{"id":3918,"date":"2023-05-22T13:06:36","date_gmt":"2023-05-22T13:06:36","guid":{"rendered":"https:\/\/quebit.com\/askquebit\/?p=3918"},"modified":"2023-05-22T13:11:43","modified_gmt":"2023-05-22T13:11:43","slug":"planning-analytics-and-object-permanence-2","status":"publish","type":"post","link":"https:\/\/quebit.com\/askquebit\/planning-analytics-and-object-permanence-2\/","title":{"rendered":"Planning Analytics and Object Permanence"},"content":{"rendered":"

Planning Analytics and Object Permanence<\/strong><\/p>\n

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Object Permanence <\/u><\/strong>is \u201cthe understanding that objects continue to exist even when they cannot be sensed; a fundamental concept studied in the field of developmental psychology, the subfield of psychology that addresses the development of young children’s social and mental capacities\u201d (wikipedia.org).<\/p>\n

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Does <\/strong>How does this relate to Planning Analytics?<\/strong><\/p>\n

In every Planning Analytics model\u2019s lifespan, new objects are continually added, and typically existing objects are not always decommissioned. This means that the total number of objects increases while some objects continue to consume valuable resources while not adding value\u00a0 (since they are not or seldom used).\u00a0 In other words, you\u2019ll have objects continuing to exist while not<\/em> \u201csensed\u201d (not used and therefore not adding value to the model) \u2013 not very economical.<\/p>\n

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Taking a baseline <\/strong><\/p>\n

To remedy this situation, start by establishing a baseline. A baseline<\/strong> is an initial measurement of a condition that is taken at a specific point in time to be used for comparison over time looking for variations. In our case, we want to establish some baseline facts such as the total memory footprint <\/u>of the model and perhaps the time it takes for the server to start up<\/u> (without the aid of persistent feeders<\/a>). These data points (and others) can be useful as \u201cmeasurable results\u201d to gauge the effect of any object decommissioning you do.<\/p>\n

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Identify the Usual Suspects<\/strong><\/p>\n

In horticulture<\/em> there exists the practice of regularly pruning plants so as to stimulate new growth as well as maintain the health of the plant. You may refer to what is known as the \u201c 1\/3 pruning rule\u201d which involves cutting about 1\/3 of wood during any pruning activity. This moderate pruning practice is a balanced approach to thinning out shrubs to stimulate new growth. The plant loses a good number of stems, usually in the top section, allowing more light and air to enter the inside of the plant.<\/p>\n

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While I don\u2019t advise looking to remove 1\/3 of your planning analytics model objects, I would<\/em> recommend being aggressive with your cleanup (backups first of course!). To that point, I suggest beginning by creating a \u201cprofile\u201d of your model to guide your pruning, including:<\/p>\n

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