In the 38th session of Chandoo.org podcast, Let’s optimize data to ink ratio of your charts.
What is in this session?
In this podcast,
- Announcements
- What is Data to Ink Ratio?
- Obvious ways to optimize Data to Ink Ratio
- More ways to optimize Data to Ink ratio
- Highlighting what is important
- Conclusions
Listen to this session
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Links & Resources mentioned in this podcast
Recommended Books for creating better charts:
- Information Dashboard Design by Stephen Few
- Visual Display of Quantitative Information by Edward Tufte
Optimizing data to ink ratio – Charting case studies
- Closing gaps in gender equality chart
- Why 3D pie charts are evil…
- Visualizing world education rankings
Techniques for highlighting what is important
- Display alerts in dashboards to grab user attention
- Adding meaning titles & legends to charts
- Never show simple numbers in your dashboards
Invitation to “Becoming a better analyst” Webinar
As mentioned in the podcast, I am running my first ever webinar on Wednesday, July 15th – 2015 (2PM EST). The topic is “How to become a BETTER analyst?”
Please click here to register for the webinar & become a better analyst.
Transcript of this session:
Download this podcast transcript [PDF]
How do you optimize Data to Ink Ratio?
What about you? Do you worry about data to ink ratio when making charts? How do you optimize it? Do you have a process for it? Please share your tips by posting a comment.
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8 Responses to “Pivot Tables from large data-sets – 5 examples”
Do you have links to any sites that can provide free, large, test data sets. Both large in diversity and large in total number of rows.
Good question Ron. I suggest checking out kaggle.com, data.world or create your own with randbetween(). You can also get a complex business data-set from Microsoft Power BI website. It is contoso retail data.
Hi Chandoo,
I work with large data sets all the time (80-200MB files with 100Ks of rows and 20-40 columns) and I've taken a few steps to reduce the size (20-60MB) so they can better shared and work more quickly. These steps include: creating custom calculations in the pivot instead of having additional data columns, deleting the data tab and saving as an xlsb. I've even tried indexmatch instead of vlookup--although I'm not sure that saved much. Are there any other tricks to further reduce the file size? thanks, Steve
Hi Steve,
Good tips on how to reduce the file size and / or process time. Another thing I would definitely try is to use Data Model to load the data rather than keep it in the file. You would be,
1. connect to source data file thru Power Query
2. filter away any columns / rows that are not needed
3. load the data to model
4. make pivots from it
This would reduce the file size while providing all the answers you need.
Give it a try. See this video for some help - https://www.youtube.com/watch?v=5u7bpysO3FQ
Normally when Excel processes data it utilizes all four cores on a processor. Is it true that Excel reduces to only using two cores When calculating tables? Same issue if there were two cores present, it would reduce to one in a table?
I ask because, I have personally noticed when i use tables the data is much slower than if I would have filtered it. I like tables for obvious reasons when working with datasets. Is this true.
John:
I don't know if it is true that Excel Table processing only uses 2 threads/cores, but it is entirely possible. The program has to be enabled to handle multiple parallel threads. Excel Lists/Tables were added long ago, at a time when 2 processes was a reasonable upper limit. And, it could be that there simply is no way to program table processing to use more than 2 threads at a time...
When I've got a large data set, I will set my Excel priority to High thru Task Manager to allow it to use more available processing. Never use RealTime priority or you're completely locked up until Excel finishes.
That is a good tip Jen...