Month: November 2019

If you are trying to find what zipcodes are the ones with highest volume of sales, would the following image provide you the info

OR, would this one?

Both options show the same amount of marks, which is about 40K data points

It turns out that creating polygons to draw the zipcode boundaries is a bit more intensive than creating circles

I would minimize the number of sheets in each dashboard and ideally keep it to less than 5

Not sure how much more I can add, other than the fact that most of my dashboards are with sheets between 5 and 10. Having had many engagements providing the insipiration for these dashboards, I think my rule of thumb works pretty well

This is very similar to the filter conditions tip but there is a new concept here as well.

In today’s modern tech world, everyone expects more. Every one wants to be able to feel and touch their data. That is exactly what “Visual filtering” allows you to do.

you click on a data element and all the other reports are showing only contextual data.

Once your audience starts using visual filtering, the entire perspective on data changes.

Here’s an impact story:

One of my customers implemented a snr leadership facing dashboard and they recently conducted a meeting where not only did they use the Tableau dashboard to provide info, they decided to actively participate in some slicing and dicing of the data on the dashboard using “visual filtering”

This is the dream

One of the other execs in that meeting upon seeing the power of visual analytics

There are so many things you can do by setting up the right set of filter conditions on a dashboard.

• Use Visual Filters
• Use filter conditions that make the smallest SQL (think include or exclude)
• Use hierarchical filters instead of relevant filters when possible
• Use Wildcard filters vs. enumerated lists

Watch the video clip starting at 26:53

So, you got access to data, you know what you need from the data source and you are off to the races, building Tableau dashboards

EXCEPT, each one of your queries takes a long time to execute on the DB.

Here are some ideas around how to get past those issues:

1. If you are told that the DB is well tuned, but it is NOT giving you the performance you need, you need to take the situation in your hands, right? That’s when Tableau extracts can help you out.
2. If you are given a very complicated SQL that you don’t know how to reconstruct using Tableau visual joins, go ahead and plug in the SQL into the CUSTOM SQL view, and then extract the data
3. If you are told that the final dashboards needs to be on live data but you need to create dashboards while the DB is being tuned, extract thed ata
4. If you need to work offline on developing something, extract the data

As you can see there are many scenarions you need to extract the data, but for some reasons, the biggest reason that I STILL see in the field is that many databases are still not tuned for modern analytics that Tableau provides. In that case, we just need to extract the data out

Tableau extracts can make a huge different to your analytical queries as it is a columnar data store. If your data privacy and policies permit, consider using extracts instead of live database connections. Most definitely helpful during dashboard design phases even when final product needs to be based on live data

Here’s how

PRO TIP: Hiding unused fields from the data source can make a huge impact