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Tableau Tips: Synchronizing Multiple Data Sources in a Tableau Dashboard

The setup
You have connected to more than one data source in your Tableau workbook, for example, your sales database in Oracle and a customer call database in SQL Server.  Tableau seamlessly allows you to connect to multiple data sources in one workbook.  Views developed from both data sources can be placed in a single dashboard.

However, if you try to use global filters or quick filters to synchronize the two data sources, you will find that it isn’t possible; only one data source can be linked to either.  In fact, the term “Global” filter in Tableau can be confounding, since it is only global to views using the current data source.  Global filters will not work with other data sources even if the data items have the same name!

The solution
As of Tableau 5.0 (released in 2009), a new feature

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Rapid Graphs- Tableau for Customer Segmentation and Lifetime Value Analysis

This was presented at the Tableau Conference 2009. It is about the tremendous opportunities to leverage customer segmentation and lifetime value data at the individual customer level. Click here for the presentation.

Identifying segments for every customer can unlock long-hidden value for your marketing team. Combining this information with Tableau creates a simple means for marketing owners to evaluate and improve their decisions.

A second opportunity is adding lifetime value estimates to each customer as you acquire them and as their behavior adds additional information to inform their value. With lifetime value estimates, you will gain a huge competitive advantage in evaluating your the long-term value of your marketing investments and product decisions. Are your most expensive programs just impacting short-term outcomes and only reaching lower value customers? How are your customers being affected by a blend of marketing programs over time?

Click here to see a summary view on Tableau Public which was constructed from part of the Tableau workbook used in this presentation. Many thanks to Ellie Fields at Tableau for selecting the views and publishing this summary!

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Data warehousing success- 100% Vision / 80% Tactical / 20% of the Work

My Background in Data Warehousing

I have been involved in the creation, development, and maintenance of seven data warehouses through the years- one of them before I even knew about the term “data warehousing”!  I have built them with SAS and Oracle, SAS alone, Informatica and Oracle, Oracle alone, and SQL Server.

I have also visited many companies as an adviser, consultant and user of their data warehouse. In these many visits, I have seen some successes and many failures. Often, the failures could have been prevented with some key guiding principles.

Data Warehousing or Enterprise Data Integration?

Data warehousing is now known by a new buzz word, Enterprise Data Integration. In fact, SAS recently renamed SAS ETL Studio as SAS Data Integration Studio (they also added some new features around the EDI area, one new feature was around continual data acquisition so that near real time data feeds are available in the data warehouse.) Another great part of SAS EDI is SAS Data Quality, this should be a consideration throughout the entire process, but I won’t directly comment about data quality in this post. Since most people still use the term data warehousing, so I will keep the popular terminology over the analysts and even SAS.

What Does it Take to Build a Successful Data Warehouse?

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