Neutral Isn’t Neutral

When you are asking your survey respondents to report their feelings or sentiments, it can make sense to provide a neutral response option. Sometimes.

Other times I think we provide that option just because we think we should, because of convention. Dr. Sheila B. Robinson discusses when a neutral option makes sense and when it doesn’t over on her blog.

If it is truly appropriate that a survey respondent would feel neutral about a topic, it is definitely ok to add it as a midpoint in your response options.

From the respondent point of view, neutral makes conceptual sense in between agree and disagree.

But when we are reporting this data out and using it to make decisions and take actions, we don’t feel neutral about neutral anymore. We interpret neutral through the lens of our decision-making scenario. Let me explain.

The most common way people visualize Likert-ish response options is with the traditional stacked bar.

I’ve talked before about some of the drawbacks of stacked bar charts, but I have one more bone to pick here, my friends. How would we interpret neutral if we were to tell some stories about this dataset?

You might offer me something like “Some respondents were neutral about whether we should hold these classes.” Sorry, but WTF are we supposed to do with that? It isn’t an informative interpretation of the data in real world decision-making scenarios. There are budgets on the line. There are instructors who might be out of a job. You need to make some judgment calls here.

You either want people to feel neutral or you don’t. Neutral is either a desirable response option (for you, as the data interpreter) or it isn’t. Let me explain.

My recommended alternative to a traditional stacked bar is a diverging stacked bar, where the data line up around a preferential cut point in the response options. The chart splits into the response options you want vs. the response options you don’t want.

It looks like this with a 4 point scale:

where it is a bit easier to visually compare positive and negative sentiment because the data share a backbone between agree and disagree.

When we add in a neutral option without taking on the lens of the data interpreter, I see people making diverging bars that look like this.

Sigh. This cuts off all the power of a diverging stacked bar. Moreover, it suggests we can conceptually divide neutral people equally into positive and negative sentiments, which is not faithful to their intended responses. This just doesn’t work.

The more accurate take on this would be to keep neutral people as a whole group but make an interpretive judgment call about whether you prefer neutral or not and group them to one side of the chart.

It is easier to do this if you put yourself into the mindset of your decision-makers. Let’s say you are the teachers’ union, advocating for jobs for your union members and a well-rounded education for all children. You would be interested in showing support for all classes, so you’d probably want to include neutral people with the positive sentiment, assuming you can win those respondents over.

Grouping neutral with one side of sentiment gives neutral storytelling power that it didn’t have before.

Would you recommend this course to others? Uh, I don’t want people to feel neutral about my course, I want them to be cheerleading advocates. So I’d group it with the negative sentiment.

How was your shopping experience today? Honey, neutral isn’t going to get shoppers to return.

The point here is that when we are interpreting the data, we have to do something with this midpoint. Neutral isn’t neutral.

Asked and Answered: Visualizing Demographic Data

This blog post is part of a series called Asked and Answered, about writing great survey questions and visualizing the results with high impact graphs. Dr. Sheila B. Robinson is authoring the Asked series, on writing great questions. Dr. Stephanie Evergreen is authoring the Answered series, on data visualization. View the Asked counterpart to this post on Dr. Robinson’s website.

I’m not really sure if this still even needs to be said but if you are reporting more than two or three demographic groups, do not use a pie chart.

It is too many wedges for accurate judgement and the default legend requires a lot of eye-bouncing. And eye-bouncing means brain-bouncing and when brains bounce we lose retention.

Of course if you only have a couple of demographic groups to show, you CAN use a pie…

…but recognizing only two demographic groups is becoming increasingly rare. So guess which chart type is back as your go-to, standby, lean-on-me rock solid friend?

It’s a bar chart.

A thick, beautiful, clutter-free, sexy bar chart. It works.

If your demographic data are resulting in very small ns, a bar chart of percentages can be misleading. In these cases, try a unit chart.

Unit charts represent every individual in your dataset. This means you have to be a bit thoughtful about whether you can actually use these. They may end up identifying some people who were expecting to remain anonymous.

Either way, just “showing the demographics” is a problem because it doesn’t carry any meaning. People are willing to give us about 3 seconds of their attention. In that time, they expect to see a point, an answer to “so what?” and charts just “showing the demographics” do not typically do that. We waste the power of a visual when we present people with meaningless visuals. If all you plan to do is “show demographics,” stop right there, save yourself your time, and just pop that data in a table in the appendix.

Alternatively, you can take the opportunity to MAKE a story out of demographic data. Give readers some insight.

Sometimes our demographic data has different meaning when it is laid out geographically, so we map.

JusticeMap.org plotted income as a map and this visual of the Twin Cities area makes it look like people with higher incomes live in the urban Minneapolis/St. Paul area.

But when you zoom in closer

…it becomes apparent that the actual urban core is much lower income than the surrounding suburbs. Maps make it possible to add depth and nuance to our demographic data stories.

Larger sets of demographic data can also be visualized with a back-to-back chart a.k.a. a population pyramid.

Traditional population pyramids compare male and female by age ranges (again, just two groups, so….) . You would think this graph by USA Facts would look more like a pyramid, in that populations generally have a lot more young children than older folks. However, in the United States, our population pyramid shows a boomer bump. And a millennial bump. Can you see them?

In a population pyramid, there should be a mirror image on either side of the spine, unless there’s something interesting happening somewhere. In this case, I spy more older Black women than older Black men.

In my work, we have used back to back charts to compare two groups even when we are not talking about gender.

You can even combine a population pyramid with a map to get a big picture of demographics over geography, like USA Facts did.

In this tile map, each square represents a state and the population pyramid shows the Black male and female populations by age in that state. While it gets tiny, we can see stories like where Black people tend to live (Texas, Florida, Georgia) and where they don’t (Montana). We can see more pronounced millennial bumps in California and New York, a more narrow base (fewer young babies) in Mississippi and more Black women of all ages in Maryland. So cool!

Demographic data doesn’t have to be boring. You can make something sexy and compelling out of the data, EVEN if you just use a bar chart.

In the other posts in our Asked and Answered series, we provide options for Rating Data, Ranking Data, and Check All That Apply. See you over there.

We go into way more detail on these topics in our books. Dr. Sheila B. Robinson is co-author of Designing Quality Survey Questions. Dr. Stephanie Evergreen wrote Effective Data Visualization.

Asked and Answered: Visualizing Ranking Data

This blog post is part of a series called Asked and Answered, about writing great survey questions and visualizing the results with high impact graphs. Dr. Sheila B. Robinson is authoring the Asked series, on writing great questions. Dr. Stephanie Evergreen is authoring the Answered series, on data visualization. View the Asked counterpart to this post on Dr. Robinson’s website.

Figuring out how you want to analyze and report rank data can be tricky. Will you tally up which choices earned respondent’s #1 rank? Top 3? Will you weight the choices in some way?

How you answer those questions will depend on how you asked the question, so be sure to see Sheila’s blog post. If the question asks respondents to rank their top three, you could simply show a bar chart of all votes.

Or you could take this a step further and ONLY show the top 3 choices.

And any time your data could be visualized in a bar chart, you can always take a jump to a dot plot or lollipop chart. You got this.

Any of these variations will be a perfectly fine visual to show rank data at a single point in time. If you have rank over time OR rank comparison across multiple groups, try a Bump Chart.

Bump charts can look a bit complicated at first glance, but some patterns should emerge, like the spike in NYC is 1999 (thank Prince, may he rest in peace) or the cities that are consistently high or low. Bump charts are cool!

In the other posts in our Asked and Answered series, we provide options for Check All That Apply, Rating Data, and Demographics. See you soon.

We go into way more detail on these topics in our books. Dr. Sheila B. Robinson is co-author of Designing Quality Survey Questions. Dr. Stephanie Evergreen wrote Effective Data Visualization.

You Probably Need Gridlines

A while back, I published a blog post on how Better Charts Tell Clearer Stories, in which I made over some breast cancer data from Komen into this graph:

and when I posted this image on Instagram, someone commented that they didn’t understand why I had used gridlines, apparently in disagreement enough to use these emojis: 😢💔😱

Here’s the thing: This chart NEEDS gridlines. I’ve said this before but I find this anti-gridline trend so common that I need to address this topic explicitly.

The *medium gray not black* gridlines are necessary because I do not have data labels on every one of the dots in the chart. Here’s what that chart would look like with data labels instead of the y-axis and its gridlines:

My friends, this is obnoxious. Overly detailed. Now, some of you are in a position where your particular audience truly wants to see every data point. Uh, ok. Give it to them by adding all these labels. This is the one time that you don’t need the gridlines or the y-axis.

In my case, for a public audience, the exact cancer rates per year are less important than the overall pattern of the data, so a y-axis and non-obtrusive gridlines are sufficient.

The commenter said they’d prefer to only show two gridlines so audiences can get a sense of the range of the data. I presume this to mean at the top and bottom of the y-axis scale. Here’s what that looks like:

What were the cancer rates in 1975? What were the cancer rates in 2016? It is *really* hard to estimate those values! At best, you can say very general things like “between 70 and 150” or “2016 was higher than 1975” but without the gridlines the audience’s ability to read the data is unnecessarily limited.

More commonly, I see folks keep the y-axis with its default increments and simply remove all gridlines, like this:

These increments along the y-axis make it possible to estimate the values on the left side of the chart. 1975 is much easier now. But the values on the right side of the chart (*the more current data*) are not to easy to estimate because our brains have a hard time tracking that far. You know what would help with that tracking? Some gridlines.

One of our tutorials in the Evergreen Data Visualization Academy addresses which lines you need, which ones you don’t, examples of both, how to add or remove them in Excel and Tableau and R, and the research behind why.

Learn in the Academy!

You can find step-by-step instructions on how to make 60+ awesome visuals in my Evergreen Data Visualization Academy.

Video tutorials, worksheets, templates, fun, and a big-hearted super-supportive community. Learn Excel, Tableau, R or all three. Come join us.

Enrollment opens to a limited number of students only twice a year. Our next enrollment window opens April 1. Get on the wait list for access a week earlier than everyone else!

Master Dataviz with Graph Guides!

Our newest program, Graph Guides, is a custom-built, year-long sprint through 50 Academy tutorials.

When you enroll, we’ll assess your current data viz skill set, build you a customized learning path, and hold your hand as you blaze your way to new talents.

We open enrollment to 12 students at a time and only twice a year. Get on the waitlist for early access to our next enrollment window.

Horizontal Dumbbell Dot Plots in Excel – Way Easier Version

Ok, babes, prepare to be amazed. It used to be that making this viz was pretty tedious but I’ve recently refined a totally new hack (thanks to a lollipop chart example provided by Sevinc Rende, one of my mentees) that makes this soooooooo easier. It used to be Rockstar Level 9. Now it is Rockstar Level 5, if that.

We will create a dumbbell dot plot out of a stacked bar, where the first stack is composed of our first set of dot values and the second stack is composed of *the difference* between our first and second values (so that it would end at our second values on the x-axis scale). So let’s calculate the difference between the 2020 and 2010 scores.

We would make a stacked bar of the 2010 data and the difference data, but you’ll see when you copy that formula down the rest of the spreadsheet, we run into a problem at row 7, where the 2020 score is lower than 2010. I purposefully added this twist just to show you what to do in these situations because otherwise this new method is so easy.

We’ll have to treat the data for row 7 as their own series. Highlight the 40%, 26%, and -14% and drag them to the right so they are in columns E to G. Label these columns and switch the difference calculation so that it is the 2010 data minus the 2020 data, to get a positive number.

Now, highlight the entire table, starting in A1, and insert a stacked bar chart.

Look up in your toolbar menu and click Switch Row/Column (Switch Plot if you are on a Mac) to get the racial groups going down the y-axis.

We won’t actually need the 2020 or the Rev 2010 data in this chart (we just needed those data points to calculate the difference) so click on those segments in your graph and hit the Delete key on your keyboard. You should end up here:

The segments most to the left (2010 and Rev 2020) will become No Fill. Right-click on one of the segments, select Format Data Series, head to the paint bucket icon and, under Fill, select No Fill.

With those segments still highlighted, look for your green Chart Design tab. On the left side of that toolbar, you’ll see Add Chart Element. Open that, hover on Error Bars, and select Percentage from that submenu.

You’ll have to add those error bars for each series, so once for the 2010 data and once for the Rev 2020 data point for Hispanic/Latino.

Then click on the gray Difference segments and add those error bars there, too. And then once more for the green Rev Diff segment at Hispanic/Latino. While there, change that bar segment from green to gray (same way you changed the others to No Fill – just pick gray!). Can you see the shape of your dumbbell dot taking place?

Let’s adjust those error bars so they are circles. Right click on one of the error bars to select all in that series. Select Format Error Bars. In the menu that opens, select No Cap and reduce the percentage to .5.

Then click in the paint bucket icon. In Begin Arrow Type, pick the circle shape. Pick a color. You can change the size of the circle by picking from Begin Arrow Size or by changing the line width. Repeat this process for all error bars in your chart, being mindful of the ones that switch places (Hispanic/Latino).

You could pretty much be done! For further formatting, you could decide to make the dumbbell stick a little thinner. Right-click on any one of them and select Format Data Series. Slide the Gap Width slider to the right to make those bars thinner.

Change the font, adjust the x-axis if you want, etc. I reversed by y-axis so that American Indian/Alaskan Native was at the top (right-click on that axis, select Format Axis, check Categories in reverse order. If that moves your x-axis to the top, look for the Horizontal Axis Crosses menu and select At Maximum Category.).

No matter what extra formatting you do, the legend is probably useless. Click on it and hit the Delete key. Add labels directly to the first set of dots using your favorite method – text boxes, data labels you hijack from elsewhere, whatever works.

The one drawback to this method is that it isn’t easy to put data labels directly in the circles – much easier to do that in the old version where we hacked a scatterplot. So keep your x-axis and gridlines here.

Are you totally impressed by how much easier this method is? I know, me too! Like Sevinc first showed me, you can modify this process for horizontal lollipops and dot plots, too. If you liked horizontal dumbbells before but avoided them because they were a pain in the neck to make, Welcome to Easy Town.

This method is so fresh it isn’t in any of my books. It is in my Data Visualization Academy and Graph Guide programs.

Learn in the Academy!

You can find step-by-step instructions on how to make 60+ awesome visuals in my Evergreen Data Visualization Academy.

Video tutorials, worksheets, templates, fun, and a big-hearted super-supportive community. Learn Excel, Tableau, R or all three. Come join us.

Enrollment opens to a limited number of students only twice a year. Our next enrollment window opens April 1. Get on the wait list for access a week earlier than everyone else!

Master Dataviz with Graph Guides!

Our newest program, Graph Guides, is a custom-built, year-long sprint through 50 Academy tutorials.

When you enroll, we’ll assess your current data viz skill set, build you a customized learning path, and hold your hand as you blaze your way to new talents.

We open enrollment to 12 students at a time and only twice a year. Get on the waitlist for early access to our next enrollment window.

Asked and Answered: Visualizing Check All That Apply

This blog post is part of a series called Asked and Answered, about writing great survey questions and visualizing the results with high impact graphs. Dr. Sheila B. Robinson is authoring the Asked series, on writing great questions. Dr. Stephanie Evergreen is authoring the Answered series, on data visualization. View the Asked counterpart to this post on Dr. Robinson’s website.

In a Check All That Apply situation, respondents can – and usually do – choose more than 1 answer. This means the responses will never total to 100%. And that’s why this data can not be visualized as a pie chart.

Which means a bar chart, ordered greatest to least, is your alternative. But that can have many variations.

In this example, created by Dr. Sheila B. Robinson, she used 100% stacked bars for each survey item, to indicate that each item could have totaled 100% if all respondents checked that box. This is a nice way to show that, while the response options as a whole can’t add to 100%, each option on its own CAN. Plus, look at the cute icons.

If bars or stacked bars are too boring (I don’t think so but some people have OPINIONS) you can keep moving in the direction of cute icons and turn your data into a Lollipop chart.

If you wanted to compare Check All That Apply responses between two groups, you can pair the data in a Back to Back chart.

Or choose a Dumbbell Dot plot (with MORE icons), which emphasizes the gap between respondent groups.

In the other posts in our Asked and Answered series, we provide options for Rating Data, Ranking Data, and Demographics. See you soon.

We go into way more detail on these topics in our books. Dr. Sheila B. Robinson is co-author of Designing Quality Survey Questions. Dr. Stephanie Evergreen wrote Effective Data Visualization.

Asked and Answered: Visualizing Rating Data

This blog post is part of a series called Asked and Answered, about writing great survey questions and visualizing the results with high impact graphs. Dr. Sheila B. Robinson is authoring the Asked series, on writing great questions. Dr. Stephanie Evergreen is authoring the Answered series, on data visualization. View the Asked counterpart to this post on Dr. Robinson’s website.

When we ask people to rate something, it is usually on a numbered scale, though sometimes that scale will have word associations, like “Strongly Agree.”

A common, simple way to visualize this sort of data is with a column chart.

However, a regular column chart does not quickly communicate that we are talking about 100% of our survey respondents. So people like to stack the data together – making stacked bars.

Stacked Bars *seem* like a good idea – we show 100%, we can fit more questions and data into a similar amount of space – advantages, right? Except that stacked bars are difficult for people to read. How well can you compare the values of the orange segments? Not so much.

If you are going to use stacked bars, make some helpful formatting tweaks, like smarter color coding and an order from greatest to least.

Better, right? But what will these same tweaks work when we have many more response options, like when our scales run 0-10?

Working with shades of two colors is possible when we only have 4 response options but we’ll end up with too many shades and colors here. And while order may help us interpret the 0 data, it won’t boost the readability of this chart very much. (And please don’t @ me re: net promoter. If you are getting stuck on the metric, yer missin the point, friend. See Sheila B Robinson’s blog for a perspective on the use of net promoter survey questions.)

Really, it’s hard for anyone to distinguish between a 4 and a 5 on a 0-10 scale, so let’s assign the same color to multiple response options, like this:

And if we’ve gone this far, we might as well aggregate all categories within one color so that there are fewer segment breaks and legend entries.

Fewer segments in the stacked bar makes order more meaningful, too. Now we can see some stories emerging in this data. If we wanted to highlight certain aspects of this story, we could consider only adding an action color to a single segment.

A single color on a different sort order tells yet another story.

In my past couple of examples, I used red to indicate the not-so-great stuff, blue to indicate the great stuff, and gray to mark the neutral or the passive. But when interpreting this data, you might tell a different story – one that identifies the passive crowd as a place to focus some outreach efforts. In which case, passive isn’t neutral.

If so, you wouldn’t want to hide it in gray. And you’d probably want to look at every group in its own sunshine, comparing values for each. When that’s the case, use a small multiple bar chart with action colors on each segment.

We see yet more stories when we recast the data this way.

Of course, you could selectively group two or more response options and position them against the others, in a diverging stacked bar.

This time we sorted by the sum of passive and detractor, where one more story pops out.

Of course, rating scales can always be boiled down to a single number or otherwise essentialized. If you want to keep all of the data in your visual, these are some strong options.

Wondering what to do with neutral? That’s coming in its very own blog post, but you can pick up some hints here, for sure.

In the other posts in our Asked and Answered series, we’ll provide options for Check All That Apply, Ranking Data, and Demographics. See you soon.

We go into way more detail on these topics in our books. Dr. Sheila B. Robinson is co-author of Designing Quality Survey Questions. Dr. Stephanie Evergreen wrote Effective Data Visualization.

Announcing The Graph Guide Program

Have you ever signed up for an online course with really good intentions, only to find yourself barely checking in a couple months later? In the Graph Guide program, we will not let you fade away.

When you commit to the Graph Guide program, you earn a certification in data visualization, period. We’ll hold your hand (and hound you, if we have to) every step of the way. We are your partner in your growth and development as a data visualization guru.

In the Graph Guide program, you’ll get targeted, boutique coaching on your data visualization projects, with certification waiting for you at the end. Your year in the Graph Guide program will help you produce amazing visualizations, build your skill set and competitive edge, and get you recognized for your talents. 

How It Works

We’ll start with an initial assessment of your data visualization skills and help you plot out a personalized learning pathway through our Academy tutorials, all leading up to a portfolio review and certification at the end of the year.

You’ll be held accountable. Your Graph Guide will check in with you at least every other week to help keep you on track through your learning and to support your ongoing data visualization projects. There are no chat bots here. All of our Graph Guides are actual human experts in data visualization.

Tip for Success: Use this opportunity to rock out some upcoming work projects.

Aside from this incredibly hands-on coaching experience, your year with a Graph Guide will also include everything that comes with a traditional Evergreen Data Visualization Academy membership: monthly live Office Hours, active conversations in our private Facebook and Slack groups, fresh tutorials semi-monthly, private events, and members-only discounts on dataviz swag.

How You’ll Be Assessed

To gain certification and graduate from the Graph Guide program, you’ll have to complete and demonstrate successful visualizations from 50 Academy tutorials, using any software – Excel, Tableau, R or a combination of those. You’ll have a core curriculum you must complete. Your Graph Guide will work with you to choose from many elective tutorials until you reach 50. At least one must demonstrate automation, one must demonstrate interactivity, and one must demonstrate overall layout of multiple visuals. You’ll earn digital badges for each tutorial you complete and a paper certification at the end.

Tip for Success: Try to use data from your work or student life as you complete the tutorials, which gives you real work applicability and let’s you maximize your time.

No kidding – it will be an intense year. But it will be a do-able year because you’ll be part of a supportive and encouraging environment. We specifically have restricted enrollment and a low student-guide ratio in order to focus on your growth and walk with you as you achieve certification. 

How Much It Will Cost

Our years running the Evergreen Data Visualization Academy have taught us that some folks thrive best with a Graph Guide. But we are shaping this program as we run it, so for this first year, we have pilot year pricing. You can earn certification with a Graph Guide for $2,499. In exchange for this super low price, we’ll ask you for input about the program at various points, deal?

Tip for Success: Get your boss to sponsor your year with a Graph Guide. This is the least expensive, highest quality data visualization consulting you can find.

You should come learn with us because Evergreen Data is the only research-based virtual data visualization course. Hundreds of folks have already worked their way through our Academy, earning office accolades and promotions and guru status. They succeed because we know how to teach and we ESPECIALLY know how to teach data visualization. For the first time ever, you can now get hands-on, customized, personalized, structured, incredible data viz education. We are ready for you. You coming? 

When You Can Enroll

We’ll open enrollment to 12 people on April 1, 2020 for a program that runs May 1, 2020 – April 30, 2021. Actually, people on our Academy wait list will get access to those 12 seats one week earlier – starting March 23. At that time, we’ll send you more details on the program, too.

How Our Academy Programs Compare


Chart Starter Series

Data Visualization Academy

Graph Guide
Video tutorials 10 tutorials in Excel OR Tableau OR R 50+ tutorials in Excel, Tableau, and R 50+ tutorials in Excel, Tableau, and R
Worksheets and templates In each tutorial In each tutorial In each tutorial
Research behind the chart choice In each tutorial In each tutorial In each tutorial
Written step-by-step instructions In each tutorial In each tutorial In each tutorial
Community forum In each tutorial In each tutorial, in a private Facebook group, and in a private Slack channel In each tutorial, in Academy Facebook and Slack groups, plus ongoing conversation with your Graph Guide
Payment One-time Renews annually Renews annually, though you should graduate within a year
Office Hours Once a month Once a month
Help on personal work projects Any time Intensive coaching with your Graph Guide
New content Semi-monthly Semi-monthly
Certification Digital Certificate of Participation Digital Certificate of Participation Paper and digital Evergreen Data Academy Certificate of Completion
Assessment Pretest, checklist of milestones to accomplish, portfolio review, posttest

2020 Evergreen Mentoring Program

This past year, I wrote an article that required all the vulnerability and bravery I had that whole month. It was about being a woman in data visualization and some (just some) of the shit that has come with it. It was exhausting to recall the experiences I discussed in the article (while also bracing for the reactions I knew were forthcoming) and in the process of writing it I found myself mourning the fact that I didn’t have female mentors in my life at the time to help me through those situations.

I’m here to rectify that – to be a mentor for other women and to establish future mentors, in turn.

2020 will usher in the third cohort of mentees. This past year, the second cohort has been digging deep, even when life is busy, to construct a business that has some legs, that is off and running. One said, “Basically, I went from burning out to building a business up.” And you know that’s right.

Past mentees have said this about their experience:

Stephanie never ceased to amaze me with her leadership, dedication, accessibility and overall kickass ability to help each of us recognize our strengths, build our focus, and develop skills for entrepreneurship. I am so grateful to have had the opportunity to participate in this mentoring group over the last year and know the other talented women professionals in our group will continue to support and strengthen each other as we grow our businesses. 

Robin

Stephanie’s guidance and the support of the women in this cohort were invaluable in helping me with the operational aspects of starting and maintaining a business (e.g., pricing, branding, clients, marketing); however, it was the mentorship around what it means to be a female business owner, dealing with feelings of imposter syndrome and anxiety around change that really made this an unparalleled experience for me and built my confidence as a business owner. Michelle Obama said “People who are truly strong lift others up. People who are truly powerful bring others together.” Stephanie has created something truly powerful in this mentorship experience.

Amy

What it will involve

A 1-year commitment, starting March 1, 2020.

Regular communication (meaning daily, weekly) on Slack (I’ll show you how to set up) around a new topic each month. The exact agenda will be set based upon the needs and interests of the women selected for the program. Right now, the agenda includes: figuring out your focus, knowing what to charge, branding, marketing, all the dirty behind the scenes details of running a business, centering your ethics, choosing clients, project management, hiring a team, and what to wear.

Really brutally honest conversation. I’m going to challenge you a lot. You’ll need to be comfortable sharing private details like your hourly rate, for example. Likewise, strict confidentiality is absolutely non-negotiable.

Quarterly virtual group conversations on Skype. I don’t have time to waste and neither do you so it won’t be a bunch of chit-chat on Skype, it’ll be critical check-ins where we discuss recent monthly topics, how you are progressing in these areas, and how business-building is going.

A $20 per month financial commitment. This isn’t so you pay my bills. This is so you have a little skin in the game and are more likely to make the commitment to participate regularly.

Scripts, email templates, and other forms of support to set you up for success (based off of the very same things my mentors gave to me).

Who should apply

You identify as female and are in the early stages of starting a business. You should have more than a dream of starting a business. It can’t just be your hobby project. You should be on the ground, running it, or ready to do within the very near future. It doesn’t have to be your full time job right now, but you should be planning that move within the year.

You should be interested in learning how to run a successful business. I WILL NOT teach you how to do data visualization. That’s not what this is about at all. It doesn’t matter what industry you are in. You do not necessarily have to be a running the business by yourself. This does not have to be your first career. I don’t care how old you are.

You can commit to regularly asking questions, doing a bit of homework, and responding to others. Perhaps up to 30 minutes a week. Each week. Even when you think you are on vacation.

How to apply

Send me an email, in which you tell me:

A little about you, your background, your identity

The stage of your business (there are not hard definitions around this, so just describe where you’re at)

Why you want to be a part of this

That you can commit to the time and financial expense I’m laying out here

Email it all to me by February 14.

Then what

I’ll select 4 women by February 28. Everyone will get a reply from me no matter what.

The 5 of us dive in on March 1.

With gratitude for the mentors who have come before me and with hope that we can build a better world,

Stephanie

What? So What? Now What?

People who are short on time (i.e., most everyone) have these three questions on their mind, in this order.

What’s going on?

What does this mean to me?

What are we (actually, YOU) going to do about it?

The shorthand way of structuring that storyline: What, So What, Now What. When you construct your slides/report/handout/poster in this method, you bring people right along with you because you have aligned the data with their most pressing questions.

In the Data Visualization Sketchbook, I have provided a handout that fits this What, So What, Now What framework. Here’s my completed example:

What?

Folks are typically crossing their fingers that you’ll cut straight to the chase and tell them the bottom line right up from. The answer to What’s going on? Doesn’t usually involve a discussion of your analysis methods or your data collection strategies. If you do need to discuss those things in order to answer What’s going on, it’ll be brief. What? is answered in statements like:

We are ahead of targets in all areas.

We are behind targets in some areas.

We have some data, but we don’t even know where the targets are.

In my example, What? is Satisfaction with the company is declining among middle income people of color located in the Southeast.

With this kind of structure, your audience is going to be like “Cool, thank you for getting straight to the point, but… so what?”

So What?

In this section, you outline the relevance of this data to your audience. Good lord, I hope some of them see this insight before you have to say it out loud. Even still, you need to say it out loud. And you need to craft it in such a way that your audience can immediately see what’s in it for me. What’s in it for me is SO STRONG that if you don’t speak directly to how this issue will impact the person in the audience, if you don’t make that connection crystal clear, you’ll lose some folks. So What? is answered in statements like:

You’ll lose customers.

You’ll get more funding.

You’ll build a better company and achieve your organizational mission.

In my example, So What? is The decreased in satisfaction is likely to impact other demographic groups as well and will result in a loss of revenue.

You KNOW your audience will perk up when they hear that.

Now What?

Ok, you’ve spelled out the issue and how it directly relates to the audience. They are hooked. Their pressing question is Now what do I do with this new knowledge that I have? This is where you launch into your call to action. You deliver the payload. You land the plane. Pick a metaphor. You give people a course of action to follow or something they should stay tuned for, if you are the one executing the course of action to follow. Now What? is answered in statements like:

Based off of this information, change your strategy.

Call your representative and ask for a policy change.

Stay the course, you’ve got this.

In my example, Now What? talked about next steps in the process, providing an action plan to tackle the issue.

The What/So What/Now What framework encapsulates the information your audience wants in an order they want it. Notice the lack of discussion of statistical significance. Notice the word choices – ones that reflect a common plain language, immediately understood. This framework sets your presentation up for success. When you snag The Data Visualization Sketchbook, you’ll get this blank template to work from and many others, for additional handouts, or slides, reports, dashboards, and even graphs.

In our Academy and Graph Guides programs, you can pick my brain and get input from our big-hearted community on your handout-in-progress.


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