I’ll walk through some of the components here in detail, but, first, a handful of key points:

• There is no redundant information — it’s not uncommon to see dashboards (or reports in general) where there is a table of data, and that table of data gets charted, and the values for each point on the chart then get included as data labels. This is wasteful and unnecessary.
• Hopefully, your eyes are drawn to the bold red elements (and these highlights should still pop out for users with the most common forms of colorblindness — I haven’t formally tested that yet, though) — this is really the practical application of the vision I laid out in my Perfect Dashboard post.
• I have yet to produce a dashboard solely comprised of these widgets — there are always a few KPIs that needs to be given more prominent treatment, and there are other metrics that don’t make sense in this sparkline/trend/current format
• I do mix up the specific measures on a dashboard-by-dashboard basis. In the example above, showing the past two years of trends by month, and then providing quarterly totals and comparisons, makes the most sense based on the planning cycle for the client. But, that certainly is not a structure that makes sense in all situations.

And now onto the explanation of the what and why of each element, working our way from left to right.

Metric Name

This one hardly warrants an explanation, but I’ll point out that I didn’t label that column. That was a conscious decision — the fact that these are the names of the metric is totally obvious, and Edward Tufte’s data-ink ratio dictates that, if it doesn’t add value, don’t include it!

Past 12 Months Sparkline

The sparkline is another Tufte invention, and it’s one that has really taken off in the data visualization space. That’s good, because sparklines are darn handy, and the more people get used to seeing them, the less there will need to be any “training” of dashboard users to interpret them. Google Analytics has been using sparklines for a while, even, so we’re well on our way to mass adoption!

One tweak on the sparkline front that I came up with (although I’m sure others have done something similar): I add a second, gray sparkline for either the target or the prior reporting period. I like that this gives a quick, easily interpretable view of the metric’s history over a longer period — has it been tracking to target consistently, consistently above or below the target, or bouncing back and forth? Is there inherent seasonality in the metric (signified by both the black and gray sparklines having similar spike/dip periods)?

One limitation of sparklines is that they don’t represent magnitude very well. If, for instance, a particular metric is barely fluctuating over time, then, depending on how the y-axis is set up, the sparkline can still show what looks like a wildly varying value. It’s a minor limitation, though, so I’ll live with it.

4-Month Trend Arrow

The 4-month trend is the single icon that results from a conceptually simple (but a little hairy to calculate) assessment of the most recent four data points. That was the punchline of an earlier post on calculating trend indicators. Whether the basis of the trend is months, weeks, or days can vary (not within one dashboard, generally, but as a standard for the dashboard overall), as well as whether it’s 4, 5, 6, or more data points. It’s a judgment call for both driven by the underlying business need that the dashboard supports.

I promise, promise, promise to make a simplified example of this arrow calculation and post it in a future post — check the Comments section for this post to see if a linkback exists (I’ll come back and update this entry as well once it’s done)

Current

Typically, when sparklines are used, the exact value of the last point in the sparkline is included. In the example above, I’ve done something a little different, in that I actually provide the sum of the last three data points. This is a quarterly dashboard, but the sparkline has a monthly basis to it to show intra-quarter trends. If the current value is sufficiently below the target threshold, then the value is automatically displayed as bold and red.

There are certainly situations where “Current” would actually be the last point on the sparkline. Like the trend arrow calculations, it’s a judgment call based on the business need that the dashboard supports.

YOY

In the example above, there is a comparison to the prior year. But, this could be a comparison to the target instead. Target-based comparison is even better — straight period-over-period comparisons tend to feel like something of a cop out, as prior periods really are more “benchmarks” than true “targets.” Now, setting a target as something like “15% growth over the prior year” has some validity! That would then impact both the gray sparkline, the “when does Current go bold red,” and this %-based calculation.

28 Data Points

In the version of the widget above, there are 28 unique pieces of data presented for each metric: the metric name (1), the black sparkline (12), the gray sparkline (12), the trend indicator (1), the current value (1), and the year-over-year growth percentage (1). And that’s not counting the conditional formatting that highlights values as bold and red when certain criteria are met. That’s a key aspect of the widget design. 28 sounds like a lot of data to represent for a single metric. Yet, they seem pretty digestible in this format, don’t they?

Let me know what you think. Does this work? What doesn’t work?Similar Posts:

• Calculating Trend Indicators
• Dashboard Design Part 3 of 3: An Iterative Tale
• Dashboard Design Part 1 of 3: An Iterative Tale
• Social Media Measurement: A Practitioner’s Practical Guide
• Four simple rules for identifying a good metric

© Tim for Gilligan on Data by Tim Wilson, 2009. | Permalink | 3 comments | Add to del.icio.us
Post tags: sparklines, trend indicators

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Several weeks ago, one of the presenters in a meeting showed some data as a map overlay. As soon as she projected the first map, someone in the meeting quipped, “Good luck understanding this one, Jim!” Jim, you see, is colorblind. And, apparently, most of the people in the meeting knew it. Approximately 8% of men have some form of color blindness (it’s much more rare in women — only 1 in 200). And the overlays on the map were color-coded very subtly. Jim commented that it was hopeless!

As it happened, I was exploring a fresh set of data that same week, as we’d recently rolled out some new customer data capture capabilities. As I worked through how best to present the results, I decided to grab a colorblind-friendly palette from the web and use it in the visualization of the information. I’d hoped to find a site with one or more Excel files that I could download with such a palette, but, worst case, I was prepared to snag a palette and manually update my Excel file (for future sharing on this blog, of course!).

No. Such. Luck!

What I did find was a slew of information on the different types of color blindness (which I’ll touch on briefly in a bit), as well as a bevy of almost-useful tools and palettes:

• How to make figures and presentations that are friendly to Colorblind people — ultimately, I used the palette that is ~2/3 of the way down this page for my spreadsheet (the figure labeled “Set of colors that is unambiguous both to colorblinds and non-colorblinds”).  Mr. Excel actually references this palette and provides a macro that will update a workbook’s palette with this palette. The downside of this palette is that, while it may be plenty functional, I can’t say I’m wild about it from an aesthetic viewpoint. But, I’d spent the 30 minutes I’d given myself to dig, so I ran with it.
• Colorjack Color Blindness Simulation — a view of the color spectrum as seen by people with eight different forms of color blindness. That’s informative…but doesn’t really provide a realistic way to build a functional palette for data visualization purposes.
• Colorjack — a nifty tool for finding a color palette. Unfortunately…there’s no way to test how colorblind-friendly any of the palettes are
• Colorblind Web Page Filter — there were a number of tools for sale that would simulate how content would appear to people with different forms of colorblindness, but this is the (free) online tool I wound up using for the exercise below. It couldn’t be easier to use — you just provide a URL and what form of color blindness you’re interested in, and it renders it

So, aside from the one palette that was solely focussed on functionality and not at all on aesthetics, I struck out. As I pondered this over the next few days, it occurred to me that, perhaps Excel’s default colors always seemed so gosh-awful because they were actually developed explicitly with colorblindness in mind. I could not find any documentation to support the theory…so I turned left and headed down that rathole to see if I could figure it out myself.

The exercise was pretty simple. I created a 10-color bar chart using the Excel 2007 default palette. Note: This was created purely for palette-testing — this actual chart is a great example of needlessly using more color than is needed! Here’s the chart:

Like the one colorblind-friendly palette I found online, I really don’t like the aesthetics of this palette. It’s been toned down a bit from the Excel 2003 (and earlier) versions, which is good, but it still seems rather harsh. Could that be for colorblind compatibility? I think so! I took the chart above and ran it through the Colorblind Web Page Filter mentioned above for the four most common types of color blindness (as described in a Pearson report by Betsy J. Case):

Overall, the palette seems workable in all four situations. The first three colors absolutely work. Color 4, as well as color 5, start to lose a little contrast from color 1, but they still seem manageable. Color 5 and color 7, as well as color 10, start to get a little problematic in some cases, but, if you’re going beyond four colors in a single chart, you might need to reconsider your chart type anyway. Right?

Now, one final test: for achromatopsia. On the one hand, this is extremely rare. On the other hand…it’s common when your office has a lot of black-and-white printers:

Apparently, any palette that works in grayscale is a quick way to check for compatibility with all forms of colorblindness. It’s also…a best practice. Interestingly, the Excel 2007 palette really lays an egg here, in that colors 1, 2, and 4 are all barely distinguishable!

Clearly, there is an opportunity here to test a variety of functional, attractive palettes for grayscale printability and the top four forms of colorblindness and develop something better than the Excel defaults. But, that’s an exercise for another time. I think I’ll aim for the first four colors of the palette being “highly distinguishable” in all scenarios and the next four being “functionally distinguishable.” What do you think? Would this be useful? What else should I take into consideration?Similar Posts:

• Data Visualization — Few’s Examples
• Quick Excel Tip: The FASTEST Way to Sum Numbers
• How Succinctly Can I Explain Why Pie Charts Are Evil?
• One more reason why you CAN’T just start with the data
• Shortest Excel Tip Ever: <F4> and <Ctrl>-Y

© Tim for Gilligan on Data by Tim Wilson, 2009. | Permalink | 3 comments | Add to del.icio.us
Post tags: colorblind, palette

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The “game flow” graph…how in the WORLD is that telling me anything? That the score goes up as the game goes on? Really? Ya think?

My friend was referring to the diagrams that ESPN.com is providing for every game in the tournament. The concept of these graphs is pretty simple: plot the score for each team over the course of the game. For instance, the “Game Flow” graph for the Oklahoma vs. Morgan State game looks like this (you can see the actual graph on the game recap page — just scroll down a bit and it’s on the right):

This isn’t an exact replication, but it’s pretty close — best I could manage in Excel 2007 (the raw data is courtesy of the ESPN.com play-by-play page  for the game). ESPN’s graph is a Flash-based chart, so it’s got some interactivity that the image above does not (we’ll get to that in a bit).

The graph shows that the game was tight for the first 4-5 minutes, then Oklahoma pulled away, Morgan State made it really close mid-way through the first half, and then Oklahoma pulled away and never looked back. My friend had a point, though –  the dominant feature of the graph is that both lines trend up and to the right…and any chart of a basketball game is going to exhibit that pattern (actually, the play-by-play for that game has a couple of hiccups such that, when I originally pulled the data, I had a couple places where the score went down due to out-of-sequence free throw placement…but I noticed the issue and fixed it). In business, we’re pretty well conditioned to see “up and to the right” as a good thing…but it’s meaningless in the case of a basketball game.

Compare that graph to a game that was much closer – the Clemson vs. Michigan game (the graph on ESPN’s site is on the recap page, and the raw data is on the play-by-play page):

This was a tighter game all through the first half. Clemson led for the first 7-8 minutes, Michigan pulled substantially ahead early in the second half, and then things got tight in the last few minutes of the game. But, again, both lines moved up and to the right.

These charts are not difficult to interpret:

• The line on top is the team that is leading
• The distance between the lines is the size of the lead
• The lines crossing signifies a lead change

But, could we do better? Well, my wife and kids are out-of-town for the week (spring break), I have the social life you’d expect from someone who blogs about data and data visualization, and the fridge is well-stocked with beer. Party. ON!

At best, my level of basketball fan-ness hovers right around “casual.” Still, I follow it enough to know the key factors of a game update or game upset (Think: “Hey, Joe. What’s the score?”). Basically:

• Who’s winning?
• By how much?

(If there’s time for a third data point, the actual score is an indication of whether it’s a high scoring shootout or a low scoring defense-oriented game.)

Given these two factors as the key measures of a game, take another look at the graphs above. When the game is tight, you have to look closely to assess who is winning. And, determining how much they’re winning by requires some mental exertion (try it yourself: look back at the last graph and ask yourself how much Michigan was winning by halfway through the second half).

This is just begging for a Stephen Few-style exercise to see if I can do better.

First, the Oklahoma/Morgan State game:

Rather than plotting both team’s scores, with the total score on the Y-axis, this chart plots a single line with the size of the lead — whichever side of the “0″ line the plot is on is the team that is winning. The team on the top is the higher seed, and the team on the bottom is the lower seed. I added the actual score at halftime and the end of the game, as well as each team’s seed. Compare that chart to the much closer Clemson/Michigan game:

The chart looks very different — focussing on what information fans really want and presenting it directly, rather than presenting the data in a way that requires mental exertion to derive what the fan is really interested in: who’s winning and by how much? While the graphs on ESPN’s site allow you to mouse over any point in the game and see the exact score and the exact amount of time remaining, it’s hard to imagine who would actually care to do that – better to come up with an information-rich and easy-to-interpret static chart than to get fancy with unnecessary interactivity.

A few other subtle changes to the alternative representation:

• I tried to dramatically increase the “data-pixel ratio” (Few’s principle that the ratio of actual data to decoration should be maximized) — this is a little unfair to ESPN, as their site is working with an overall style and palette for the site, but it’s still worth keeping in mind
• I used color on the Y-axis to show which team’s lead is above/below the mid-line. The numbers below the middle horizontal line are actually negative numbers, but with a little Excel trickery, I was able to remove the “-” and change the color of the labels (all done through Custom number formatting)
• By putting the top seed on the top, looking at a full page of these charts would quickly highlight the games that were upsets

I’m my own worst critic, so here are two things I don’t like about the alternate charts above:

• The overall palette still feels a little clunky — the main data plot doesn’t seem to “pop” as much as it should, even though it’s black, and the shaded heading doesn’t feel right
• While the interpretation of the data requires less mental effort once you understand what the chart is showing, it does seem like this approach requires another half-second of interpretation upr front that the original charts don’t require

What do you think? What else could I try to improve the representation?Similar Posts:

• Baseball Stats and BI Musings Part I: Good Metrics?
• Put-in-Play Percentage: A “Great Metric” for Youth Baseball?
• Baseball Stats and BI Musings Part II: Data Quality
• Inventing a Metric
• Perfect Game / Pretty Good Youth Baseball Scoring System

© Tim for Gilligan on Data by Tim Wilson, 2009. | Permalink | 7 comments | Add to del.icio.us
Post tags: ESPN, game flow, March Madness, Stephen Few

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Why am I asking?

Because it doesn’t exist. Yet. Call it a working title for a project I’ve been kicking around in my head for a couple of years. In a lot of ways, this blog has been and continues to be a way for me to jot down and try out ideas to include in the book. This is my first stab at trying to capture a real structure, though.

The Best Little Book on Data

In my mind, the book will be a quick, easy read — as entertaining as a greased pig loose at a black-tie political fundraiser — but will really hammer home some key concepts around how to use data effectively. If I’m lucky, I’ll talk a cartoonist into some pen-and-ink, one-panel chucklers to sprinkle throughout it. I’ll come up with some sort of theme that will tie the chapter titles together — “myths” would be good…except that means every title is basically a negative of the subject; “Commandments” could work…but I’m too inherently politically correct to really be comfortable with biblical overtones; an “…In which our hero…” style (the “hero” being the reader, I guess?). Obviously, I need to work that out.

First cut at the structure:

• Introduction — who this book is for; in a nutshell, it’s targeted at anyone in business who knows they have a lot of data, who knows they need to be using that data…but who wants some practical tips and concepts as to how to actually go about doing just that.
• Chapter 1: Start with the Data…If You Want to Guarantee Failure – it’s tempting to think that, to use data effectively, the first thing you should do is go out and query/pull the data that you’re interested in. That’s a great way to get lost in spreadsheets and emerge hours (or days!) later with some charts that are, at best, interesting but not actionable, and, at worst, not even interesting.
• Chapter 2: Metrics vs. Analysis — providing some real clarity regarding the fundamentally different ways to “use data.” Metrics are for performance measurement and monitoring — they are all about the “what” and are tied to objectives and targets. Analysis is all about the “why” — it’s exploratory and needs to be hypothesis driven. Operational data is a third way, but not really covered in the book, so probably described here just to complete the framework.
• Chapter 3: Objective Clarity — a deeper dive into setting up metrics/performance measurement, and how to start with being clear as to the objectives for what’s being measured, going from there to identifying metrics (direct measures combined with proxy measures), establishing targets for the metrics (and why, “I can’t set one until I’ve tracked it for a while” is a total copout), and validating the framework
• Chapter 4: When “The Metric Went Up” Doesn’t Mean a Gosh Darn Thing – another chapter on metrics/performance measuremen. A discussion of the temptation to over-interpret time-based performance metrics. If a key metric is higher this month than last month…it doesn’t necessarily mean things are improving. This includes a high-level discussion of “signal vs. noise,” an illustration of how easy it is to get lulled into believing something is “good” or “bad” when it’s really “inconclusive,” and some techniques for avoiding this pitfall (such as using simple, rudimentary control limits to frame trend data).
• Chapter 5: Remember the Scientific Method? — a deeper dive on analysis and how it needs to be hypothesis-driven…but with the twist that you should validate that the results will be actionable just by assessing the hypothesis before actually pulling data and conducting the analysis
• Chapter 6: Data Visualization Matters – largely, a summary/highlights of the stellar work that Stephen Few has done (and, since he built on Tufte’s work, I’m sure there would be some level of homage to him as well). This will include a discussion of how graphic designers tend to not be wired to think about data and analysis, while highly data-oriented people tend to fall short when it comes to visual talent. Yet…to really deliver useful information, these have to come together. And, of course, illustrative before/after examples.
• Chapter 7: Microsoft Excel…and Why BI Vendors Hate It – the BI industry has tried to equate MS Excel with “spreadmarts” and, by extension, deride any company that is relying heavily on Excel for reporting and/or analysis as being wildly early on the maturity curve when it comes to using data. This chapter will blow some holes in that…while also providing guidance on when/where/how BI tools are needed (I don’t know where data warehousing will fit in — this chapter, a new chapter, or not at all). This chapter would also reference some freely downloadable spreadsheets with examples, macros, and instructions for customizing an Excel implementation to do some of the data visualization work that Excel can do…but doesn’t default to. Hmmm… JT? Miriam? I’m seeing myself snooping for some help from the experts on these!
• Chapter 8: Your Data is Dirty. Get Over It. – CRM data, ERP data, web analytics data, it doesn’t matter what kind of data. It’s always dirtier than the people who haven’t really drilled down into it assume. It’s really easy to get hung up on this when you start digging into it…and that’s a good way to waste a lot of effort. Which isn’t to say that some understanding of data gaps and shortcomings isn’t important.
• Chapter 9: Web Analytics – I’m not sure exactly where this fits, but it feels like it would be a mistake to not provide at least a basic overview of web analytics, pitfalls (which really go to not applying the core concepts already covered, but web analytics tools make it easy to forget them), and maybe even providing some thoughts on social media measurement.
• Chapter 10: A Collection of Data Cliches and Myths — This may actually be more of an appendix, but it’s worth sharing the cliches that are wrong and myths that are worth filing away, I think: “the myth of the step function” (unrealistic expectations), “the myth that people are cows” (might put this in the web analytics section), “if you can’t measure it, don’t do it” (and why that’s just plain silliness)
• Chapter 11: Bringing It All Together — I assume there will be such a chapter, but I’m going to have to rely on nailing the theme and the overall structure before I know how it will shake out.

What do you think? What’s missing? Which of these remind you of anecdotes in your own experience (haven’t you always dreamed of being included in the Acknowledgments section of a book? Even if it’s a free eBook?)? What topic(s) are you most interested in? Back to the questions I opened this post with — would you be interested in reading this book, and do you have friends or co-workers who would be interested? Or, am I just imagining that this would fill a gap that many businesses are struggling with?Similar Posts:

• The “Right” Talent: an MVT-Meets-Fractional-Factorial-Design Anecdote
• A Seismic Shift in Demand Generation: Putting Your Leads at the Center of Your Lead Marketing (Part 1 of 2)
• Data Visualization that Is Colorblind-Friendly — Excel 2007?
• Business cliches
• Social Media ROI: Stop the Insanity!

© Tim for Gilligan on Data by Tim Wilson, 2009. | Permalink | 7 comments | Add to del.icio.us
Post tags: book, cliches, Edmund Tufte, myths, Stephen Few

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Nevertheless, here’s a handy one that requires no customization of Excel, but that I guarantee you’ll be hooked on if you start using it: <F4>

<F4> and <Ctrl>-Y do the same thing, actually, and they work in MS Word, too. What do they do? Pretty simple:

Repeat the Last Action Taken

That’s it. If you’ve just formatted a cell or set of cells with a new border and background color, then you can click on a cell and press <F4> and it’ll apply the same formatting to the new cell. And then do it again! In this case, it does the same thing as the Format Painter…but does it faster (with limitations, as described below).

If you’ve just inserted a row and you want to insert another row lower down on the spreadsheet, highlight the next row and press <F4>.

As you can imagine (if you stop and try to imagine it…and I recognize it’s got to be a pretty bleak day of creativity for this to bubble up as worthy of your imagination), this is particularly handy when doing some oddball work on non-contiguous cells.

This is handier than you might think. And, it does have it’s limitations. The main one is that it only repeats the immediately preceding action. In the Format Painter example above, <F4> is no use if you have a cell already formatted as you want and you want to copy that format to other cells. That’s what the Format Painter is for.

And, another limitation is that it doesn’t work with every possible action. For instance, if you type a value into a cell and then want that same value in another cell…<F4> doesn’t work. You’ll have to copy and paste. It becomes pretty intuitive in a hurry as to where it works and where it doesn’t.

Happy repetition!

Similar Posts:

• Quick Excel Tip: The FASTEST Way to Sum Numbers
• How Many Times Do I Have to Tell You My Name?…Redux
• “The Axiom of Research” and “The Axiom of Action”
• Two Ways to Gauge if Someone Really Uses Excel
• Monish Datta: “It was the best WAW yet!”*

© Tim for Gilligan on Data by Tim Wilson, 2008. | Permalink | 6 comments | Add to del.icio.us
Post tags: Excel tips, F4

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Backing up just a little bit. Paste Special is really, really nice to have when you need it. Specifically, pasting values, formats, formulas…and occasionally Transpose. But…mostly (for me) pasting values. When I’ve got a well-formatted table of data and need to move some data around, it’s just annoying to need to then go and fix the formatting. So, pasting formulas only or values only avoids all that. The problem is that the fastest way to do this is:

1. Copy the cell(s) you want to relocate (<Ctrl>-<C>)
2. Right-click anon the cell in the new location
3. Select Paste Special
4. Select Values
5. Click OK

All in all, not too painful…unless you find yourself needing to do it two or three times in a row (between separate workbooks, for instance).

This got annoying enough to me a several years ago that I recorded a macro and dropped it in Personal.xls so that I’d have a faster way to do this. It’s now the first thing I set up on any new computer I get.

The Result: After copying cells (this doesn’t work with cutting data), simply click on the cell where you want the values pasted and press <Ctrl>-<Shift>-<V>. That’s it.

How to Set It Up

This may look like a real hassle. It really isn’t (those four years as a technical writer tend to make my procedure writing a bit…er…detailed). But, it’s a one-time setup, and it really isn’t that bad.

If you’ve read this far and aren’t thinking, “MAN! That would be HANDY!” then just bail now. Otherwise, read on:

1. Launch Excel 2003
2. Select Window»Unhide
3. Select Personal.xls
4. Select Tools»Macros»Visual Basic Editor. This should bring up the VBA editor
5. Select Insert»Module
6. Copy and paste the following into the window:Sub PasteSpecial_Values()
Selection.PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
End Sub
7. Click on the X to close the Visual Basic Editor (you don’t need to save anything yet). You should be back on the Personal.xls workbook
8. Select Tools»Macro»Macros
9. Select PasteSpecial_Values
10. Click Options
11. Click in the Shortcut key box
12. Press <Shift>-<V>
13. Click OK
14. Click the X to close the Macros window
15. Press <Ctrl>-<S> to save Personal.xls
16. Select Window»Hide to hide Personal.xls
17. Close Excel. If you are prompted to save Personal.xls, do so.

You should be set. Let me know if you give it a shot and find it useful (and if you hit any bumps in implementing it).Similar Posts:

• Shortest Excel Tip Ever: <F4> and <Ctrl>-Y
• And the Rebellion Is Over (for Now)
• Two Ways to Gauge if Someone Really Uses Excel
• Quick Excel Tip: The FASTEST Way to Sum Numbers
• Columbus Web Analytics Wednesday: A Speedy April

© Tim for Gilligan on Data by Tim Wilson, 2008. | Permalink | 5 comments | Add to del.icio.us
Post tags: macro, Paste Special, tips

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I finally got around to reading a good chunk of the book as I was flying a three-legged trip out to British Columbia last week…and it is good! One section that particularly struck me started on page 100:

Edward R. Tufte introduced a concept in his 1983 classic The Visual Display of Quantitative Information that he calls the “data-ink ratio.” When quantitative data is displayed in printed form, some of the ink that appears on the page presents data, and some presents visual content that is not data.
:

He then applies it as a principle of design: “Maximize the data-ink ratio, within reason. Every bit of ink on a graphic requires a reason. And nearly always that reason should be that the ink presents new information.”
:
This principle applies perfectly to the design of dashboards, with one simple revision: because dashboards are always displayed on computer screens, i’ve changed the work “ink” to “pixels.”

I’ll actually go farther and say that “dashboards” can be replaced with “spreadsheets” and this maxim holds true. Taking some sample data straight from Few’s book, and working with a simple table, below is how at least 50% of Excel users would format a simple table with bookings by geographic region:

Look familiar? The light gray gridlines in the background turned on in Excel by default. And, a failure to resist the urge to put a “thin” grid around the entire data set.

Contrast that with how Few represents the same data:

Do you agree? This is clearly an improvement, and all Few really did was remove the unnecessary non-data pixels.

So, how would I have actually formatted the table? It’s tough to resist the urge to add color, and I am a fan of alternating shaded rows, which I can add with a single button click based on a macro that adds conditional formatting (“=MOD(ROW()+1,2)=0″ for shaded and “=MOD(ROW(),2)=0″ for not shaded):

In this case…I’d actually vote for Few’s approach. But, even Few gives the okay to lightly shaded alternative rows later in the same chapter, when some sort of visual aid is needed to follow a row across a large set of data. That’s really not necessary in this case. And, does bolding the totals really add anything? I don’t know that it does.

The book is a great read. It’s easy to dismiss the topic as inconsequential — the data is the data, and as long as it’s presented accurately, does it really matter if it’s presented effectively? In my book, it absolutely does matter. The more effectively the data is presented, the less work the consumer of the data needs to do to understand it. The human brain, while a wondrously effective computer, has its limits, and presenting data effectively allows the brain to spend the bulk of its effort on assessing the information rather than trying to understand the data.

Similar Posts:

• The Perfect Dashboard: Three Pieces of Information
• Test Your Data Visualization IQ
• Data Visualization — Few’s Examples
• Depth vs. Breadth, Data Presentation vs. Absorption, Frank and Bernanke
• Zuckerberg/Lacy — a Technical (Data) Twitter Analysis

© Tim for Gilligan on Data by Tim Wilson, 2008. | Permalink | One comment | Add to del.icio.us
Post tags: Edward Tufte, Stephen Few

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Question: What is the fastest way to get the sum of a small range of cells in Excel?

If you immediately think of something along the lines of: “Click on an empty cell, hit the sigma icon, and then highlight the range of cells you want to sum and press <Enter>,” then this tip is for you.

The approach I just described is great if you actually want to keep the sum in the spreadsheet as a permanent calculation. But, what if you are on the phone and discussing the data with somebody who asks, “What was the total from July through October?” You don’t already have that calculated, and you don’t really want to keep that calculation on the spreadsheet. The approach above would work. But there is a better way.

The tip put really, really simply: Highlight the cells you want totaled and look at the bottom right of your screen. They’re totaled for you there (assuming you haven’t gone in and turned off the Status bar under the View menu, and, let’s face it, if you were that desperate for screen real estate, you really should be out shopping for a larger monitor rather than tooling around the internet reading blogs).

Now, With Pictures…

You’ve got a table with numbers in it:

(The formatting of this table offends my data presentation sensibilities in many, many ways, but it’s the default way that many people format tables, and I don’t want to detract from the core of this tip.)

Let’s say you want to get the sum for January through April. Highlight the data you want to total:

Look down in the bottom righthand corner of Excel (if you don’t see this, select View»Status Bar):

Lookie there! Sum=54,200. Chances are, you never noticed that, and yet it’s been industriously summing away every time you’ve highlighted a range of cells in Excel for years!

“That’s all well and good, Gilligan, but what if I want to see the average for these four months?”

Well, that’s easy, too. Just right-click on the Sum= area on the status bar, and you will get a menu that lets you pick what you want math function that box should perform.

Change it to Average, and, henceforth and forthwith (until you change it to something else), that area will show the average of any set of cells you select.

Pretty handy. And, of course, you don’t have to drag a contiguous set of cells. You can hold down <Ctrl> and select multiple non-contiguous cells (e.g., “What was the total for Jan-07 and Jan-08?”).

Be honest. Did you already know that?

Similar Posts:

• Shortest Excel Tip Ever: <F4> and <Ctrl>-Y
• Two Ways to Gauge if Someone Really Uses Excel
• Data Visualization that Is Colorblind-Friendly — Excel 2007?
• Stephen Few’s Derivation of Tufte: The Data-Pixel Ratio
• Sometimes, it doesn’t make sense to look at the data

© Tim for Gilligan on Data by Tim Wilson, 2008. | Permalink | One comment | Add to del.icio.us
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This led to my “Two Ways to Gauge if Someone Really Uses Excel” theory (this fellow clearly did!):

1. Do they use pivot tables? It’s not just if they’ve ever seen a pivot table or struggled to create one in a pinch. It’s, when asked the question, “Do you use pivot tables?” their reaction involves some sort of significant change in facial expression along with a forceful response. Something along the lines of, “Oh my god, YES!!! I don’t know what I would do without them!”
2. Do they use VLOOKUP? Again, the key here is not whether they have heard of VLOOKUP. It’s whether they see it as a function that they simply could. Not. Live. Without!

What is interesting is that both of these features of Excel are database-like functionality. Pivot tables are, basically, a way to do semi-dynamic SQL GROUP BYs (or basic aggregation in MS Access). VLOOKUP is a poor man’s SQL join. But, both are fast (for pivot tables, I’ve learned that I can start the pivot table wizard and then, 99 times out of 100, simply click Finish without going through the intermediate wizard steps to get what I want) and, if data arrives for analysis in Excel, then both are native to the environment the data is already in.

One other note on these features is that, if someone is to the point where they automatically and naturally use pivot tables and VLOOKUP, then it is practically guaranteed that they use string manipulation functions (&, LEFT, RIGHT, MID, LEN, FIND) and conditional and comparison functions (IF, AND, OR, SUMIF). These are every bit as important as VLOOKUP, but, in my experience, they come before or with a VLOOKUP addiction; seldom do they come after.

There is a separate dimension of Excel usage that does not get covered by this test, and that is Excel’s data visualization capabilities. As a matter of fact, up through Excel 2003, pivot tables were particularly difficult to use for charting — charting pivoted data creates a pivot chart, which, while dynamic and manipulable, is pretty ugly. Excel can be used to generate very professional, impactful, clean visual representations of the data. Unfortunately, the tool’s defaults do not do this! But, that is a topic for a whole separate series of posts!Similar Posts:

• Quick Excel Tip: The FASTEST Way to Sum Numbers
• SPSS Expertise? Job Opportunity — Full-time/Contract/Flexible
• Data Visualization that Is Colorblind-Friendly — Excel 2007?
• "You can make the data say whatever you want it to."
• Am I Ever BeHIND on Posting…

© Tim for Gilligan on Data by Tim Wilson, 2007. | Permalink | One comment | Add to del.icio.us
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3D pie charts, though, are simply horrid! They actually misrepresent the data and remove whatever instantaneous clarity that a flat pie chart provides.

In the pie chart above, Which product has the greatest portion of the whole?

Product B. That’s not too hard.

Which is greater, Product A or Product D?

Trick question. They’re the same. And, you probably figured that out. But, in order to do so, your brain had to undo the 3D effect, since when it comes to raw area shown, Product A is larger.

When asked a direct question like, “Which is greater, Product A or Product D,” this isn’t too hard to do. But, that’s not usually the approach of interpreting visual displays of data. Rather, the viewer looks at it and says, “What does this chart tell me?” In a 3D pie chart, your brain has to spend extra cycles doing the A vs. D comparison for every wedge in the pie. And it gets pretty hairy when you’ve got, say, 10 or more wedges. What’s happening is your brain has to go through a (subconscious, but real) effort to remove the 3D effect. That’s an effect that somebody else wasted brain cycles and effort on adding in the first place.

This is the sort of inefficiency that process improvement folk salivate over finding in a manufacturing environment: “Person A unwraps a widgetlet and then screws it on to a doohickey and sends it to the next station. Person B then unscrews the widgetlet, inserts a washer, and then screws it back on in the exact same spot.” Obviously, if Person A didn’t screw the widgetlet on in the first place, then the process would have two steps removed: Person A’s screwing on of the widgetlet and Person B’s unscrewing of it.

It’s the same deal with 3D pie charts.Similar Posts:

• Vitriolic Rant about “3D” Charts
• How Succinctly Can I Explain Why Pie Charts Are Evil?
• Data Visualization — Few’s Examples
• What happens if you combine 3D WITH fading axes?
• Guy Kawasaki (Almost) Says 3-D Graphs Are Evil

© Tim for Gilligan on Data by Tim Wilson, 2007. | Permalink | No comment | Add to del.icio.us
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