Arranging Logos on a Slide

Even skilled graphic designers are frustrated by the never-perfect task of arranging logos on a slide. So don’t feel bad. It’s a pain, no question.

You probably have to arrange logos on slides or a website to thank sponsors, acknowledge partners, or brag about your client roster. In this example, I’m illustrating the point that people who join our Evergreen Data Visualization Academy come from quality organizations that care about the effectiveness of their communications.

It’s a challenge because every logo is a different shape – Colorado is a triangle for cryin out loud – and size and it can seem like the only solution is to just throw them randomly on a slide. Look, there isn’t going to be a clear-cut, always works answer to this dilemma, but I’ll show you my process in hopes that something like it can work for you.

Mandatory preface: not all organizational are cool with you slapping their logo on your slide or website. So double check – it should be listed in their style guide or maybe in your contract. Speaking of style guide, some of them will dictate things such as how much of a buffer must be around their logo and minimum size requirements, so keep an eye out for rules to follow.

First, I start to collect logos of similar sizes: long, horizontal logos on the left; vertical logos and circles in the upper right; and short, rectangular logos on the bottom right. It’s already starting to feel a little less random. Grouping like with like here will help me maximize the available space and increase logo size (hopefully handling any minimum size requirements).

Notice that I actually swapped Kaiser Permanente’s centered logo for their horizontal arrangement. Center style logos can be difficult to arrange with other logos, whereas the horizontal styles play a little nicer together.

To help me organize even further, I dropped in a table for the long horizontal logos on the left, zoomed in a lot, and tried to fit each logo in a table cell, as large as possible. The table helps me create a grid system so that there’s a clear structure for the logos. Notice that my table has extra .5″ columns and extra .3″ rows so that my logos will eventually have some breathing room between them. If I didn’t have those buffers and just filled every cell with a logo, the slide would look tight, dense, and overwhelming.

I added another table for the short rectangular logos in the lower right.

And another for the logos in the upper right. Note that this final table has rows that are exactly twice as high and the other tables. That precision gives the entire slide a grid structure, even though the elements I have are all different sizes and shapes.

Logos aren’t necessarily going to fill each table cell. So I rearranged some of the long horizontal logos on the left to try to collect the extra white space around the ones in that table that are a bit shorter.

Then I started viewing my slide full screen to get an overall sense of the scene. I decided the Goodwill logo was pretty powerful and eye-catching, so I moved that entire table to the lower right.

What followed was 10 quiet minutes of fidgeting with each of these logos. You see, it isn’t possible to simply use PowerPoint’s Align tool to make these logos look perfect. Because some logos have a bit of white space inside them, like Yale University, while others have almost no white space at all, like The World Bank. If I used PowerPoint’s Align tool, the logos would be technically aligned but would visibly look like they are off.

AREN’T LOGOS FUN?

So I had to visibly align. I had to eyeball it. Which requires a lot of little nudging. I resized Microsoft’s logo just a bit so that the t lined up with the end of the World Vision logo. I lowered the Kaiser Permanente logo so it visually lined up with Northland Foundation. Nudge, nudge, nudge.

Then I deleted the tables. Then I nudged more. It isn’t perfect. But, given what I have to work with here, it is pretty darn good. And its the implementation of the grid that makes “pretty darn good” possible.

One extra trick for ya: If lots of large, easily visible logos are still feeling a bit overwhelming, if things are just feeling nuts, you can muffle some of the noise.

By making all of the logos grayscale. This brings an element of visual consistency and cuts down on the visual activity levels. Not all style guides are ok with grayscale but most of them will provide a gray scale version.

Dealing with logos is never a clean and tidy process but you can make it work with a set of tables that act as a grid and some old school eye-balling it.

The Data Visualization Academy is full of some fine folks. Twice a year we open enrollment to 100 people. Now is the time! If you are ready to massively improve your data visualization and reporting skills, learn more and snag one of the open seats.

Histomaps

The histomap is one of the oldest, and most famous, ways to present qualitative data over time. The first example was created by John B. Sparks in 1931, titled “The Histomap: Four Thousand Years of World History.” At the time it was printed, the chart was marketed as a clear, non-elaborate way to engage lay people with complex world history. I am not sure I would quite describe the massive 5 foot visual in the same way today, but it does do an impressive job of distilling power and history over 4,000 years into a relatively short display. To check out the visual, you can see a nice zoomable image here.

Histomaps can be a good option when we are looking to visualize qualitative trends over time. The trick is that you need to be using two mutually exclusive variables. In Spark’s case he used time and power. In the example below, I am using time and staff’s satisfaction with their work environment. Imagine you are collecting open-ended survey data every quarter during your grant term. You code a person’s response into the mutually exclusive category of met, partially met, and not met regarding their expressed satisfaction. At first my Excel document looked like this:

Then, I calculated the percent and used that as my graph’s source data:

You then chart this data using a stacked area graph. Each group (met, partially met, and not met) is plotted as a different series. At first, the graph is horizontal and looked like this:

We need to turn this so that time is along the y-axis. I just copy and pasted the graph as an image and rotated the graphic. I then added text boxes and lines for the y-axis and added x-axis category labels.

This is a great example where added annotations can become useful. What was going on at the end of Q4 in 2016?  There was a downward trend in satisfaction until the big jump in Q1 2017. Maybe there was a toxic staff member who was let go?  Maybe a grant that funded staff salary was ending in Q4 2016 and staff were feeling insecure and nervous about their job. At the last minute that grant was renewed!  Whatever it was, an annotation can help provide more in-depth context. Since this is graph is backed up by qualitative data, let’s use it to tell the story.

Another way to think about using a histomap is to take a qualitative question and categorize the responses into themed buckets with more detail than met, partially met, not met. If you asked during a focus group what students would change about a program, you could categorize the responses into themed buckets like: teacher, curriculum, logistics, peer interaction, etc. Then those buckets could be used as your variable tracked over time.

There are so many fun ways to utilize this visual for qualitative data. Take a stab at it and let us know how it goes. Happy visualizing!

Jennifer Lyons authored this post. She is a senior associate at Evergreen Data who designs high impact visuals for clients and delivers Evergreen Data workshops around the world. She is the in-house qualitative visualization expert.

Graph Text Should Be Horizontal

In languages based on the Latin alphabet, we read horizontally, from left to right. Reading on a diagonal produces cramped necks. Reading vertical text is just not going to happen. So, as much as possible, the text in our graphs should be horizontal.

Let’s walk through a demo. I was trying to combat my sense of hopelessness about the world by exploring the latest dataviz related to the Sustainable Development Goals (don’t we all?). The vast majority of their viz is pretty awesome, especially given how complicated the data can be. I saw this graph about how few developing countries have representation on international development councils.
And while I get that we typically show change over time as a line graph, I’m not sure that’s the best chart type for this data. First of all, the data are flat, so there’s no story in a trend. Arguably, there could be a story in a flat line but the subtitle here explains that things won’t shift much in this dataset. I don’t think there’s a need for a line. Second of all, many data points are clustered at the bottom of this graph, making it super hard to see what’s going on. (Some of those folks are my clients and I care about their data!) If we show just the most current year of data, we will be able to separate that cluster and see each international organization.

I often have to prescribe this mantra to the organizations I consult with: Better as a bar. This graph is one of those cases. But the default bar chart does the data no justice.

Sure, the graph type may be appropriate now but the text is all over the place. What’s the point of a vertical, hard-to-read y-axis label if it is pointing out something totally obvious by the chart’s own title? Redundant and hard-to-read! Let’s ditch it.

The text along the x-axis is perhaps worse. The labels are so long that the text is slanting diagonally and some of the labels are cut off, literally making them unreadable. We can try to decrease the font size so that the text shifts back to horizontal.

I had to go all the way down to font size 4 in order to get horizontal labels for every column in this chart. Most readers will quit even trying to engage with the graph if the text is this small. As we point out in the Data Visualization Checklist, the smallest font size you can reasonably get away with in newspaper-distance reading is size 9. Plus, some of the labels are still so long they are wrapping down onto multiple lines. If this is happening to you or your text is diagonal, consider it a big hint that your graph type needs to swap from a column to a bar.

The category labels will automatically become horizontal at a text size that people can see. It’s an improvement but I’m still missing part of one label (you can tell by the three trailing dots). This is why I love incorporating condensed fonts into my graphs. Condensed fonts are tall and skinny, helping us fit more words into the same amount of space. In my final graph below, I used Roboto Condensed.

I also had to make the graph just a little larger. This data includes a lot of categories with wordy category labels, so the whole graph needs to increase in size to accommodate. Rather than the default 3″ x 5″ graph size in Excel, the new graph is 4.2″ x 6.1″.

The numerical axis was overkilling it with all those decimals so I deleted it. I also wanted to add the precise values because almost half of my categories had values between zero and the first gridline.

I thickened the bars, changed the default color, deleted the chart border, and added a clearer title.

Finally, a readable graph! Focus on keeping the text horizontal and several other checkpoints from the Data Visualization Checklist will fall into place like magic. Test your own graph against the entire Data Visualization Checklist on our new, interactive website.

The Link Between Graphic Design and Report Use

Though I have a PhD, I left academia to become a consultant and it was the best decision I ever made. But I’m still a research nerd at heart so when I had a chance to run a study that examined the link between graphic design and report use, I had to take it. I’ll tell you about the study and what we found in this post. We submitted a full paper on this to two journals and got rejected from both (and I’m telling you anyway, in the spirit of #ShareYourRejection). I know academics have more incentive (tenure!) and fortitude than I because after these two rejections, I tapped out. I don’t have the energy to keep submitting but I hope you’ll read through our study and findings and suggestions for continuing the research.

Nearly all research on the impact of graphic design or visual enhancements to reporting is simulated. Studies will use technology like Mechanical Turk to ask what they assume is a random sample of people whether they would, in theory, use the page being shown to them on their screen. Or, more accurately, the researchers ask if the page is appealing, assuming that appeal leads to use. Or, more accurately, the researchers ask the respondents to rate the extent to which they agree that the page is appealing, because Likert scales are easy to work with.

But that’s a far distance from actual use by real people in real decision-making scenarios. We conducted a study that works with reality.

I’ve been promoting graphic design as a skill everyone should learn since 2009. I now spend my days travelling around the world preaching this to organizations, all on the theory that graphic enhanced reporting makes it more likely to be seen, easier to digest, thus more likely to be remembered and therefore used. At least I was calling it a theory. And my dissertation relied on plenty of research that could connect the first four steps of this theory of change but we were all just making a leap of faith to get to use.

So let’s cut to the chase and then I’ll give you the back story. Did we find a link between graphic design and report use?

Not so much.

We examined the correlation between how often a report was used (cited) in congressional testimony and the extent to which that report incorporated good graphic design principles. There’s pretty much no correlation.

Why congressional testimony? Well, we were working with data collected by someone else. But more importantly, it represents actual report use in a real-life use scenario where the “subjects” don’t know data is being collected on them. We calculated use by looking the citations in recorded congressional testimony.

Real life use of reports and data is, of course, much broader than congressional testimony and I sure hope others will build off of this study and explore use in other settings.

What qualifies as good graphic design? I pulled together a checklist of good graphic design as part of my dissertation research. We had multiple trained raters review each cited report against the design checklist and correlated those scores with the number of times the report was cited in congressional testimony.

Way more details in the paper.

Not great news, I know. But let this be the first step in studying an area that urgently needs more research. I’m pretty sure there are some mediating variables, like politics and networks, that also influence whether a particular report makes the rounds among policy experts. Anyone up for studying those?

In fact, Dr. Sena Pierce just wrapped up her dissertation on the same dataset. She used the Data Visualization Checklist and multiple trained raters to score just the visuals in those cited reports and she did find some notable correlations. She’ll be publishing more on that soon. A second outcome of her study was the validation of the Data Visualization Checklist, which now has an interactive home.

Considerations for the next generation of researchers

The link between design and use is critical for any field engaged in evidence-based decision-making. As we note in the paper, we have much research on graphic design and data visualization, and reporting, by nature, should be actionable and useful yet the linkage isn’t supported by evidence. In addition, this study relies on secondary data which is always a strength in resource-constrained environments but relying on that secondary data ended up limiting the design and analysis because of issues such as skewed data.

Further, future studies need to account for the complexities of use and we included some suggestions in our current paper (e.g., multivariate analyses, more content analysis in addition to graphic design considerations that could serve as mediator variables) to help address this. Even the definition of “use” can be challenging and future researchers should work to identify, as we did, use scenarios that can be controlled without introducing researcher effects. Please read up on what we started and take it to the next level. Help advance the research in making connections between data visualization and use.

Book to Read: Designing Quality Survey Questions

I still have a landline. I’m not an old, but I conduct a lot of webinars about data visualization and reporting and the sound quality is so much better on a landline. But it is a midterm election year which means my phone has been ringing several times every day with calls from poll and opinion firms wanting to ask me questions about my voting intentions. I wish they all had read Designing Quality Survey Questions by Sheila B. Robinson and Kimberly Firth Leonard.


Why? Because Sheila and Kim fundamentally come from a place that respects the survey respondent. My favorite quote in the book:

We take to heart the notion that a survey is a form of conversation and social exchange and that respondents are rarely, if ever, compelled to answer questions for us. This drives us to use a “respondent-centered” design.

Amen. And then they spend the rest of the book telling you exactly how to do it.

If you have been steeped in survey research for most of your adult life, like me with a PhD in interdisciplinary research and evaluation, some of the book’s content may seem familiar. Even still, I will always chuckle at a triple-barreled question so I welcomed the review.

But its been a decade since I took my survey classes and, while Sheila and Kim cited some of my long-time favorite studies, times have changed. This book has so much juice in it!

Three cheers for…

The strong recommendation to only ask questions you have a clear plan to use, along with solid recommendations on what questions to cut or consolidate. Yes please! Don’t ask about my gender if you don’t need that information.

And if you do, this book has thoughtfully addressed how to word survey questions related to sensitive topics like race, ethnicity, gender, income, and religion. We did not address this enough when I was in grad school.

The few times open-ended questions make sense and how to introduce them so people will actually fill them out.

Whether to have a midpoint in your Likert scale (I’m team NO).

How better questions can help us avoid a “don’t know” response.

Clear, detailed, and realistic discussion of what respondents tend to think when presented with common survey issues.

Survey design considerations for respondents who have low-vision or blindness.

Fun ways to encourage responses.

And the narrative is accompanied by smile-worthy cartoons from the one and only, Chris Lysy, as well as adorable icons that make me jealous I didn’t have those in my book.

I’m cheering so hard because anyone who runs in the survey world needs to read and integrate this stuff and the other good ideas in this book.

What I Missed

I so wanted to see Sheila and Kim expand on their initial discussion of sampling. I know, I know. That’s a whole different book. I’m just frustrated by seeing folks put surveys out on Twitter (a convenience sample) and draw conclusions about an entire population (“People who do data visualization are most likely to write code.”) when they can really only draw conclusions about the tiny subpopulation that took the survey.

Sheila and Kim can’t save me from every frustrating thing about being a survey respondent but this book is so good I’m sure I’ll hear it reflected when I answer the phone in the future.

My favorite way to show Likert scale responses (withOUT a midpoint) is here. If you do end up with a lot of Don’t Know or missing data, here are some options for ya. Check this page for resources on how to handle those open-ended questions.

Capture Qualitative Data with Change Photos

Evergreen Data’s Jenny Lyons is back with more ideas for visualizing qualitative data.

Engaging our audiences in the complex, intricate stories of our qualitative data can be difficult. Make qualitative comparisons come alive with change photos or graphics. The hurricane this past year in Puerto Rico left many devastated and without power, homes, and community. It is hard to imagine the damage that was done. One of the best ways to visualize the impact is through a photo pair like this:

Photo credit: RICARDO ARDUENGO/AFP/Getty Images

This combination of photos  shows (above) cars driving through a flooded road in the aftermath of Hurricane Maria in San Juan, Puerto Rico, on September 21, 2017 and (below) the same highway six months later. Even still, six months after Hurricane Maria hit the island, many remained without power. Photos like this make the change and impact come to life, way more than a simple narrative story. The damage done is staggering.

When we teach people about the power of data visualization at Evergreen Data, we often use change photos. We will even take our clients own data and past visuals and redesign them. It is often these before and after visuals that win people over. Below is an example from a blog Stephanie wrote on the transformation of some business slides.

I want to challenge you to think both abstractly and literally about visuals that could visualize your qualitative findings.

Example 1: During key informant interviews you learned that by implementing a new health advocate program at the health center, clients felt like the process of being connected to services became streamlined rather than the usual complicated steps and missed referrals.

One way to think abstractly thing about this is with a road.  Before this program, clients had to navigate a complicated and windy road from point A to B.  With this new resource program, health advocates help clients go from point A to B down an easy, simple road.

Beside these change photos you could include some client quotes that are associated with the finding.

Example 2: When surveying staff, you found that before the non-profit program started using a new data entry and database management system, data was entered and stored in many different ways. Basically, data collection and management were a hot mess. Once the new program was implemented, data are cleaned and organized in a stress-free accessible manner.

The great thing about this example compared to the last one is that it uses real images. When I look at that pile of haphazard papers, it gives me anxiety because I can more realistically imagine this scenario. Using real photos over diagrams or graphics is always best if you have the choice.

Example 3: You found during a series of focus groups that clinicians didn’t have a team to work to solve programs with felt isolated, stressed, and alone. They were less likely to serve clients with innovative solutions. When clinicians worked on a team to support clients, they were more efficient and confident in the services they provided.

If you can’t use photos of the actual clinicians in the study, you can build a set of change photos using paid stock photo sites. First, I searched on “stressed doctor” at Shutterstock. I found one image I liked and clicked it. Below the image, you’ll see a set of photos with the same model, so I can quickly locate the same “clinician” looking happier (and apparently drinking a beer, too).

No matter your project scope or the availability of photos or resources, I encourage you to brainstorm ways to visualize your qualitative story. When examining the findings associated with your project, think of both literal and abstract ways to pull at people’s visual brain and heart strings by using change photos.

Until next time!

<3 Jenny

PS. This post is a preview of what’s to come in the second edition of Effective Data Visualization. We have overhauled the chapter on qualitative visualization. You’re going to love it! Expect it to hit the shelves next Fall. 

Ways to Visualize Cost-Benefit

Cost-benefit analysis a way of saying “Yes, this program has great impacts… but it’s super expensive.” You’ll often hear the results of cost-benefit analysis in the news, phrased as “For every dollar we spend on this program, we save $$$.” Clients at the University of Alaska, Anchorage (I HAVE THE BEST CLIENTS!) recently asked me for ways to visualize cost-benefit. Visualize??? We usually don’t *see* cost-benefit, we just hear it stated as a sentence. And sometimes that will be your clearest method of communication. But here are three possible ways I thought of to visualize cost-benefit.

Icons

We can turn a simple statement into a visual by adding some very basic icons to each part of the statement, helping us tell our story.

Of course, how you frame your cost savings makes a difference in how people are motivated. So if showing off the benefits doesn’t work, you can always visualize where the savings actually come from. For most social programs, it comes in avoiding payments on things related to crime and justice.

Photographs

If icons aren’t cutting it for you, you may be able to make stronger connections with your potential audiences through photographs. Again, these mainly serve to support the main sentence. In this screenshot from President Obama’s State of the Union, they used a photo of him in a classroom to illustrate the environment where we need to do the investing.

Quadrant Plot

Both of the previous examples are pretty basic and assume you already have a condensed statement. If you need to weigh out several options, a quadrant plot may be your answer.

The quadrant plot allows you to see how several alternative options shake out. It’s just made from a basic scatterplot in Excel, where I assigned numerical values to every dot. I labeled either end of both poles with textboxes.

What other ways can you effectively visualize cost-benefit? (I asked this on Twitter and got a bunch of responses about the cost-benefit of visualizing and that’s a whole different animal, my friends.) Email me with other emails and I’ll update this post.

Data + Food

I like to think I’m a foodie but actually I just love to eat. We all do! Food gets people to the meeting. Food grabs folks’ attention. Food gets people talking (between bites, let’s hope). So let’s play to the player and figure out how to get people engaged in our data via food.

One idea is to actually put the data inside food. I love this idea. But are you trying to send your data to policymakers? Cause they aren’t going to touch your homemade Findings Cookies.

In our latest work with Michigan Fitness Foundation, we took data + food in a different direction. Inspired by visuals produced at the Robert Wood Johnson Foundation, we used Michigan-grown food images to visualize data about their programs and how their work makes Michiganders healthier and more productive.

MFF recognized the importance of accountability and showing the impact of their publicly funded SNAP-Ed work. The public and policymakers have a million requests pulling at their attention, so we went for something short – a one pager – and visually engaging. We wanted memorable graphs that visually connected to the topic to make a lasting impression after the (max) 30 seconds of initial attention we are likely to get.

MFF had 3 messages to convey. So after leaving space for the heading and a footer, we broke up the page into 3 main sections, assigning colors from their brand to each. We helped them craft two essential points to serve as the evidence for each message. Then we developed visuals to illustrate those pieces of evidence. And the visuals are not just pictures – they are tied to data. Those little broccoli and strawberry graphs were actually made in Excel, yo.

Not all data is easily visualized. We went through multiple possibilities for the section in the lower left before landing on the best one.

I don’t know about you, but just looking at these graphs makes me crave a salad. And when I fork a carrot circle, I’ll be thinking about this Michigan Fitness Foundation one pager on SNAP-Ed in Michigan. Because that’s how data + food works.

Sidebars are Your Friend

Sidebars are an unsung hero of reporting.

Sidebars are a way to offload details such as your methods or caveats about your data that are related but not a direct part of the narrative.

Sidebars are not call out boxes. Call out boxes are for giving a tiny shout out to parts of your content. Call out boxes are usually bright and attention-getting, so people will look at them. Sidebars formatted so that they hide a little. We use muted colors to deemphasize things like (and this might hurt a little) the details of your data analysis.

Other content that belongs out of the main narrative and in a place like a sidebar because, though it might be important for you to state, people don’t care as much as your primary points:

Acknowledgements
Mission Statement
Vision Statement
Data Collection Methods
Who Commissioned the Report
Background on your Organization
Contact Information
Definitions

Put that ish in a sidebar!

In this report from OPE, sidebars are inserted on several pages to contain somewhat irrelevant information.

Gorgeous! But perhaps more important, this is also where the team defines acronyms that appear in that section. That’s one way to make reading long reports even easier. Usually what I see is a page in the front of the report listing the definitions of all acronyms. But this isn’t a great idea. Why? Well, because people read reports online these days. Back when we always read reports on paper, sure, it might have been handy to dog-ear the acronyms page so you could quickly return to it when you forgot what a certain acronym meant. But people don’t dog-ear PDFs. In PDF land, people scroll right past all those boring pages up front and then have to scroll back and forth, getting more annoyed with you at each scroll, to remember those acronyms.

A smarter idea is to define each acronym close to where it is actually used in the report using sidebars. So we have made it easier to understand our writing with better acronym placement, provided an appropriate line length for reading, and added in some white space? Yes, please!

Check out more examples of awesome sidebar usage and how to format them in my latest book, Presenting Data Effectively.

Why No One is Reading Your Report

Here’s the hard truth: Your report probably sucks. Mine sure did. The heart of your content is likely fine, maybe even helpful. But, if you are anything like the hundreds of reports I see every year, the entire set of cultural norms we have somehow developed around reporting is just setting us up for failure, writing a destiny where no one is reading the report.

Why? Let me lay out the most common issues I see and propose some strategic solutions.

It is way too freakin long.

Sometimes I have folks in my workshops who sheepishly admit that their reports run over 200 pages. What? It’s painful to write a 200 pager, and even more painful to read through it. Even a 75 page report is going to feel like a burden to anyone who needs to read it.
Solution: The 1-3-25 Reporting Model.

This reporting model asks us to break down our work into a 1 page handout, a 3 page executive summary, and a 25 page report, with as many appendices as you’d like. This way, no matter what level of interest someone has in our work, there’s a document of appropriate length that matches their interest.

The good stuff is buried in the back.

No one wants to slog through pages and pages on your methodology before getting to the good stuff. We have been trained to report our findings and conclusions last but it is time to rethink that training because it usually isn’t the best fit for an audience that typically wants to cut to the chase and learn what you know.

Flip the report order so that you start with what people came to hear. Stick non-essential stuff in a sidebar or in the appendix.

It is written in some faux academic jargon.

I find it fascinating how otherwise plainspoken people completely switch to a different language when communicating for work. Whether on a slide or a page, people swap easily understood sentences for passively-structured, jargon- or acronym-filled, faux academese. It is an odd language that, I think, is used with the intention of sounding objective and smart. But it isn’t reader-friendly.

Join the Plain Language initiative. Write like you would talk to a group of smart middle schoolers. It doesn’t mean dumbing down your content. It means writing in a way that is accessible.

It lacks visuals.

Photos and graphs engage hearts and minds in ways that text cannot. You do not need fancy software or extensive new skill sets to incorporate more visuals. I have dozens of ideas for you in my Quantitative Data Visualization Collection

and Qualitative Visualization ideas are here in force, too.

It looks like it was made in Word.

If it looks like Word and it smells like Word, chances are it is as boring as Word. This isn’t to say you can’t use Microsoft Word for your reports. You definitely can. Just don’t use the default selections for things like font and color. Definitely do not use the default Word art, table formats, or diagrams. For more advice on what to do instead, check out the Report Guidance I recently published with the Kauffman Foundation.

Much of this applies even if you reporting in, say, a webpage instead of a traditional PDF.

I’m laying out 5 of the most common reasons that reports fall flat. Can you tackle these 5 things? Yes, of course. It’s just 5 things. But these 5 things are monumental. They’ll completely shift your reporting culture. You’ll become more widely respected and well known in your field. And it might take you some time to implement all 5 of these changes. If you need a jumpstart, get in touch with me. At Evergreen Data, we can set you up with reporting templates that build in these ideas from the ground up and help you take a giant leap forward.

From the blog