{"id":4328,"date":"2024-03-11T19:14:37","date_gmt":"2024-03-11T19:14:37","guid":{"rendered":"https:\/\/quebit.com\/askquebit\/?p=4328"},"modified":"2026-01-16T15:44:02","modified_gmt":"2026-01-16T15:44:02","slug":"characteristics-of-a-good-data-model-in-ibm-planning-analytics","status":"publish","type":"post","link":"https:\/\/quebit.com\/askquebit\/characteristics-of-a-good-data-model-in-ibm-planning-analytics\/","title":{"rendered":"What Are the Key Characteristics of a Good Data Model in IBM Planning Analytics?"},"content":{"rendered":"

IBM Planning Analytics – <\/strong>Characteristics of a \u201cGood\u201d data model <\/strong><\/p>\n

Modeling<\/h2>\n

Many \u201cstrategies\u201d exist today for modelling (building a database) in IBM Planning Analytics. If you are a member of the IBM Community,<\/a> you\u2019ll find no shortage of opinions and recommendations – from informal blogs and discussions to more formalized courses and mentoring.<\/p>\n

In fact, there is a free and downloadable \u201ceducational guide\u201d that covers an overview of the steps involved in building a model including the details involved in each step.\u00a0 The guide is available (in the IBM Community library<\/strong>) here: https:\/\/community.ibm.com\/community\/user\/businessanalytics\/viewdocument\/building-a-model-in-planning-analyt<\/a> (note: to supplement understanding of the material, you can also download the database which is referenced in the guide and use it to explore the modelling objects in more detail).<\/p>\n

\"\"<\/a><\/p>\n

Beyond the Build<\/h2>\n

The Planning Analytics Workspace<\/a> (PAW) platform can really simplify creating a data model – still, as an architect, you should take time to carefully design your data model, making sure that it aligns with current requirements as well as the application\u2019s expected future state.<\/p>\n

A \u201cgood\u201d design makes it easier to scale your model, maintain it, as well as take advantage of current and perhaps future Planning Analytics features, and of course better serves the needs of the business, while the impact of poor design decisions<\/em> made when designing a model are often not immediately noticeable. Rather, the \u201cweakness\u201d of a model is exposed as the application scales.<\/p>\n

\u201cScaling\u201d can include adding additional users, modifications to security schemas, increasing data volume or velocity, material data reclassifications, and so on.<\/p>\n

The Perfect Design<\/strong><\/p>\n

The first step to beginning a data modeling exercise is to accept that there are no \u201cperfect\u201d data model designs, only very good<\/em> ones. Since organizational (and data) requirements grow and evolve, there can be no \u201cone size fits all\u201d or \u201conce and done\u201d models.<\/p>\n

\u00a0<\/strong><\/p>\n

A \u201cgood\u201d data model design is one that serves as a foundation for data use<\/em> by providing correct answers<\/em> from trusted data sources, can \u201cflex\u201d to handle changing data formats and needs , and is user-friendly in that it is easy for users to view and understand the data and its relationships.<\/p>\n

\u00a0<\/strong><\/p>\n

Some other characteristics of a \u201cgood\u201d data model design include:<\/p>\n