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Closing Data’s Last-Mile Gap: Visualizing For Impact!

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I worry about data’s last-mile gap a lot. As a lover of data-influenced decision making, perhaps you worry as well.

A lot of hard work has gone into collecting the requirements and implementation. An additional massive investment was made in the effort to perform ninja like analysis. The end result was a collection trends and insights.

The last-mile gap is the distance between your trends and getting an influential company leader to take action.

Your biggest asset in closing that last-mile gap is the way you present the data.

On a slide. On a dashboard in Google Data Studio. Or simply something you plan to sketch on a whiteboard. This presentation of the data will decide if your trends and insights are understood, accepted and inferences drawn as to what action should be taken.

If your data presentation is good, you reduce the last-mile gap. If your data presentation is confusing/complex/wild, all the hard work that went into collecting the data, analyzing it, digging for context will all be for naught.

With the benefits so obvious, you might imagine that the last-mile gap is not a widely prevalent issue. I’m afraid that is not true. I see reports, dashboards, presentations with wide gaps. It breaks my heart, because I can truly appreciate all that hard work that went into creating work that resulted in no data-influence.

Hence today, one more look at this pernicious problem and a collection of principles you can apply to close the last-mile gap that exists at your work.

For our lessons today, I’m using an example that comes from analysis delivered by the collective efforts of a top American university, a top 5 global consulting company, and a major industry association. The analysis is publicly available.

I’ve chosen to block out the name of entities involved. Last-mile gaps exist at all our companies. It is not important where this 2018 analysis came from. In the tiny chance that you recognize the source, I request you to keep it out of your comments as well.

For each of the 17 examples we review, I’ll share an alternative version I created. I invite you to play along and share your version of any of the examples. I’ll add them to the post, and credit you.

Ready?

Let’s go!

I persistently advocate for simplicity in slides. Don’t create handouts!

In this case the goal was to create handouts, perhaps to make it easier for audiences to consume the data by themselves. I would humbly still advocate for simplicity when it comes to data presentation.

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Some of the fixes to solve for simplicity could be to use fewer sprinkles, a simpler header – graphics and text –, and we can be very selective about what’s on he slide. As you look at the slide, I’m sure you’ll come up with other ways in which we can liberate the white space for the tyranny of text/colors.

Solving for simplicity contributes to communication effectiveness. It of course reflects on your brand, and, most of all, helps you have better control over the story you are trying to tell.

For the rest of this post I’ll ignore the simplicity and storytelling elements and focus exclusively on the data itself. How, what, why and instead of.

Look at the graph above, and the little table… Ponder for a moment what you would do to close the last-mile gap and help the essential message shine through.

Here are some things that stood out for me:

1. Graphing choices can exaggerate or undersell reality.

One way to exaggerate is to start your y-axis at 40, as it the case above. The resulting line exaggerates the trend and ends up implying something that might not quite be there.

Start at zero. Please.

2. False precision can cause clutter, and undercut the Analyst’s brilliance.

This is very subtle.

You’ll notice that the numbers on the graph are expressed with one decimal point. As in 47.7, 56.5, etc. If you pause and consider how this data is collected, via a small triple digit sample self-reported survey results, you’ll quickly realize that the error range in this data is likely a few points. If that’s true, showing the .6, .5 is implying a precision that simply does not exist.

Besides, this false precision also clutters the graph.

3. Remove the distractions, ruthlessly.

In an 11-year span, each data point is a lot less important than the trend. Do you need the dots on the graph? Do you even need the numbers for the individual months?

When it comes to closing the last-mile gap it is helpful to have a ruthless streak. It is helpful because in service of our ultimate objective, you’ll have to kill some of your favorite things, you’ll have push back against your boss/peers who might love clutter, and you might have to help change an entire culture. Hard, painful, work. But, immensely worth it.

Here’s an alternative way to present the data, using nothing more than the standard settings in good old Excel:

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It shows the trend, simply. You can see it is up broadly over eleven years. That it was under 50 and is now close to 70.

Did you notice the trend is not as exaggerated as the original? And, still effective!

You might use a different font, perhaps have the graph be smaller, or maybe twist the month-year in the other direction. No problem. I’m confident if you apply the first three filters, whatever you create will close the last-mile gap better.

Here’s an example of doing exactly the opposite of principle #1. The y-axis is artificially set at 100%, as a result the trend is understated.

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You don’t need to go this far.

Just let your favorite graphing tool auto-set the major and minor-axis, which will result in the graph looking like this…

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Simple. No funny business. 

The trend stands by itself waiting your words as to why it is meaningful.


This next one is pretty interesting. My request to you is to not scroll beyond the slide. Pause. Absorb the graph. Try to understand what the author is really trying to say.

For bonus points, consider the perspective of the person reading this graph rather than the person who created it.

Read. Don’t scroll. Absorb.

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How well did you understand the trend and the insight being communicated? What would you have done differently if you’d created the graph?

Here are some things that stood out for me:

4. Show as much data as is required, and no more.

The goal in the original seems to be to show top priorities for 12 months. If so, is the data for August 2017 really adding value?

Often we want to show all the data we have (after all we spent time collecting it!). In this case, it get’s in the way of understanding the 12 month shift.

5. Experiment with visualization options, even in Excel!

We have five dimensions of data, and two data points each (if you apply principle #4). We want the audience to be able to compare two data points for each dimension, and look across all five dimensions.

The bar chart is a sub-optimal way to let the audience see this. Consider experimenting with different visuals in Excel (or D3js).

I applied the radar chart to this data, and got this lovely end result…

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It is ten million times easier to see the two data points for five dimensions, and realize that only two have changed.

Likewise, the overall trend also pops out at you so much easier in this case.

It would have taken ten minutes for us to explain the data and trend in the original. We can do that in five seconds now. You can use the time remaining discussing why this trend is material and what to do about it (if anything). Actually allowing data to play its natural role: Influence decisions.


This is a really nice example of a lesson that we tend to forget all the time (myself included).

You know the exercise by now. Pause, reflect on this slide, then scroll.

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Here’s what stood out for me:

6. Don’t send a graphic to do a table’s job.

In this case, we are comparing two simple data points, on two dimensions (past, present). Why do we need a graph taking up all the space?

Why not just have a table that shows previous 12 months as 7.1% and a row under it with next 12 months as 8.9%?

Even better, why not just have one line of text:

Percent change in marketing budgets = +1.8 PP

Why have two fat bars?

Once you arrive at that conclusion, you’ll apply principle #4 and realize that the most interesting data on this slide is not the visual… Rather, it is the table on the top right corner of the slide.

Bada, bing, bada, boom, ten seconds later here’s your slide:

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A simple table with a touch of colors that draws out the core message simply, directly and quickly.

The lighter shade for the core numbers will result in them being pushed a bit into the background. This simple choice guides the reader’s eyes gently to the delta (the most important bit).

I like playing with the borders a bit, as you see above. You might have other things you are picky about. And, that is ok. 🙂

To illustrate principle #6, here’s another slide where the graphic is completely unnecessary:

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A tiny table with two data points will do just fine.

Here’s a bonus lesson for the analysis ninjas out there. Please don’t imply a linear trend between “current levels” and “next three years.” There is no indication that data from 2017 to 2020 is available, and it is highly unlikely that it will follow a linear trend. This is another example of breaking principle #1.

(Let’s not lose sight of the big picture: I am delighted that spending on analytics is going to increase that much! As our leaders spend this largesse, I hope that they’ll remember the 10/90 rule to ensure optimal returns. The money needs to go to you!)


This one flummoxed me.

Let’s see if you can internalize what is going on. Stare at the graph intently, seriously, and see if you get the points…

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Bold items naturally catch the eye, in this case the blue bars. Most people in the western world look from left to right, that is how you’ll likely try and understand what’s going on.

Your first impression will likely be that the blue bars are showing a random trend in marketing spending.

If you are the curious type you’ll realize that is the wrong conclusion, and you’ll want to understand what’s really going on. Soon enough you’ll get to the x-axis and a carefully review will illuminate that the reason for the weirdness is the choice to show the industry names alphabetically!

7. Please, please, please keep the end-user in mind.

In this case the end-users (our senior leaders) would be primarily be interested in understanding where marketing spending is highest and lowest. This is very difficult to accomplish above.

Secondarily, they’ll want to know where they fall in context of all other industries, this is almost impossible to accomplish above.

The reason the x-axis is organized alphabetically is to allow you to look up your specific industry easily. This thought is good. My hypothesis is that it likely forms a small percent of the use cases, primarily because just knowing your spend is not that valuable. What’s valuable are the above two use cases.

Here’s what I recommend keeping front of mind: If a non-analyst is looking at the data, what uses cases form the basis of the value they’ll extract. Then, ensure the info viz is solving for that.

In this case the bars with the data seem to be randomly sorted. The visualization is getting in the way, creating a wider last-mile gap.

Luckily this is a quick fix in good old Excel. Two minutes later, you’ll have a little waterfall…

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It is easy to see the outliers and the pack of eight that are close to each other (something you can’t even see in the original).

It will certainly take an extra couple of seconds to find your industry, but in service of the two bigger use cases,  it is a small price to pay.

You can play with the layout to your heart’s content. If you dislike waterfalls for some reason and prefer towers…

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I like the waterfall, but this is not bad. 🙂

Play with the colors, drop shadows, fonts, and more. Make the graph your own. Just don’t forget to look at it through the eyes of the end user and solve for their use cases.

(Speaking of colors… I’m partial to chart styles 17 through 24 in Excel. In my work you’ll see a particular affection for style 18.)


I hate pie charts. I really do.

You can read a 506 word love-letter to my profound dislike (including a lovely exercise you can do).

Here’s the scientific reason:

Comparison by angle is significantly more difficult than by length.

That is well on display below…

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The colors in the pie will catch your eye. Yet, from the sizes of the slices it is difficult to internalizes the differences between each dimension.

8. Eat Pies, Don’t Share Them!

Since humans find comparing lengths much easier, it should only take a few minutes to take the data and convert the slide above into something that closes the last-mile gap efficiently.

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The above slide is a good example how to apply all the principles you’ve learned thus far. The question and the data are the hero, almost all by themselves. Allowing you to focus sharply on your story.

Scroll up and down and compare the two slides. You’ll see many more differences.


I’ve extoled the virtue of using a table, instead of trying to be extra sexy and throwing in a graphic.

The challenge with tables is that they can become overwhelming very quickly.

Here’s an example that illuminates that clearly.

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It feels like there is a lot. It also breaks principle #2, false precision,  which makes things worse.

Considering the core message the analysis is trying to send, I believe that it is also breaking rule #4, extra perhaps unnecessary data.

9. Make your tables pop, guide the reader’s eye.

There are numerous tools available to you inside Excel to make your tables pop. I usually start by playing with the options at my disposal under Conditional Formatting.

One straight-forward option is to use Color Scales, green to yellow, to produce a simpler table that pops…

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The elimination of the overall average makes the table tighter.

It is easier to look at the trend in each column. What’s even more delightful is the second use case of comparing the highs and lows across the four dimensions. So much easier.

While all the data is still there, most senior leaders want to understand trends and the contrasts. They want relative positioning, the above table does not require expending too many brain cells to get that. And, if your boss does not trust you… She still has the numbers there.

Notice the combination of fonts, colors, style treatments, in the table above. Bunch of subtle points there.

If your personal tastes are different, no problem. There are other styles you can use.

Here’s the data rendered using solid fill Data Bars…

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In this case I feel data bars add clutter, but they make internalizing the trend across individual dimensions easier.

If, like me, you are biased towards radical simplicity via white space, you can keep the table. Consider applying some subtle font color treatment to create something that’s still a step change over the original…

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I’ve shown the highs and lows in a way that you’ll see them quickly.

Red was chosen on purpose to emphasize that it was the most important thing from the customer’s perspective. Blue fades into the background a bit because it is the least important.

One final touch.

I felt it might be of value to see the product and services dimensions together, comparing them across B2B and B2C.

Here’s that version…

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There’s a little air gap in the table to emphasize the two comparisons are different. You can usually use visual cues like these to help the consumers of your analysis.


We disagree on a whole lot of subjects in our country these days, but the one thing we can all agree on is that the human attention span is probably ten micro-seconds.

Add to that short attention span the fact that each executive has 18 other urgent things taking up their brain cells. As if all that was not hard enough, while you are presenting they are also likely on their phone or laptop.

Persuading anyone in these circumstances is a herculean task.

With that context in mind, how many leaders do you think will understand what’s going on here…

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4 dimensions x 5 time periods x crazy swings = Ouch!

For bonus points, notice the randomness in the x-axis. It jumps from 2014 to 2017 without any visible explanation. To make things worse, look at the trend lines – they connect the two data points to imply a trend between 2015, 2016 that may or may not exist.

For even more bonus points, notice that there are four Februaries and as if it is no big deal an August is thrown in randomly.

Ouch. Ouch.

These might seem like small issues, but I assure you that you’ll instantly lose credibility with any intelligent leader in the room. They won’t raise their hand and start to berate you. They’ll quietly make a mental note about you, and then not pay any attention to anything you are saying.

There’s an even more important principle to learn from this visual…

10. Let the higher order bit be your north star.

It can be difficult to figure out how to go from the complex to the simple.  My recommendation is to start with the most important thing you are trying to say.

In this instance the goal is to illuminate the percent change in marketing knowledge in the next 12 months. So, are the rest of the data points necessary and of value?

In service of the higher order bit, I would argue that we can also get rid of the two Februaries and the lonely August. (Though I sincerely respect the effort it took to get those data points.)

With those decisions we are left with just two data points. We can move to a simple table and close the last-mile gap by creating this slide…

Simpler, right?

We can do one better.

If the objective is to just show the change, we can just show the percentage change.

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The colors help focus the attention even more.

To see the dramatic change, scroll back up and look at the original and then come back here. Incredible, right?

It might seem that this is hard work that takes time. It does take more time. But, it is not in the ink rather it is in the think. Discussing, debating, really thinking through what we are trying to communicate. The visualizing part takes a lot less time.


The biggest problem with this type of analysis, compiled into 95 slides, is that it never answers the question why?

Take this slide as an example. It shares a very positive view of analytics…

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The slide breaks all ten principles we’ve discussed in this post, but beyond that there is a bigger problem here.

11. Why. Your job is to answer why!

Your first instinct is the marvel at the shift (all blues are up!), and reflect on how this graph is long-term job security for everyone who reads this blog.  But, you’re an Analyst and that good feeling won’t last.

Your mind quickly goes to… Why? What is causing this shift?

Look at Mining/Construction, 60 percent points of change. OMG! Why?

The entity creating this report sadly never answers any why question anywhere. Perhaps by design.

But, consider this: Data creates curiosity. If the Analyst does not satiate that curiosity via deeper analysis that explains why, the same data turns into a disappointment. It certainly drives no change.

I’ve written about this topic before, using an example from Econsultancy and Lynchpin: Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

Without the why your last-mile gap is a million miles wide. If you are going to be in the data regurgitation business, please consider it your job to answer the why question. Without it all this is… fake news.

A challenge for you to tackle.

Now that you are aware of the 11 principles that aid in closing the last-mile gap, I want you to tackle something on my behalf.

I had not idea what to do with this slide… Can you create an after version?

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Partly the issue is that I could not truly internalize what was being said. Partly it is that the numbers don’t really seem to change much. Partly it is because I was torn between the graphic and the table on the top right.

Regardless, I gave up. Perhaps you can teach me, and our readers, what a version with a reduced last-mile gap will look like.

Just email me your version (blog at kaushik dot net) or comment below.

Here’s a summary of the 11 principles you can use to close the last-mile gap:

01. Graphing choices can exaggerate or undersell reality.
02. False precision can cause clutter, and undercut the Analyst’s brilliance.
03. Remove the distractions, ruthlessly.
04. Show as much data as is required, and no more.
05. Experiment with visualization options, even in Excel!
06. Don’t send a graphic to do a table’s job.
07. Please, please, please keep the end-user in mind.
08. Eat Pies, Don’t Share Them!
09. Make your tables pop, guide the reader’s eye.
10. Let the higher order bit be your north star.
11. Why. Your job is to answer why!

I wish you smaller gaps and more decisions that are data-influenced.

As always, it is your turn now.

In your practice, how wide is the last-mile gap? What do you think contributes to the gap the most? Which of the above principles have you used, to good effect? Do you have a favorite principle, or five, to close the gap? If you had to kill one practice when it comes to data presentation, who would be the chosen candidate?

Please share versions of the above examples that you’ve taken a crack at fixing. And, your lessons, best practices, and as always your critique via comments below.

Thank you.



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Orignal Article Can Be Found Here

Create High-Impact Data Visualizations: Nine Effective Strategies

Green_Visual I believe deeply in the value of making data accessible.

In service of that belief, there are few things that bring me as much joy as visualizing data (smart segmentation comes close). There is something magical about taking the tons and tons of complexity that lurks in our data, being able to find the core essence, and then illustrate that simply. The result then is both a mind and heart connection that drives action with a sense of urgency. #winning

While I am partial to the simplest of visualizations in a business data context, I love a simple Bar Chart just as much as a Chord or Fisher-Yates Shuffle. As we have all learned, tools matter a lot less than what we do with the tool. 🙂

In this post I want to inspire you to think differently. I’ve curated sixteen extremely diverse visualization examples to do that. By design none of them from the world of digital analytics, though I’ll stay connected to that world from a how could you use this idea perspective. My primary goal is to expand your horizon so that we can peek over and see new possibilities.

To spark your curiosity, the visuals I’ve worked hard to find for you cover the US debt, European politics, lynching and slavery, pandemics, movies, gun control, drugs and health, the Chinese economy, and where we spend our lives (definitely review this one!).

The sixteen examples neatly fall into nine strategies I hope you’ll cultivate in your analytics practice as you create data visualizations:

This post has quite a bit of depth, and loads for you to explore, reflect and internalize. It will take a few visits to absorb all the lessons. In as much, my recommendation is to read one section per day. Take time to really understand what’s going on, go to the site, play, look at the higher resolution versions (click on the images), make notes of what you’ll do for the first time or change about what you already do. Most importantly, practice taking action. Then, come back, read the next one and take action. I promise, the rewards will be rich.

Let’s go make you an even more effective influencer when it comes to data!

Strategy 1: The Simplicity Obsession

One of the reasons so many visuals are so very complex is that the Analyst/Creator is trying to demonstrate how clever they are. Sadly in the process of demonstrating aforementioned cleverness, the visuals ends up being incredibly complex crammed with every little bit of amazesomeness they  are trying to demonstrate…

us maximum personal income tax rate vs national debt burden per capita
(Click on the above image for a higher resolution version)

There is absolutely no doubt in my mind that the Creator worked very hard, and, I sincerely mean this, they are very clever.

The problem is that the essence of what they want to communicate is probably only known to them, or to any person willing to take the time to first learn the job of the analyst, dig into the data themselves, create this picture and then understand what is being said.

It breaks my heart.

Go on. Scroll back up. See if you can understand what is being said.

In my humble opinion there is an additional subtle problem. The Creator was asked to plot the data, or perhaps share the insights, but it is unclear whose job it was to answer this simple question at the end: So What?

When you start with that as your destination, so what, as the creator of any visualization you are going to ask for a lot more context, you are going to make sure the visual is in service of the answer, you’ll make sure your cleverness is focused on the outcome the data has to serve.

Please, please, please keep that in mind.

The complicated thing above is trying to highlight an important trend, is missing the context, and is simply not as dramatic as the reality of it actually is!

Here’s a better visual showing the National Debt Burden, with four additional elements of context…

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(Source)

Did you get what the point was in zero seconds?

Are you a whale-load more scared as you contemplate the red and the green?

Are you freaked out that if there is one thing both political parties in the US seem to be good at it is the red (!)?

That is what a good data visual does.

For the few of you that are a part of the team I lead, in addition to creating a visual for your analysis that is simple and effective, you know that my expectation is that you’ll come with recommendations on what to do.

To demonstrate that there are many paths to JesusKrishnaAllah… Here is another simple view of the debt, with a different x-axis, a stretched out y-axis, along with a different set of context…

federal_debt_past_future
(Source: CBO)

Different questions, different arguments, different outcomes. But, you’ll get to them much, much, much faster than the first visual.

I can’t stress this enough: Don’t try to earn your performance review from the client/audience. Earn it from your boss. Tell your boss how hard you worked, show her how clever you are, earn her praise. Spare your client/audience – show them the simplest manifestation of your brilliant insight, with the NACR criteria applied.

(For more on using NACR to identify out-of-sights, see TMAI #66.)

Strategy 2: If Complex, Focus!

You are going to see my deep bias for simplicity for the rest of this post (or in the 745,540 words written on this blog thus far). I do not want to come across as a simplicity snob.

Deployed well, there are instances where I love complexity.

I thought this was exceptionally well done…

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(Source: Michael Paukner  |  His Flickr collection)
(Click on the above image for a higher resolution version)

While it is a little difficult to follow all the arrows back to the original country, the shape of the graphic is an homage to the visual’s topic. The background color could not have been more prefect. And, notice there is just the perfect amount of information about every tree.

There are other more subtle things to admire. I love, love, love that Michael put the US on the right. When we “trip up” our audiences like this,  it gives them a pause and forces them to look at all the other information more carefully.

There is of course data itself that gives you many pauses. Notice the youngest tree in the graphic is older than Jesus Christ. Or, that we should all be so glad that the American West was settled last (by then we were more appreciative of nature as humans).

I am fine with complexity, if the essential makes it through. I am fine with complexity, if someone who’ll spend 1/100th of the time on the visual compared to you get’s it.

Strategy 3: Venn Diagrams FTW!

I love Venn diagrams. Ok, strictly speaking Euler. But, let’s not get pedantic.

I’ve used them to simplify the presentation of complex topics. Ex: Six Visual Solutions To Complex Digital Marketing/Analytics Challenges

I am only slightly kidding but one of humanity’s most complex undertaking is to understand what the heck Europe is. One end’s up ruing even asking, because you hear back EU, EEA, Euro Zone, Schengen, EFTA, and more.

I felt Bloomberg did a wonderful job with, what looks like an amoeba-inspired, Euler diagram…

eu_explained

(Click on the above image for a higher resolution version)

The color schemes are contrasted enough to allow you to follow along nicely.

The context from the sizes of the economy is a nice touch. (This is embarrassing but I was surprised how big Italy is, and how small Sweden is.)

The clusters of countries next to each other, for the sake of cleaner lines, all by itself has a built-in message. Cyprus and Ireland. UK, Romania, Bulgaria and Croatia. So on and so forth.

Overall, this is a topic that has been tackled numerous times, with painful to see results. Bloomberg managed to make it as simple as possible, with valuable built-in context.

Staying in the same geographic area, and my Euler-love, here’s another fantastic visualization of often a very complicated answer: What is each political party in the UK promising?

I adore this as the answer…

uk_election_manifesto_economist
(Source: Economist)
(Click on the above image for a higher resolution version)

Would you have believed that the totally out there UKIP would have something on common with Labour? Or that Labour is completely alone in the minimum wage issue?

The visual makes it easier to understand what we might be most interested in from the thousands of pages that form each party’s manifesto. You, the audience, is now empowered to agree more passionately with your party or feel the uncomfortable squirming that comes with realizing what your party is solving for. Both. Fantastic. Outcomes.

Clearly this is a political picture, and someone has to decide what to include and what to exclude because the parties promise the Earth, Moon and the Andromeda galaxy. But that is the life of an Analyst… They have to make tough choices.

Two hopes.

1. I hope every single news organization in every single country in the world will copy this visualization and create it for their main political parties. (Also see related NYT example on Guns below.)

2. What will you do with this? Can you pull out all the content types from your digital existence and create a visual like this one for which goal (overlapping goals) each type is solving for? How about displaying countries and products purchased? Oh, or your main traffic sources and the visitor acquisition metrics?

So much to do, so simply, and so little time!

Strategy 4: Interactivity With Insightful End-Points.

There is a common belief that your company’s decision makers would use data more if they could explore it – more efficiently, deeper, etc. This is almost never true, primarily due to the problem outlined in the orange and blue triangles that outline skill/competency and insights/action.

Hence, in a business context I rarely advocate for initiatives whose only purpose is to allow the broad collection of company employees to go on random fishing expeditions.

Exploratory environments can be useful, especially when they are 1. sharply focused 2. have an ability to eliminate dead end-points and 3. allow for smart elements like modeling. Let’s look at the first two below and the third one in the following example.

Here’s a valuable dataset from the Equal Justice Initiative on Lynchings in America.

lynchings_america_eji
(Click on the above image for a higher resolution version)

Even at a glance the data is useful, along multiple dimensions.

In this case exploration of the data makes it even more valuable. You hover your mouse over your area of interest, and click…

lynchings_alabama_eji

You get your data drill-down, but what’s of most impactful is that you also get an end-point with a valuable insight providing meaning to the data.

In this case the number 29 for Jefferson County would be an insufficiently valuable end-point. The inclusion of Elizabeth Lawrence’s story on the other hand provides meaning. That is what gives the exploration a purposeful end-point.

You can now zoom out, move on to exploring other areas, continuing to get enriched value from the data.

In a business context when you are working with interactive data visualizations, ask this very valuable question: In a sea of data, whose job is it to include a logical end-point with an insight of value?

Surely, your terabytes of Google Analytics data dumped into a Tableau exploratory thingamagigy won’t magically throw them out there.

Surely, lay business decision makers, even senior ones, won’t have all the context they need to have to convert thingamagigy fishing expeditions, sorry, explorations, into the brilliance you feel the data contains.

Interactive visualization are great, only when packaged with insights for actions at logical end-points in exploration. Tweet that.

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This is a difficult example to share because of the deeply emotional content it contains. But, those who do not learn from history are doomed to repeat it. Beyond the value of the lessons from the visualizations, I encourage you to explore rest of the EJI website. At the very minimum please consider spending five minutes listening to the story of John Hartfield told by Tarabu Kirkland, and six minutes on the story of Thomas Miles Sr told by Shirah Dedman. Thank you.
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Bonus: Another insightful visualization on this topic is at pudding.cool, The Shape of Slavery

mapping_slavery

A bit more complex of a visualization, a function of the depth of data populated.

Follow the story of Louisiana as you reflect on the data.

Lots of data visualization, storytelling and life lessons in this data set as well.

Strategy 5: What-if Analysis Models.

Building on the thought above, if you create exploratory environments it can be exceedingly accretive to decision-making if we build in what-if type models. Rather than stopping at an end-point, provide an option of doing some type of sensitivity analysis with the goal of prodding the audience to take action.

For example… Let’s say they end up looking at Visitors, Conversion Rates, and Revenue. You can easily imagine how you want someone to explore that data by traffic sources or campaigns or geo or myriad valuable dimensions. You can create an environment where they press buttons to get that data.

Necessary, but not sufficient.

Why not build in a model where the decision maker can change Conversion Rates, to see the impact on Revenue? Move it from 1% to 1.5% to 8%. See what happens by traffic sources. Then, make a smarter decision.

Or, empower them to play with discounting strategies. What happens if they offer a 5%, 10% or 18% discount? Show impact on Revenue and Profit.

Even without bundling insights into your prepackaged environment, the what-if models allow your decision makers to play with scenarios, understand impact and make smarter decisions about what to do.

That’s the key. Don’t make visualizations with dead ends.

Here’s a great example of that from Mosaic. The visualization is about outpacing pandemics.

Quoting them: Vaccines are an essential weapon in fighting disease outbreaks. But how does the time taken to develop vaccines compare to the speed and frequency of outbreaks? And how can we do it better?

This is the simple view that greets you, outbreaks from 1890 to 2016 with vaccine development during that same time…

outpacing_pandemics

Each element is clickable.

As an illustration, the longest bar is Typhoid fever and the smallest, mercifully, is Measles. For each bar, click on Measles, you’ll see the first big outbreak (1917, 3,000 deaths) and the last (1989, 123 deaths). It is really easy to explore the data.

What I love is the sensitivity analysis.

Click on the yellow dot, and you’ll see that in action. First, you see what actually happened…

outpacing_pandemics_ebola

Simple exploration. Good reporting. Easy to understand.

The buttons with the number of weeks represent what I wanted to highlight here. Click on them, and it demonstrates what the outcome would have been if action was taken earlier.

I choose 22 weeks…

outpacing_pandemics_ebola_22_weeks

Even if the vaccine had been introduced after 22 weeks, a long time, we could have saved 1,628 lives!

The team also built in some hypothetical scenarios to help inform decision-making.

You can play with the implications of a fast-moving flu-like pandemic. It would have grievous overall impact, 30 mil deaths in 12 months.

But, what if we restrict 50% of the travel since we don’t have a vaccine yet. That would have an impact…

outpacing_pandemics_flu_travel_restrictions

Not quite as material as one might imagine, but it slows things down.

What if a vaccine was introduced 22 weeks in?

outpacing_pandemics_flu_22_weeks

Insanely helpful. 17 mil lives saved.

This type of modeling is rarer than seeing a rhino in the Ngorongoro crater. (We were there last week, you should go, it is pretty awesome.)

As an analyst, as a Big Data person, as a Data Scientist, pouring the right data on humanity is only marginally effective. In this example, in others above, I hope you’ll see the type of additional creativity we can bring to our work to power smarter decision-making. Starting with no dead end-points.

Strategy 6: Turbocharging Data Visuals with Storytelling.

You know this. Even if data is shared in a simple environment, most people are unable to internalize it. As has been hinted in most examples today, the problem is that the Analyst’s brain has not been packaged with the data.

The Global Gender Gap Report is a fabulous example how to solve this problem. The team nor only shares in a simple and beautiful environment, they also include the story they want to tell in that same environment. The output is not the reporting, the output are the conclusions from the Analyst’s brain.

It is very difficult for me to show the beauty of what they have done in static screenshots. You just have to go there and scroll.

Explore how the initial trend in the gender gap morphs into multiple visualizations, note the subtle but important emphasis on trends, and, most importantly, feel joy from how the story is presented with the data (text on the right).

The website and visualization will work on your mobile device (yea!), but it is best admired on the largest screen you can find.

To tempt you, let me just contrast the gender gap performance of the United States (precipitous decline in the last two years!) with… with… inspired by FLOTUS, the 10 year performance of Slovenia…

gendar_gap_browser

Play with the histogram and scatterplot options.

Go back and forth a few times (yes, gender parity is an issue I care deeply about), make sure you absorb the many nuances both in the story (why the above stinky performance by the US?) and the way the text (story) and the visualization (data) play together.

When you send data out, is it bundled with a piece of your brain?

Remember, you’ll be the last person with the intelligence and skills to understand the deep layers and nuances in what the data is actually saying (assuming you are an Analysis Ninja!). It is imperative that your brain go with the data.

Bonus 1: Another fantastic example of this type of sequential storytelling is Film Money….

film_money

Lars Verspohl takes you along on a wonderful journey through cost and profit structures of movies. Like me, you’ll love the simple and delightful visualizations, how gracefully flow it all flows, and that all the charts and data are primarily there to support the story that emerges from his analysis.

Please also note the thought put into the order in which the story is told, if and when the visualizations switch (from the one above) and the techniques deployed to keep you interested. All excellent, loads to learn.

Bonus 2: This is one subject, storytelling, that I just love, love, love. Indulge me as I pile on and share one more, dramatically different, example of storytelling where data and text go hand in hand.

The team at Reuters Graphics does a fab job of explaining China’s debt problem.

china_debt_reuters

Almost all the visuals are extremely simple. As you scroll through, observe though how they peel back layers of the onion one by one, segment the data, and zero in on the core point they want to make.

Really lovely. Worth emulating.

Strategy 7: The Magic of 2 x 2 Matrices

If you’ve read anything on this blog, you’ve read the importance of seeking why answers to provide critical context to the what answers that you get out of Adobe or Google Analytics. Hence, the amazing value of Surveys, Usability Studies (on or offline), Heuristic Evaluations, shadowing Customer Service calls, and more.

Customers are an amazing source of problems they are having, sometimes they are also a good source of ideas. The challenge is that if you ask people for their opinions you get tons of ideas.

How do you value them? How do you present them? How fast can you get from data to action?

One solution I love is a visualization strategy used by the team at the New York Times. The example illustrates, simply, the ideas related to an emotionally charged topic: Gun Control.

Everyone knows this is a polarizing topic. Friend against friend. Blue vs. Red. Police and minorities and every other combination thrown in. It is a mess.

But. Is it really as fraught with angst as we believe?

No. It turns out if you ask Americans about individual ideas that will reduce gun deaths… A vast majority of us agree!!

nyt_gun_control_ideas

The lowest supported idea is “Demonstrate need for a gun.” Support for it is just shy of 50%. A number that simply sounds unbelievable. 

Did you think vast majorities in our countries agree with these common-sense ideas? I have to admit I did not. It is hopeful data.

But, this is not the reason for the inclusion of this visual on our list.

Rather than just share the ideas, the NYT team added incremental value (remember packing the Analyst’s brain?) by asking Experts to opine on the effectiveness of each idea. That’s what you are seeing in the distribution above.

From the 2×2 matrix, here is the slice of ideas American’s support and the ones Experts say are effective…

nyt_gun_control_ideas_effective

There are only two ideas rated as ineffective by Expert, but are supported by over 70% of the Americans (national stand your ground law and honor out-of-state conceal and carry permits).

We all basically agree on ideas, and a lot of them will have an impact.

I love the presentation of the ideas and the fact that Experts were brought in to give valuable context. This is what I meant in my above example by not simply taking all the customer ideas and running with them. A wonderful way for you to visualize multiple ideas, and you can combine it with an Expert dimension or a Customer Satisfaction dimension or even a Revenue dimension to give context to the ideas.

One last element of value from NYT.

I’ve said that all data in aggregate is crap. I’m so happy that the NYT team also segmented the data.

What does Mr. Trump support…

nyt_gun_control_trump

What do American law enforcement support…

nyt_gun_control_police

And, lots more slices that make the data even more meaningful.

Segment. Always, always, always segment!

It is beyond the scope of this humble analytics blog to explore why in the face of such unanimity that nothing actually happens when it comes to reducing gun violence in the US. But, for lovers of data, for believers in the power of data to drive smart decision-making, this is one more reminder on the limitation of data if you can’t tell the story properly.

Strategy 8: Close Contextual Clusters.

Let’s close with examples of work that you’ll normally include in your enterprise analytics efforts.

Usually data we have is lonely. Just the Visits or Assisted Conversions or Order Size. Without other contextual elements, it turns out this data is less useful.

Consider this, conversion rate could go up by a statistically significant percentage… While revenue actually goes down. Or, the overall Visits to the site stay steady… But drop dramatically from your usually second highest source.

The European Monitoring Center for Drugs and Drug Addiction, also known by the gorgeous acronym EMCDDA (!), publishes a ton of data. Their Statistical Bulletin 2017 has a lovely collection of graphs and charts that we all use in some shape or form. The only difference is that we rarely report on Heroin Price and Purity. 🙂

emcdda_europa_heroin

Along with the use of (mostly) simple visuals to illustrate the data, I appreciated the context that they provide. Sometimes using the time dimensions, sometimes using geographic breakdowns, sometimes using two likely interplaying elements (like above), so on and so forth.

This simple strategy is quite effective at delivering insights – or at least causing the audience to ask relevant interesting questions.

I encourage you to take some time and explore the numerous examples on the site

emcdda_europa_charts

I’m confident the visualization strategies will spark upgrades to the work you are doing at your company to communicate data more effectively.

Our friends at the EMCDDA mostly avoid two things that I find as poor practices in data visualization. They triggered this in my mind, let me take the opportunity of sharing them with you.

1. Never ever, never, never, never create the loooooooooonnnnnnnggggggg infographics that seem to be in vogue these days. Essentially they are taking 69 “slides”/graphs/tables and shoving them into a 9-meter-long thing that no browser can render decently. By the time you absorb the third screen full of stuff in tiny font/image, you’ve already forgotten what’s on the second.  You have many examples in this post as to how you can avoid making yourself look like sub-optimal Reporting Squirrel.

2. Pie-charts are a very poor data visualization choice. Humans find comparison by angles significantly harder than, for example, by length. I explain this a lot more in the May 14th edition of my newsletter The Marketing Analytics Intersect: Eat pies, don’t share them.

[You should subscriber to TMAI for a weekly dose of intelligence that’ll keep you at the bleeding edge of our industry.]

Bonus: In the spirit of government data, I’ll be remiss if I did not share with you three examples of interactive scatter plots from Our World in Data (produced by the University of Oxford).

The second one is timely, it shows how when we look at health spending and life expectancy the United States is a massive outlier (and not the good kind)…

owid_life_expectancy_health_expenditure

I love fusion charts, the first one on the site, Child Mortality vs. Mean Years of Schooling, is a good example of that as well. And, it shows great news.

Please review all three. Then, consider plotting one for your digital data. Conversion Rates by Discounts for Top Ten Traffic Sources. Time on Site by Visits to site for Content Types. And, more.

Strategy 9: Multi-dimensional Related Line Graphs.

One final example, to cause introspection about the final years of your life.

Wait. Things really got serious.

They did. But, I really do want you to lean into this one.

A small reason is that you are likely creating graphs like these every single day for your dashboards. I hope you’ll find lessons in how to make yours simpler. Notice the use of fonts and colors. Notice the labeling, or not, of the axis. And other little things.

A big reason is that I care for you deeply and I want this data to be a cautionary signal to all of us to possibly start making new choices.

The plots are from the American Time Use Survey, a multi-year study from 2003 to 2015 conducted by the US Bureau of Labor Statistics.

Age on the x-axis and hours we spend per day with on the y-axis…

american_time_use_survey
(Source: halhen on Reddit  |  Github)

In our 20s we’ll spend most time with our friends and our parents. Our partner and co-workers will take over our lives from then on through our 50s.

I’ll let you internalize the rest, and please share via comments what you see as the lessons in this data.

Three things stood out for me, as I consider the larger latter chunk of life. 1. We might be giving an extraordinary amount of importance to our co-workers, perhaps worth a rethink. 2. I love my spouse, regardless of who goes first, I felt very sad after staring at the Partner and Alone graphs. 3. The data demonstrated the value of loving oneself – of being proud of who you are, of being comfortable in one’s own skin. After all each individual will spend huge chunks of a decade plus… alone. You have from now until you are 50 or so to get there. Hurry!

: )

The power of great data visualized simply.

Closing Thoughts.

The sixteen diverse sources and visualization strategies help you think differently about how you are bridging the critical last-mile when it comes to impact from data – from you to the person who’ll take and action of business value. We don’t give enough time and attention to this last-mile.

While some of these clearly take special skills (especially the ones that tell integrated stories), I hope you’ll note that most of them are simple and ones that you can create with just a little more effort.

What’s most important today is that I’ve sparked your commitment to upgrading your personal data visualization skills.

Good luck!

As always, it is your turn now.

Which one or two examples did you like the most? Why? Is there a visualization technique you deploy in your analytics practice that’s not covered in this post? What barriers prevent you from improving your data viz skills? What are your pet peeves when it comes to data visualizations? Do you have go-to sources when it comes to inspiring you?

Please share your tips, best practices, critique, and praise for the people who created the above examples, via comments.

Thank you.

PS: I was not kidding in the opening of this post… I’ve written a lot about data visualization and shared guidance for this type of storytelling in numerous different contexts. To continue your immersion, here’s another collection of knowledge…

I hope you love it, and paint more beautiful pictures with your data.

Create High-Impact Data Visualizations: Nine Effective Strategies is a post from: Occam’s Razor by Avinash Kaushik


Source: Avinash

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