June 15, 2012 | By Rey Villar
Recently for a client, we hooked up Tableau to their financial dataset. We had used Tableau before with other data analysis tasks so we thought it was appropriate for this task. The dataset was mostly orders, fee schedules and other types of financial master data. Ajilitee had built a managed analytics data warehouse for this client–outsourced data warehousing. We had been investigating some data quality issues for some time and were wondering how Tableau could help us spot some issues. The data warehouse runs our analytics platform and uses Microsoft Analysis Services to power dashboards on Sharepoint.
I cannot go into the details of what we found but we did want to comment on Tableau. We used the desktop version as we had in the past. Its an easy install and it was up and running, connected to our datasource in around 5 minutes (it took an additional 5 minutes to locate it on the Tableau website fill in the web form and download the software–so 10 minutes in all).
We were immediately productive in the tool. We dragged and dropped dimensions and facts onto the workspace. Data instantly updated–it was extremely fast. The datasets were not massively huge like the type Ajilitee often works with but they were not tiny either. The initial data did not reveal what we thought existed in the datasets so we needed to filter down. We could easily drag dimensions to the filter boxes and select what to filter on. Using this simple approach, we drilled down to the area of concern and identified some issues we thought existed. We were mostly doing categorical (nominal) data analysis with a numerical metric (such as revenue or order counts).
In 30 minutes we finished our task.
That’s 30 minutes from start of analysis to insight and understanding. We’ve done a lot more with Tableau but we thought we would call this out as a nice cycle of interaction with a product that delivered what we wanted.
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