9/23/2023 0 Comments Tableau prep best practices![]() ![]() Think of changes to region realignments, the company organization chart, or item price. However, other dimensions tend to change much more frequently than facts. Some dimensions, like the date/time calendar, change very little, if ever. For example, in the statement “a patient in a hospital at a given time with a medical condition who is attended by a medical professional”, the fact might be the patient encounter, and the associated dimensions might include the date and time, medical professional, medical condition, and facility (hospital). ![]() In dimensional data terms, fact data describes an event or transaction, like a store purchase, and dimension data represents reference or master data that describes the event. Inasmuch as is possible, modularize and keep separate collection of dimension data and fact data, waiting to join them together at the last possible opportunity. But in the cases I’ve seen, challenges associated with data blending make it more practical to tailor a custom data source.) Separate Dimensions From Facts Another reasonable expectation is that teams might assemble data for a dashboard by blending data sources, and thereby achieve reusability of single-subject data sources. However, in my experience different visualizations have unique requirements such that shared data sources haven’t made much sense. (For those using Tableau data sources without a data prep tool, publishing separately enables many workbooks to share a single data source. In my experience, large data sources embedded in workbooks have a tendency to unexpectedly reload themselves in Tableau Desktop, delaying development by tying up the workbook for up to 30 minutes or more while displaying a “Processing Request” message box. Likewise, the team could change a viz without dragging the entire data source down from the server and thereby slowing down the change cycle. Using the CVI example, when Marketing Analytics separately publishes workbooks and data sources, then they can change the CVI calc without the risk of inadvertently altering visualizations. Tableau provides the ability to publish data sources independently of workbooks. Publish Data Sources and Workbooks Separately But regardless of whether a team uses a data prep tool or custom queries in Tableau data sources, workbook developers should stick with two rules of the road to increase maintainability and reduce time to market for revisions and corrections. There are different ways for such a team to pull source data efficiently, like using Tableau Prep or Alteryx. However, there are teams, like the fictional Marketing Analytics, that support significant Tableau complexes accessing large, lightly designed data stores. With that architecture, CVI would be a column in the Customer dimension table, and a CVI revision would require only a relatively quick database logic fix. Marketing Analytics suspends new development for two sprints to focus only on updating these fields across all reports.īest practice for Tableau teams supporting enterprise analytics and reporting is to draw data from a well curated dimensional database. Two months later, in the midst of adding that new field to the 30 relevant workbooks, managers requested a revision to CVI and three other calculations shown on the team’s dashboards. In spite of generally sound practices, Marketing Analytics struggles to maintain consistency from one Tableau workbook to another.įor example, at one point the imagined company introduced a new “customer value index (CVI)”. After a while the team finds itself supporting scores of published workbooks serving a few hundred managers and executives. After a few initial successes with some impactful visualizations, the team gathers steam. ![]() They gain approval for Tableau licenses and Tableau Server publication rights for five tech-savvy data analysts. ![]() Imagine a business team - let’s call it Marketing Analytics - with read-only access to a Hadoop store or an enterprise data warehouse. In addition to sound development practices, following two key principles in data source design help these teams spend less time in maintenance and focus more on building new visualizations: publishing Tableau data sources separately from workbooks and waiting until the last opportunity to join dimension and fact data. It’s not unusual for talented teams of business analysts to find themselves maintaining significant inventories of Tableau dashboards. ![]()
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