IBM
How do you use one title dimension selection to control another dimension’s subset in a PAW cube view?
A useful feature of building a cube view in Planning Analytics Workspace is the availability of using MDX to control dimension element selections. This functionality can be leveraged to use the selection made in one title element drive what element a separate dimension uses. In this article I have two examples. The first is using…
Read MoreHow Do I Use Hash Values in Planning Analytics to Detect Changes and Improve Data Processing?
When working with IBM Planning Analytics, it is not uncommon to source data from a relational data source. Typically, you will see a Data Warehouse or Data Lake where data is transformed and loaded on a schedule using an ETL tool. ETL, which stands for Extract, Transform and Load, extracts data from a source, modifies it in some…
Read MoreHow Do I Use Conditional Feeders in IBM Planning Analytics to Create More Efficient Feeding Logic?
One of IBM Planning Analytics (PA) biggest advantages over other applications is the ability to use feeders. When it comes to issues of sparsity within cubes, feeders improve performance by giving developers a way to prevent Planning Analytics from trying to look through millions of intersections to find all the data to consolidate. Essentially, feeders…
Read MoreHow Do I Use the SQL Server OFFSET-FETCH Feature in IBM Planning Analytics to Page Query Results?
The OFFSET-FETCH filter is a SQL combination designed so that you can specify how many rows you want to skip before specifying how many rows you want to return in a SQL query. This can come in handy in a variety of ways such as returning results to the user one “slice” at a time…
Read MoreWhen should you feed from string cells in IBM Planning Analytics?
While developing an IBM Planning Analytics (PA) model, you may have come across a situation similar to the following: A rule and feeder are seemingly working correctly, but upon a data change the expected cells are not returning values, implying they are no longer being fed. Consider the example below showing a simple rule from…
Read MoreHow can you use an alternate hierarchy to manage security in IBM Planning Analytics?
IBM Planning Analytics (PA) allows administrator (admin) users to control users’ read and write access through different levels of object security. The system provides a set of redefined groups for admin purposes but allows custom groups to be created as well that users can belong to. Planning Analytics admins commonly use security controls to limit users’ access to elements within a dimension. This can be done by setting a security group’s access…
Read MoreWhat are the best practices for number-to-string conversions in user-executed file exports in Planning Analytics?
In some cases, it is necessary for a file to be exported by Planning Analytics as needed by the users of the system. In most cases this is done by using the ASCIIOUTPUT command in a TurboIntegrator process. When the export uses a NumberToString() function to convert one of the exported values from a number…
Read MoreHow do you filter descendants using MDX to create a dynamic set in PAW?
For those of you in the Planning Analytics (PA) world – Have you used any MDX lately? (MDX=Multi-Dimensional Expression). The goal of this article is to help you spot additional opportunities for MDX to assist in everyday analysis, reports, and extracts out of a PA model. This is a follow-on to an earlier Knowledge Base…
Read MoreHow do you use dynamic server references in PAfE workbooks?
In this article we look into creating a dynamic server reference with PAFE workbooks so that they can be successfully opened across multiple instances. Read on to learn why we do this, how to set it up, best practices, and some troubleshooting tips. What is a Dynamic Server Reference? Planning Analytics for Excel worksheet functions…
Read MoreHow can you use TM1py with Python to interact with IBM Planning Analytics?
TM1py is a free Python package that extends the functionality of Planning Analytics by integrating the TM1 REST API with Python allowing the use of Python libraries. TM1py enhances data source integration, enables machine learning using python, and facilitates Planning Analytics object control using Python automation. In a recent internal “Hackathon” a group of QueBIT…
Read More