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14 Misconceptions About Charts and Graphs

by Jorge

Misconception #1: A Better Chart Starts With… the Chart

Wrong. It starts by asking yourself if you really need one. Perhaps a statistical measure of some sort is good enough, perhaps you should use a table. If your job is to find patterns in a data set and build shared knowledge about it, what really matters is how efficiently the message is sent, and how efficiently it is received by the audience (two different things).

Misconception #2: You Should Master the (Technological) Tools of the Trade

No, you don’t. Just because you know how to create a chart in Excel it doesn’t mean that you know how to create a chart. If you use Microsoft Excel as your charting software then yes, you should learn more Excel (to spend more time with the kids). But you must go beyond technology, or else you end up creating some very stupid charts. Please note that a vast majority of Excel training courses will not teach you what it should (best practices). It will only tell you how to make “cool” graphs, like a 3D exploded pie chart.

Misconception #3: Defaults are good enough

They aren’t. Each chart must be tailored to the specific data set, audience and message. For instance, try to create a graph that clearly displays a large number of series and you’ll fail if you use the defaults (but can do it with clever color coding). And if you use recognizable defaults, like the Excel 2003 charts, you’ll look very, very, lazy (at best).

Misconception #4: Vendors obviously implement the very best templates

(I’ve heard this one recently, and I found it so incredibly naive that I had to write about it.) They don’t. About 90% of the Excel 2003 chart gallery is junk, and you must heavily reformat the remaining 10% to get something useful. Select other tools, like Crystal Xcelsius and the scenario is even worse. And I am unable to create in Cognos something that remotely resembles a chart (people tell me that version 8.4 is a little better).

Misconception #5: Better charts are just “prettier” charts

I hear this all the time. A good chart may look “prettier”, but that’s just an unintended consequence of a design that communicates better. In information visualization, prettiness must be a by-product of function. The very concept of a “better communicator” is sometimes difficult to comprehend, and trying to explain it is a waste of time, because people need to see it in action. You must take the user by the hand and guide him/her. You must force comparisons: “what can you learn about x using this chart?” and “what can you learn about x using that chart?” “how long did it take you to learn x using this and using that?”.

Misconception #6: It’s All About the Wow Factor

It is not. Many marketers and graphic designers fail to understand this. Marketers are hopeless in their relentless search for the wow factor and the eye-catching, “professional-looking” graphs, and graphic designers should know better, but they prefer to sacrifice data on the altar of Beauty (form is everything, data is a nuisance).

The dominant view among visualization experts (namely Tufte and Few) is that “form follows function“: every ornament in a graph should be eliminated, every object must serve a clear purpose, efficiency should be maximized (labeling series instead of using a legend, for instance). Given the extremely low graphic literacy levels among the general population, this may not always be the best approach.

Misconception #7: A good chart displays the actual values

No. If you label each data point you get a useless table over a useless chart. Labels are not only a distraction but often actually hide patterns in the data. Short labels and annotations can, and should, be used to identify or explain outliers or other interesting data points and circumstances. If your audience expects to see the underlying data then add a link to the table.

Misconception #8: Good Charts Should Be Read at a Glance

No, they don’t. The more complex, the longer it takes. It really doesn’t matter if it takes a second or an hour. What matter is how efficiently the graph  communicates. If a chart takes for ever to be read look for bottlenecks: the series are not easily identifiable, patterns are hidden, demands on the working memory are high, etc.

Misconception #9: The More Detail the Better

What we see as detail can be seen by someone else as clutter. Clutter is the natural child of loss aversion and is is very difficult to remove. If you have 12 competitors your audience will want to see the market share for each of them, even if it doesn’t make any sense. Tufte says “to clarify, add detail”, and yes, 12 competitors in a line chart can be made clear and useful, but you must know how to categorize them and provide a framework to help the user (you can use a large number of categories in a pie chart, for instance).

Misconception #10: It’s All About Selling Your Point, No Nuances

In The three laws of great graphs Seth Godin says that “there is no room for nuance [in a presentation]” and your charts should reflect that. Maybe it is just me, but I hate it when I am not allowed to draw my own conclusions because the data made available by the presenter is too biased towards his/her own points of view. Depending on the situation, a clear path that is supported by a lot of details is much better than a yes/no pie chart.

Misconception #11: You Have to Have Color, Lots of Color

Wrong. Color is a very difficult subject. Large surfaces of primary colors like we often see in presentations should be avoided because they are hard on the eyes and, because everything stands out, nothing stand out. A good option is to use grays for non-data elements like grid lines, and pale colors for color-coding. As a rule of thumb, color should always carry some meaning. Use primary colors to highlight a data point or some other small detail.

Misconception #12: A Single Chart is Enough

It is not. We live in an increasingly complex world, and traditional charts are very simple tools. While we wait for a new set of charts to be invented, we can use interaction (see below) and multiple charts to create a richer picture. That’s why scatter plot matrices, small multiples or trellis displays, and specially those multiple variations of executive dashboards are much more powerful than a simple chart.

Misconception #13: Charts Are Interchangeable

They aren’t. You can use a column chart or a line chart to display a time series, but while a line chart performs better than a column chart when reading trends, it is easier to compare data points using a column chart. Most visualization experts will tell you that you should use a bar chart instead of a pie chart (also because it is easier to compare data points), but a pie chart gives you the perception of a whole that is absent in a bar chart. Every graph has its own strengths, and you should select the one that suits your needs.

Misconception #14: Create It and Forget It

Don’t. Making sense of your data is a process of exploration and discovery. A pattern in a subset may be hidden by a noisy background. Different measures may lead to more complex insights. Creating a chart that the user can interact with should always be your primary goal. Unfortunately, that’s beyond the skills of an intermediate Excel user (if you what to learn about interactive charts my Excel dashboards may be a good starting point).

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This post lists 14 widespread misconceptions about charts, but probably is a very incomplete list and you may not agree with all of them. What misconceptions would you add/remove?

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[Update: Jon has been writing extensively about Excel 2003 and Excel 2007 (by the way, it's a great resource that helps us to see through the marketing noise). I said in the comments below that I prefer to use Excel 2007 charts to post images in this blog. He doesn't agree and he tries to prove in his last post that charts in Excel 2003 are actually better. He uses good examples to prove his point but I still believe that this (Excel 2007):

looks better than this (Excel 2003):

Yes, probably there is an "overaggressive anti-aliasing", but the line in Excel 2003 is too "crispy" for my taste. Again, it is just a matter of creating images for a blog, not exactly for serious work...]

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How to Make Better Pie Charts with On-Demand Details

by Jorge

This article goes much against conventional wisdom about pie charts (and doughnut charts) by answering these two simple questions:

  • Can we use a large number of categories in pie charts? (Yes, we can.)
  • Can we make a productive use of the apparently useless doughnut chart? (Yes, we can.)

Disclaimer (Sort of…)

Let me start by declaring this: I believe that the analysis of simple proportions is, by its very nature, very limited. It only scratches the surface of the data and it is useless for serious, decision-making processes.

A circular chart is poor because the underlying message is poor. If you can run a business using pie graphs to make sense of your data please let me know what market are you in, because I want to be there too (well, not really…).

Pie chart belong to the media and to some simple presentations. Leave them there. And don’t make the charts you see in the media your role model.

The part-of-whole issue

That said, one must recognize that proportions are so pervasive and hard-wired into our brain that escaping them is almost impossible.

A circular chart conveys perfectly the idea of part-of-whole relationship. You can’t use a bar chart to show this relationship because the whole just isn’t there! Yes, you can use percentage scales, yes you can say it in the title, but it isn’t the same thing, is it?

As I wrote in my previous post on loss aversion, each chart answers a question from a different perspective. Charts are not interchangeable.

Often pie charts are used just because they may look better (this is, of course, in the eyes of the beholder) but what the user really wants/needs to know would be better answered by a bar chart. This is a problem of graphic literacy and information management. It has nothing to do with the intrinsic qualities of pie charts.

The limit of 4 to 6 categories in pie charts

There is a widespread believe that you should not use more than four to six categories in a pie chart.

That’s is wrong or, at the very least, very incomplete.

In fact, you can use as many categories as you want, and still get meaningful insights from the chart. Problem is, you must know what to do with your data (graphic literacy and information management, again), and a large number of bad charts come from this simple fact: people don’t know what to do. Garbage in, garbage out.

“The Secret Strenght of Pies”

Here comes the fun part. In an article published back in 1991 by Ian Spence and Stephan Lewandowsky, titled “Displaying Proportions and Percentages” the authors write:

“the pie chart outperforms the bar chart for complicated comparisons, suggesting that the perceptual addition and comparison of components is inherently easier with the pie chart than the bar chart.” (emphasis added)

(By the way, the authors also say that this advantage will be lost if you “explode” the slices.)

Stephen Few, in his “Save the Pies for Dessert“, cites this article and writes about “the secret strength of pies”:

It is not difficult to believe that it is somewhat easier to sum the areas of slices in a pie than it is to imagine the combined heights of bars stacked on one another.(…) Regardless, the fact remains that a comparison of two sets of summed parts is rare in the real world. But, by all means, should you ever need to display data for this purpose, a pie chart would serve you well.

Please note that Stephen Few, in his highly regarded book “Show me the Numbers” says:

“I don’t use pie charts, and I strongly recommend that you abandon them as well.”

Few acknowledges that pie charts “could serve you well” in a very limited set of circumstances (“a comparison of two sets of summed parts is rare in the real world”).

Is it really rare? It may be, but that’s because people don’t know what to do with their data (again). Let’s see.

You have 10 or even 20 categories and you want to use them all (your loss aversion tendency?). Because 20 ungroupable categories are rare in the real world, you should be able to visually group them, using a color (hue) for each group and a different saturation for each category. By doing this, you are adding layers of detail, and the reader will be able to select the level of detail that suits his/her needs. This works best when using an interactive chart because you don’t have to label everything (just use your mouse to identify on-demand the more relevant detail categories) but even a static chart can be used (in this case, label only the relevant details).

The Consumer Expenditure Chart

I used this methodology to design the consumer expenditure chart above, with living expenditure (on the right) and discretionary expenditure (on the left).  As you can see, living expenditure accounts for almost 60% of the total. That’s something you can’t easily see with a bar chart.

Then, there is a second level of detail, where you have categories like Housing (more than half of living expenditure) or Transportation. And finally, you could use your mouse to identify those detailed categories in the outer gray ring.

I’ve added some arcs to compare the profile of total consumer units to consumer units with five or more persons. Each arc always starts at the same degree of the corresponding slice. Different proportions lead to gaps or overlaps. Please note that this is not a core feature of this chart. Just wanted to play a little with comparisons (an obvious issue: since the first arcs are closer to the center, a gap between them is different than a gap between the last arcs).

The Secret Strength of Doughnut Charts

As we saw above, pie charts are better than bar charts when comparing proportions. But, as soon as you add a second pie chart you are trying to compare proportion A1 with proportion A2, not proportions A and B of the same pie. There is a shift in the analysis and the pies become useless (use bar charts instead).

Just because you can merge both pie charts in a single doughnut chart it doesn’t mean that you gain efficiency, because the essential problems remain in place.

For many, a doughnut chart is a bad mutation of a bad chart. But if, just if, two bad’s become on good? Could a doughnut, if correctly use, become a kind of pie chart on steroids?

Let me emphasize this: never use a doughnut chart to compare series. I don’t, and I strongly recommend that you should avoid it as well… Always use a doughnut chart to add detail to a series. That’s the secret strength of doughnut charts.

And please, please, could someone write an article on doughnut charts for the English Wikipedia?

I made this chart in Excel

In case you are wondering, you can make the Consumer Expenditure chart in Excel, 2003 or 2007. Instead of the default theme colors, I used some of the colors that will be available in Chart Tamer (thanks, Andreas!).

Conclusion

Pie charts do not deserve their bad reputation. They seem to be more efficient than bar charts in some very specific tasks, like  comparing combined proportions. We should take advantage of that by adding multiple levels of detail. We shouldn’t be afraid of using a large number of categories, provided that those levels of detail are clear and meaningful.

The doughnut chart is the most misunderstood of our chart toolbox. It is seen as completely useless because two series should not be compared using circular charts, but that’s not what doughnut charts should be used for. They should be used to extend the power of pie charts, managing efficiently the level of detail that we need to add to create more insightful charts.

Is this a good way to use pie and doughnut charts? Please share your thought in the comments.

[Update: If you want to know how to create this chart (with a bonus hole-remover...) Jon has a detailed explanation here.]

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Chart Design: Abortion Ratios 1980-2003

by Jorge

Abortion ratios 1980-2003 by race, marital status and age

Source: U.S. Census Bureau (original Excel file). The abortion ratio is defined by the number of abortions per 1,000 abortions and live births.

(Click to enlarge)

Notes:

1. We know that information visualization is all about pattern detection. But often our design choices hide relevant patterns behind the obvious one(s). Take this panel, for instance. Everyone can see the downward pattern, but what about the U-shaped pattern across age groups? You can see it, right? Well, follow the usual path and you’ll miss it.

2. A ratio (or a growth rate) is something that should always be put in the context of actual volumes or proportions. There is no “best answer” to link both dimensions but the panel displays a reasonable solution. As you can see, the abortion ratio among women less than 15 years old is very high but its proportion in the total number ob abortions is almost residual. On the other hand, the ratio in the 15-19 age group may be lower, but it is much higher than the average ratio and accounts for around 17 of the total abortions. Whenever possible, you should keep these two measures close together.

3. There are seven age groups in this data set. Put them all together in a single line chart and you’ll miss the pattern across groups, as discussed above, but you’ll also have a hard time disentangling the whole thing. Before creating the chart always ask: what is my specific question? This will help you to prioritize and create a focus-context display. If a series answers your question (need-to-have) add it to the chart and color-code it. If a series is interesting but doesn’t directly answer your question (nice-to-have), you may add it to the chart to provide context, but gray it out and delete if from the legend, if you have one.

Please let me know what you think and suggest ways to improve the panel.

[Update: Jon discusses the process of pattern discovery in RE: Abortion Ratios 1980-2003. Andreas adds several good suggestions and shows how to display the date in a more consistent small multiples chart.]

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Performance vs flexibility in Excel: Demographic Dashboard Lookup Edition

by Jorge

I am sure Albert Einstein was thinking of Excel dashboards when he said “everything should be made as simple as possible, but not simpler”. Let me tell you why.

Demographic Dashboard: The VBA edition

I published some time ago a first version of my Demographic Dashboard to show how an average Excel user could design a dashboard. This version is relatively complex, because it uses pivot tables and some recorded macros to manage the data.

Demographic Dashboard: The VBA-free edition

Then, partially because of a discussion around the use of VBA, I launched the VBA-free edition. It still uses pivot tables but I managed to remove all the VBA. Although I believe that VBA is a powerful tool that should be used when needed, many users are not comfortable with a programming language (not even in the simple form of recorded macros), so this version was designed to address their concerns.

Demographic Dashboard: The Lookup edition

Prior versions use a pivot table connected to an external data source, but I believe that a large majority of Excel users are not aware of pivot tables and how powerful and helpful they can be. Wouldn’t it be nice to just copy/paste into the Excel sheet a simple flat file and design the dashboard around it? Isn’t that standard procedure for these users?

So, by popular demand (sort of) I decided to get rid of the pivot table and design a new version. No VBA, no pivot tables. Just a simple data table and some lookup functions.

Let me tell you this: If you want to follow this path, don’t. It can be really complicated. And creating or updating a data set in Excel by copy/pasting the data is unreliable, dangerous and can seriously compromise your dashboard. But if you need that adrenaline boost go ahead, try it!

Before that, let me share with you some interesting stats. Since I have three versions of a very similar dashboard it make sense to compare performances, so I implemented a simple metric: how long does each dashboard take to cycle through all the 55 time periods? Here are the results in my new computer:

  • VBA version: 15 seconds;
  • VBA-free version: 45 seconds;
  • Lookup version: 2:15 minutes.

This is not exactly unexpected. If you need a (very) long formula to calculate something that you can easily get using a simple GETPIVOTABLE formula performance will drop sharply. On the other hand, the file size of the Lookup version is half the size of versions using pivot tables. If you are planning to email it this could be an option.

Get the Demographic Dashboard Lookup

The VBA-Free and the Lookup Editions of the Demographic Dashboard are bundled with the Demographic Dashboard Tutorial. If you want to create powerful Excel dashboards I am sure this tutorial can help you.

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Creating a VBA-free Excel dashboard

by Jorge

VBA free Demographic DashboardDo you need VBA to create an Excel dashboard? Our recent discussion clearly shows two lines of thought: a) you should use it because there are things you just can’t do without VBA and if you have access to a powerful tool you should put it to work; b) you should avoid VBA like the plague because the average user don’t understand it and it can cause a serious problem if the programmer leaves the organization.

In an informal survey among friends and colleagues (all of them Excel users), I’ve discovered that 55% doesn’t know what VBA is, 40% knows but doesn’t want to use it, 4% uses recorded macros from time to time and 1% actually edits the recorded macros to add some sort of functionality (well, this happens to be me…).

The real world always depresses me…

The poll on the right seems to tell a better story, but you guys are le crème de la crème, so I can’t use you as a representative sample, I’m sorry…

The first version of my Demographic Dashboard uses some macros to synchronize pivot tables and to add some functionality to the user interface. Simple stuff, really. But there is a divide, and I had to know if I could create a VBA-free Demographic Dashboard.

That’s a the simple story behind the VBA-free Demographic Dashboard. This dashboard uses the same data set (population by sex, age and country for the period 1996-2050) and the final result is similar (that was the idea). But how can you achieve the same results without VBA? Just simplify, simplify, simplify. Let me give you some examples.

Dashboard objects

free-vba-xySome times we use VBA out of laziness. Take a look at this object: I could place the background image on the sheet and use a few lines of code to correctly place the small triangle. That was my first idea. But why? Why don’t you just use a chart? It is simpler, safer and doesn’t require VBA. The final solution uses a simple scatter plot. Yes, I know, it doesn’t look like one. It’s amazing the things you can do with scatter plots.

free-vba-population-pyramid I already published a screencast on how to create a population pyramid, but this technique is missing. I wanted to recreate a popular chart format in magazines: lines to encode the current data and areas for the reference data.

I was unable to come up with an elegant solution using the standard available formats in Excel, so I had to improvise. This is a regular area chart, but I use the camera tool to rotate the image to achieve this effect.

Please note that you shouldn’t use this technique if you are planning to print your dashboard because of a well known bug in Excel (but there is also a workaround).

In the previous version of the dashboard I used lines in a scatter plot to display current and reference data. No need for the camera tool.

free-vba-xy2

I really like this chart. It is amazing how it shows the aging process that currently affects every country in the world, specially if you can animate it to see how the passage of time changes the dependencies. As an European I find it really scary…

In this version, the active country is shown in the context of the active region and, unlike the previous version, it also shows the remaining countries, so the user can see the region in context.

I usually delete grid lines, but here they are almost invisible and they actually seem to help. Sometimes I forget that scatter plots are square by definition, but not in this case…

free-vba-top-ten I don’t have a single post discussing sparklines, partially because I like to stick to things that everyone can do with a standard Excel installation (and you’ll usually need an add-in to create sparklines) and because there is so much to say about them that I wanted to write a complete series, and I don’t seem to find the time to do that. But you should definitively consider using them when planning a dashboard.

What you see on the left are not exactly sparklines, just a small line chart where each line is aligned with the country name. I kind of like these lines jumping out of their “natural borders” (the row limits and the table itself). Download the dashboard and compare these lines with the top ten countries in Europe, for example.

free-vba-links

Lately I’ve been playing a little with links to external sources and I decided to add these two. The first one opens a Google Map with the active country and the second one opens the CIA Factbook for the same country. You don’t need to hard-code the links, it is just a string that is automatically changed whenever you change your data (in this case, the name of the country). You can also put the data into your worksheet, like I did when I created my Excel thematic map.

free-vba-country

There is something that you can’t do without VBA. When you select a region, the list of countries automatically reflects that change, but the current country is not changed. A simple macro can easily select a new value when a different region is selected. So, you may be comparing Angola with countries in Europe (Angola is in Africa). Since I can’t change the default country, I added a conditional formatting for that cell, and when the country is not found in that region the background changes to warn the user.

Get the VBA-Free Demographic Dashboard

The VBA-Free edition, as well as the Lookup edition are bundled with the Demographic Dashboard Tutorial, a step-b-step video tutorial that will certainly boost your Excel skills. Check it out!

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How to create a dashboard in Excel

by Jorge

Excel dashboards and executive reports are powerful, fairly easy to design and a great ways to improve your Excel and data visualization skills. Because of its flexibility, you can virtually design any dashboard in Excel exactly the way you, or the users, imagined (and then send project to IT for implementation).

Once you know what will the Excel dashboard be used for and what kind of metrics users expect, there are three major areas that you must address when creating it:

  • how to bring data into your spreadsheets;
  • how to manage the data and link it to the dashboard objects, like charts and tables;
  • how to design the dashboard report.

Let’s take a look at each of them.

How to bring data into your Excel dashboard

Yes, Excel is a very flexible tool, but to create an Excel dashboard you can’t just paste some data and add a few charts, can you? You must maintain and update it, and if you want to decrease the cost associated with that tasks you must impose some structure to your data.

Usually the data will (should) not be entered directly into the spreadsheet. This means that you must copy /paste the data from the source (not wise, really) or connect the spreadsheet to the data source. There is a standard way to communicate with external databases called ODBC. You can use it to connect your dashboard to a table in Access or Oracle, for example.

Once you have that connection established, every time the data changes at the source it also changes in the spreadsheet, after refreshing. Keep in mind that a good practice is to minimize the amount of data you bring into your spreadsheet. If you don’t need it, don’t use it. However, if there are calculations to be performed, try to do them at the source.

Data can be imported using two basic structures: a flat file and a pivot table. As a rule of thumb, I would say that a pivot table tends to create larger files but the calculations will be faster. The flat file will be smaller but, if you need complex formulas to select the data, performance will suffer (you must test this in your own project).

How to manage the data and link it to the dashboard objects

If you use pivot tables you can extract the data using the GETPIVOTDATA function. If you use a flat file, there is a vast array of formulas you can use, like database formulas (DSUM, DGET), lookup (VLOOKUP, MATCH, INDEX) or math formulas (SUM, SUMIF, SUMPRODUCT).

Having many formulas and calculations is dangerous for your dashboard integrity. You can decrease this by using pivot tables. That’s one of the reasons I choose pivot tables by default.

Named Ranges

Do I have to tell you that you should document your work? Ok, I’ll tell you: yes, you should. But you can simplify that boring task by using named ranges. I am sure this is a familiar example: “=Sales-Expenses” is better than “=$G$4-$H$5”.

Named ranges not only help you to make your formulas cleaner and easier to read, but they also play a key role in data management for Excel dashboards. Example: let’s say that the next update adds a new column to your table. If you don’t use named ranges you’ll have to change every single formula and add that extra column. With dynamic named ranges, the extra column is immediately available. This means less, but more reliable work.

Dynamic Charts

I am a truly believer in dynamic charts, and dashboards that the user can’t interact with doesn’t make much sense to me. All my dashboards have controls to change markets, change products, change sales territories, whatever. What is important is that the user must be able to interact with the dashboard and build his/her knowledge by exploring the available data.

If you are creating interactive charts you will probably need dynamic ranges. You can do it using the OFFSET() function.

Macros

OK, you are not a programmer (I am not) but you should consider using some macros to speed up some repetitive tasks. Also, some cool things that you can add to your dashboards can only be done using macros, like greeting the users and selecting the right profile when they open the dashboard (you may want to read the discussion around the use of VBA in Excel dashboards).

How to design the dashboard report

If you like the sort of charts that you find in marketing-oriented tools like Crystal Xcelsius let me tell you this: they don’t work. There is a first wow! and then their uselessness starts creeping in.

This is not only a matter of opinion: I tried (and failed) to replicate my Demographic Dashboard (see below) using Xcelsius and detailed the process in a series of posts (1, 2, 3, 4, 5), so I know why these tools don’t work. Don’t try to use Excel to mimic them.

Excel 2003 chart defaults are ugly and you should avoid them like the plague (and make sure you select the acceptable formats). Throughout this blog you’ll find many charting tips, and tips to improve your Excel dashboard, so I will not elaborate much on this. The basic premise is this: a chart is used to discover actionable patterns in the data, and you should do your best to show those patterns.

This means that you should remove everything that has no added value (3D effects, backgrounds, etc), create a hierarchy of focus and context data (make relevant data stand out, using color carefully) and add at least a basic form of interaction (let users select the series, for example).

Books

The reference in dashboard design if of course Stephen Few’s book, Information Dashboard Design. Other recommended reading are John Walkenbach’ Excel Bible (2003 or 2007) and Excel Charts (2003 or 2007); Tomothy Zapawa’ Excel Advanced Report Development (2003 or 2007); Michael Alexander’s Excel 2007 Dashboards and Reports for Dummies and Jon Peltier’s blog.

An Excel Dashboard Example: The Demographic Dashboard

This is an example of dashboard reporting using many advanced functions:

As I said above, it is difficult sometimes to put everything to work together in an Excel dashboard project. With the Demographic Dashboard, I wanted to show how to connect the dots and create a fully functional dashboard. So, I used a very interesting dataset from the US Census Bureau (population by sex, age and country, 1996-2050) to design it.

Actually, there are three dashboards… I took advantage of Excel’s built-in flexibility to create three versions of the same dashboard. They all look the same, but each one uses a different set of techniques:

  • The VBA version: This was the first one. It uses Visual Basic for Applications and pivot tables. It is faster than the other ones, but it relies on VBA to perform some tasks, and that may put off some users (I’m just using recorded macros, nothing too complex);
  • The VBA-Free version: After some discussion on the merits of using VBA (Excel Dashboards: Do You Need VBA?) I decided to add a VBA-free Excel dashboard; It mainly uses pivot tables and the powerful GETPIVOTDATA function. It is a well balanced version: it is slower than the previous version but since it doesn’t use VBA some users don’t have to leave their comfort zone;
  • The LookUp Table: It uses a spreadsheet database (basically a table that you paste into a sheet) and lookup functions (VLOOKUP, MATCH, OFFSET) to get the data for the dashboard report. Personally I don’t feel very comfortable with spreadsheet databases (more maintenance, more error prune, slower) but it is a good starting point to learn advanced Excel techniques (and sometimes is it your only option).

For a deeper discussion on the pros and cons of each version you may want to read the post Performance vs flexibility in Excel: Demographic Dashboard Lookup Edition.

Wrap-Up

Excel is a great tool for any type of executive reporting. It allows for fast, flexible and cost-effective dashboard implementation (or prototyping). Intermediate users should be able to design and implement an executive dashboard in a matter of days (or even hours, for the first draft), provided that they know how to select the right techniques.

An Excel Dashboard is the Perfect Excel Learning Tool

You can read a million tips and still miss that wow! moment when you see all those small pieces working together. That’s why designing a dashboard is one of the best ways to learn Excel. If you want to get the details but also the whole picture, get my Demographic Dashboard Tutorial and start your Excel wow moment now!

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