Posts by agoddard
Securing by Access Rules – Workday Adaptive Planning
Prior to the 2019.3 release, security was restricted to level access control in conjuncture with account, version, and sheet settings . Limited no longer! Security is more robust and now includes access rules on accounts and custom dimensions in order to limit specific intersections of data that users and groups can view or edit. Levels,…
Read MoreDashboards at a Glance in Planning Analytics Workspace
Much of analytics is trying to decrease the amount of time and effort for converting data into valuable insights. Planning Analytics is a tool that does this very well. Since you’re reading this post, you are probably already familiar with Planning Analytics and web and reporting environment called Planning Analytics Workspace (PAW). PAW is great…
Read MoreUsing a SharePoint site for IBM Planning Analytics flat file data sources
Technology executives and managers are always looking for ways to be more effective while at the same time reducing costs in maintenance hours, physical servers, processes, etc.— It’s always a balancing act. One way to accomplish both goals can be by leveraging existing technology investments that are tried and true. Microsoft’s SharePoint and OneDrive is…
Read MoreIntegrating Planning Analytics with Certent Disclosure Management (CDM)
What is Certent CDM? It is a reporting management platform where you can create, compare, and communicate all crucial aspects of internal and external reporting with flexible deployment, on-premises, or in the cloud to best support your global business needs. If you are using CDM and need to report on TM1/Planning Analytics data, don’t worry…
Read More5 Powerful MDX Functions to use in your IBM Planning Analytics Model
IBM Planning Analytics (PA) subsets can be made dynamic through multi-dimensional expressions (MDX). This reduces manual maintenance and ensures that subsets display the latest metadata and data available in the model. For example, when the Current Year of a model is modified, an MDX driven “Current Year Months” subset will be updated immediately. Today we…
Read MoreDynamic Reports with MDX in Planning Analytics
Dynamic Reports with MDX Dynamic Reports in Planning Analytics for Excel (PAfE) takes “complex reporting” to a whole new level! This report is fully embedded within Excel and utilizes TM1 formulas. One of the greatest strengths of this report type is that it allows for expandable / collapsible rows, dictated by the TM1RPTROW formula. There…
Read MoreLoading Data to IBM Planning Analytics using TM1Py and Pandas Part 1
Title: Loading Data to IBM Planning Analytics using TM1Py and Pandas Part 1 Author(s): Paul Caggiano Application: Planning Analytics, Python Software Version: Planning Analytics Version 11.8.01300.1; Python 3.9.12 Keywords: TM1Py, Python, Planning Analytics, TM1, Pandas Python is no doubt one of the fastest growing programming languages in terms of its use for data science. This…
Read MoreLoading Data to IBM Planning Analytics using TM1Py and Pandas Part 2
Title: Loading Data to IBM Planning Analytics using TM1Py and Pandas Part 2 Author(s): Paul Caggiano Application: Planning Analytics, Python Software Version: Planning Analytics Version 11.8.01300.1; Python 3.9.12 Keywords: TM1Py, Python, Planning Analytics, TM1, Pandas This article is Part 2 of the series exploring how to integrate TM1Py with your IBM Planning Analytics (PA) instance…
Read MoreLoading Data to IBM Planning Analytics using TM1Py and Pandas Part 3
Title: Loading Data to IBM Planning Analytics using TM1Py and Pandas Part 3 Author(s): Paul Caggiano Application: Planning Analytics, Python Software Version: Planning Analytics Version 11.8.01300.1; Python 3.9.12 Keywords: TM1Py, Python, Planning Analytics, TM1, Pandas This article is Part 3 of the series exploring how to integrate TM1Py with your IBM Planning Analytics (PA) instance…
Read MoreHighlighting Input Cells in PAW
The power of IBM Planning Analytics (PA or TM1) comes from its ability to receive input information from different users, take that input and, through rules embedded in the model, consolidate a forecast (budget), or demonstrate variances to actuals. As developers working with PA we have to build models that perform well in the back…
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