Just because two variables seem to change together doesn't necessarily mean that one causes the other to change. ![]() The data patterns are exactly the same in each of those cases and an algorithm can't tell the difference between each case. You can't tell just from seeing that relationship in the data that A is causing B, or B is causing A, or if something more complicated is actually going on. Explanations are based on models of the data, but are not causal explanations.Ī correlation means that a relationship exists between some data variables, say A and B. While Explain Data can be used with smaller data sets, it requires data that is sufficiently wide and contains enough marks (granularity) to be able to create a model.ĭon't assume causality. For more information about aggregation, see Data Aggregation in Tableau.Ĭonsider the shape, size, and cardinality of your data. Explain Data can't be run on disaggregated marks (row-level data) at the most granular level of detail. This means that your data must be granular, but the marks that you select for Explain Data must be aggregated or summarized at a higher level of detail. This feature is designed explicitly for the analysis of aggregated data. Use granular data that can be aggregated. When running Explain Data on marks, keep the following points in mind: A tool that is giving you an answer or telling you anything about causality in your data.A tool to prove or disprove hypotheses.A tool and a workflow that helps expedite data analysis and make data analysis more accessible to a broader range of users. ![]()
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