TM1 Tips & Tricks: When to Build Multiple Cubes

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IBM Planning Analytics Tips & Tricks: Upgrading from TM1

Read more blog posts on upgrading from TM1 to IBM Planning Analytics:

IBM Planning Analytics Tips & Tricks: Cleaning Up Your TM1 Environment

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