Lee Lazarow, Author at Revelwood Knowledge Center – Page 3 of 5
Lee Lazarow, Author at Revelwood Knowledge Center – Page 3 of 5

Did you know that there are different ways to create selectors in IBM Planning Analytics Workspace (PAW)? TM1 Perspectives allows you to create selectors via the SUBNM formula. This approach offers an easy way to create a selectable list on a report or input template, but it requires some training to learn how to create and manipulate the formula. PAW allows users to more easily create selectors by simply dragging items onto the screen via a “selector widget.” This is … Read More

Did you know that that way you write your rule could impact the performance of the model in IBM Planning Analytics? There are three parts to a rule. The area definition describes the section of the cube to which the rule will apply. The qualifier allows formulas to be restricted to base level numbers, consolidated, numbers or string values. And the formula defines how the components will be calculated. Sometimes I am asked about the best approach to use when … Read More

Did you know that you don’t have to show all of your dimensions during an ad-hoc query in IBM Planning Analytics Workspace (PAW)? TM1 cube views and formulas require a reference to every dimension within the cube. While all of these dimensions have a use within your cube, they are not always needed for your reports, templates and ad-hoc querying. PAW books allow you to hide dimensions during ad-hoc browses. This is done by simply dragging the dimension off the … Read More

Did you know that you can bookmark items for quick navigation in IBM Planning Analytics Workspace (PAW)? PAW’s navigation menu allows you to scroll through your entire system which includes cubes, dimensions, views and other aspects of your model. This ability to access your models within a single list is very powerful, but it can also become very large. PAW offers an easy way to quickly navigate to important components of your model that via the use of bookmarks. To … Read More

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 … Read More

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. … Read More


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