Tableau: Live, Extract, and Joining

In business today, you hear the term data-driven decisions, but do we understand what that means? As business owners or people working for a company, we must realize that all decisions must be supported by some information to make sense- i.e., Winter coats sell less in the summer, so we don’t restock winter coats out of season. We gather information by collecting data on our customer’s behaviors with our ads, website, campaigns, and sales functions. However, that data itself is not the sole answer. Your data must be turned into understandable information. This is where business intelligence software steps in. Tableau is Microsoft’s analytics program that works to create relational databases. External data sources can be linked to the program to pull together visual reports focused on desired criteria. Below are some functions of Tableau and how they work.

Live or Extract

There are two types of data inputs into Tableau live and extracts. Extracts are snapshots of data from external data sources uploaded to the platform and saved on its system memory. For example, uploading your company’s quarterly sales reports in an excel workbook. Extracts are quicker to create visualizations but will only have the static data to work off of. This can pose a problem if the data you work with gets updated frequently. Using extracted information will work adequately for your needs if you’re working on analyzing last quarter’s sales activities. However, if you need weekly reports, working with live data would be a better option. Live data gets updated in real-time, yet it is slower to work with.


When multiple data sources are uploaded into Tableau and used for the same workbooks, the data files need to be joined together. Joining refers to data from one source linking to data from another source by common fields. There are four types of joins inner, right, left, and outer joins. The inner joins create data tables that have matching information on both sides. For example, the Product ID or Customer ID matches the data in both data sources, and only data with matching fields will appear in your table. The right and left joins use data from tables on either side but display a null value in the data table for empty areas on their corresponding opposite side. The outer join refers to data tables that demonstrate all the information from both data sources; however, they show a null value on either side when the field is empty.

Next Steps

Data-driven decisions can be beneficial to businesses if the right data is collected, and meaning insights can be formulated around the information. Take a look at how your data reporting is conducted. Does your data change rapidly, and can it benefit from live reporting? If you haven’t yet, read my last blog on efficiency with ERP systems.