🚨 YOU'RE VISUALIZING YOUR DATA WRONG. And Here's Why...

[Music] the sad truth is I see it all the time and to be honest when I started building dashboards many moons ago I was definitely guilty of this as well what am I talking about well it's when dashboard designers prioritize style over substance or to put it another way design over data they try and make their dashboards look cool by incorporating unnecessary Design Elements while at the same time completely ignoring the basic fundamental principles of data visualization because after all effective data visualization is less art and more science as I said way back in 2010 when I was starting out I used to try and impress clients by making their dashboards look as cool as possible until someone recommended to me a book that once I'd finished it completely changed the way I designed dashboards from that point on in this video I'm going to break down the biggest takeaways I got from that book as well as other things I've learned since to help you better visualize your data and build better dashboards let's jump in foreign [Music] bi online with me Adam feiner helping you do more with data so the book in question is called the visual display of quantitative information it's by the author Edward tuft who is widely considered to be one of the Godfathers of modern data visualization and original dataviz OG if you like it's a fantastic book and even though it was first published in 1983 well before the Advent of bi tools what it teaches can easily be applied and adapted to Modern interactive dashboards so right at the beginning of the book Edward tuft sets out eight fundamental principles of data visualization that contribute to what he calls graphical Excellence they are as follows show the data so basically visually representing sets of numbers that otherwise would be hard to understand properly he refers to something called anscom's quartet which is a group of four data sets that have nearly identical simple descriptive statistics yet have very different distributions and appear very different when visualized induce the viewer to think about the substance rather than about methodology graphic design the technology of graphic production or something else essentially avoid anything that will distract the viewer from understanding the visualized data so this is what I referred to earlier when talking about making a dashboard look cool sometimes in an effort to do that you actually end up distracting the viewer from the whole point of the dashboard in the first place the data in a minute I'll talk about ways to avoid this pitfall number three avoid distorting what the data have to say data that's visualized perhaps with the intention of trying to tell a different story or misleading the viewer is essentially lying about the data common way this is done is by setting the y-axis value of a graph at a point greater than zero to exaggerate the significance of certain data number four present many numbers in a small space essentially the whole point of data visualization make large data sets coherent there simply is no better way to make sense of large data sets than with visualization but the key word here is coherent it's actually easier than you might think to visualize data poorly perhaps by using an unsuitable visualization or too many different nonsensical colors encouraged the eye to compare different pieces of data essentially all data visualizations should be comparing data to help the viewer understand it better a single figure or data point on its own without any context has limited usefulness it's only when you include other data that it can be better understood through comparisons and correlations the implication here is that the comparisons should be made as easy as possible for the viewer to see reveal the data at several levels of detail from a broad overview to a fine structure the visual display of quantitative information was first published in 1983. long before the arrival of bi tools and even a couple of years before the first release of excel so data visualization has come a long way the arrival of dedicated bi and dashboarding tools makes revealing the data at different levels much easier to achieve through interactivity options you can include in dashboards allowing the viewer to filter and drill down into the data to see it at different levels serve a reasonably clear purpose description exploration tabulation or decoration when it comes to dashboards I feel it's important for each one to maintain a clear objective so in practice you should avoid presenting data from too many different data sources on the same dashboard unless it serves the dashboard's objective and narrative and if you do they should be clearly distinguishable if you're new to data visualization I'd recommend you print off these eight points and keep them close by so you can refer to them while you're building your dashboards and reports so how are you visualizing your data wrong what are the common mistakes how do you avoid or correct them probably the best way or at least the starting point is to consider what Edward tuft calls the data to Ink ratio so the data doing ratio is the total amount of ink used to print the data in the visualization the lines on a Time series chart the bar and a bar chart all the figures in a table divided by the total amount of ink used to print the whole visualization he also describes it as the proportion of the Graphics Inc devoted to the non-redundant display of data information there can also be redundant data ink that should be subject to one of Tufts two erasing principles aimed at increasing the proportion of data Inc ink that fails to depict statistical information does not have much interest to the viewer of the graphic in fact sometimes such non-data Inc clutters up the data and second redundant data ink depicts the same number over and over if we look at this simple graphic how many times can you see the same information displayed I'll give you five seconds well if you guessed six times you'd be correct here they are [Music] so we can remove five out of the six and leave just the column color now let's look further at redundant graphic ink and see how erasing it can improve data visualization here's a Time series chart let's analyze its data to Ink ratio which elements are the non-redundant data Inc and which might be considered redundant starting with the chart title do we even need it does the chart itself not already communicate to us what the data is that we're looking at through other elements like the axis titles is the big blue bar not distracting and drawing the eye away from the data I'd say yes in which case I'd remove it or at the very least remove the blue and make the title less overpowering next do we need a border around the chart no so we remove it this Legend here do we need this as well it's actually information that appears three times in this chart once in the legend once in the y-axis title and once in the chart title so we should choose to keep just one of them we'll start by removing the legend and then we have another choice to make do we keep the chart title or the axis title either is fine but I'm going to remove the y-axis title and keep the chart title while we're at it why not just remove the x-axis title as well what purpose does it actually serve the chart title says we're looking at sales over time we're using a Time series chart do we really need to have an axis title stating that we're looking at a date when the values on the axis show this clearly I'd say no but only on the Proviso that there are not multiple different dates in the data set and we need to know which one we're using for example order date ship date Etc in this case we can remove it let's stick with the x-axis for a second do we really need to display so many date values or can we reduce the number of values and therefore the data to Ink ratio how about this this or even this okay maybe that's going too far it might be okay if you're dealing with a shorter period of time like the last 28 days or just when you have fewer values in the time series for this particular chart I think it's helpful to see where the Years start so let's go back a step what we can see when we reduce the number of values on the x-axis is that we also reduce the number of vertical grid lines which is removing even more non-data ink I think we can do the same for the y-axis let's reduce the number of values to four that looks better so some data visualization purists might even suggest you remove grid lines altogether like this but personally I think having at least some guide to see where values far apart on the series are in relation to a particular level is quite helpful so what I would do is actually modify the grid line colors or reduce their opacity so that they're not so prominent like this if we compare where we started to where we are now we can see that it's a vast Improvement the data takes center stage and the remaining ink is there to Aid the comprehension of that data so what else counts as redundant ink on a chart or dashboard well things like Drop Shadows on borders making graphs 3D unnecessary images and sometimes even things like borders on kpi visualizations aren't really necessary just ask yourself is it a design element that's helping to present the data more clearly and effectively or does it serve no purpose and is purely an aesthetic element [Music] something I see quite a lot when helping clients build their own dashboards is that they tend to make their charts and graphs too big real estate on a dashboard is at a premium so you need to optimize the space you have the purpose of your visualization is to communicate the data effectively what you should really try and do at least when space is limited is to reduce the size of your chart or graph until you really can't understand the data in it then increase the size back a bit you see still legible the takeaway is that it doesn't need to be big to be understood here I think now that we've reduced the chart size we can also reduce the axis label font size as well this being said if space permits you're free to make your charts as big as you like color so important to use it correctly although it's only discussed briefly by tuft in the visual display of quantitative information I'll give you my take personally I like to keep things simple when it comes to color don't use too many when the visualization doesn't call for it and especially don't use bright gaudy colors that distract from the data obviously there are loads of cases when you should or need to use different colors in the same chart for things like heat maps and the segments on a pie chart you should make sure for example that when you are presenting the same Dimension values in different charts of a report you maintain consistent colors otherwise you'll just end up confusing the viewer basically when considering the use of color it needs to Aid the graphic and not just be a simple design element for design Sake One semi-exception to this is that you can in certain circumstances Be Clever in your use of color by using different ones for different data sets so blue for sales data red for social media data Etc this can help the viewer to better read and understand a dashboard [Music] oh does Edward tuft not like pie charts not at all he actually calls them dumb the full quote is a table is nearly always better than a dumb pie chart the only worst design than a pie chart is several of them for then the viewer is asked to compare quantities located in a spatial disarray both within and between pies he's actually referring to this kind of map visualization and I kind of agree but then I asked myself isn't this really showing data at different levels one of the eight points he sets out at the start of the book for me when you're looking at something like this in a dashboard your primary focus should be drawn to the size of the pie charts on the map that will indicate a primary metric then the pie charts allow for a close inspection of the data by a secondary Dimension I.E at a different level I disagree that pie charts are completely dumb and I'll explain my thinking pie charts are used to display the parts that make up a whole 100 represented by the 360 Degrees off the circle for me there is no other visualization type other than perhaps the tree map that achieves this you can't visually represent how say a set of five values makes up 100 using The Columns of a bar chart or the dots on a scatter plot for example yes you could use stacking to represent 100 but then would you want to use a single column to display the share of a whole of these five values it wouldn't be my choice the other reason I don't think that pie charts are dumb is that people intrinsically understand the concept of a pie and cutting a pie up into Parts it's instantly recognizable and understood it just makes sense to us so when you're working with an at a glance dashboard a pie chart gives the viewer something that's understood very quickly without the need for much further inspection other people might argue that where the segments of a pie chart are quite close in value and it's hard to distinguish which has the greatest share it makes no sense to use one and that the small differences between the values might be better expressed with something like a column chart or a simple table and I would agree to an extent but I also think that this is missing the point of a pie chart altogether what I said to begin with a pie chart is meant to display 100 and if the parts are equal in their size this tells you that the parts are equal in their size you have effectively communicated the distribution of values making up the whole so now that I've explained why I think pie charts aren't done there are caveats I always recommend that you only use a pie chart when you have no more than say five values to display otherwise it gets too complicated to read and where possible consider using a donut chart which is essentially a pie chart with a hole in the middle because they use less ink as I said earlier the visual display of quantitative information was written in the 80s before business dashboards as we know them even existed which means that it doesn't cover dashboard design principles there's actually a video on my channel where I talk about my 7-4 design Essentials that do in fact incorporate elements from tuft so if you want to check that out here it is but before you do if you've got value from this video I'd really appreciate it if you could give it a thumbs up and maybe leave a comment what do you think of pie charts thanks so much for watching this video and I'll see you soon in the next bye

Channel: Adam Finer - Learn BI Online

Views: 25,019

Keywords: data visualization, storytelling with data, data visualization tips, data visualization design, what is data visualization, data visualization examples, data visualization best practices, data visualization techniques, tableau data visualization, data visualization tableau, data visualization provide a context, data visualization storytelling, data visualization using tableau, business intelligence, bi dashboard, dashboard design tutorial, dashboard design, dashboardard design

Id: 5pCdw-PV0Js

Length: 17min 10sec (1030 seconds)

Published: Sun Mar 19 2023