If you work in global health as I do, that statement needs no explanation.

For anyone who is not sure what I mean by that: when I took the class, a key challenge was trying to synthesize and summarize abundant global health data into actionable information products. I fretted about how to convince colleagues to use our project’s branding and data visualization guidelines, how to get them to attach the label to the line on a line graph rather than in a legend somewhere else on the graph. I took for granted the global health resources like the Demographic Health Surveys.

A few months after taking the course, USAID was dismantled, and funding was slashed for many other federal government agencies. Dozens and dozens of my friends and former colleagues lost their jobs.

Meanwhile, much of the hyperbolic hype around AI makes it seem like all knowledge workers are going to be replaced soon.

In this climate, it might seem superfluous to talk about tips and tricks for making reports more appealing.

I’ll offer two examples of useful things I learned during the course, one simple and one more theoretical.

Fewer Colors

The first is simple but powerful.

I admired the clean design that I saw in Ann’s communications.

My designs, in comparison, seemed too cluttered.

During the class I realized that it was because Ann uses a limited color palette and largely uses colors gradations in graphs rather than using many different colors.

Templates to Create Structure

I decided to take Report Redesign because I was being asked to help write and design policy briefs, but I had never learned how to do that.

I thought the course would empower me to make suggestions– even though it wasn’t exactly the focus of the course – and it did!

The course materials included templates, and the tutorials explained how to create templates. It is a huge time-saver to start with template rather than developing each product from the blank page. 

Using those as inspiration, I developed simple PowerPoint templates and an accompanying worksheet to help teams brainstorm their key messages and decide how to present them visually.

My team has shared these resources during several webinars, and colleagues have said that they appreciated the straightforward guidance. 

I am glad I took the Report Redesign class, and I would encourage others to take it as well.

There will always be a need to communicate data clearly and accurately.

The class is a great balance of theory and practice.

The time allotted is enough to learn hands-on skills but still a reasonable time commitment for people with full-time jobs.

Connect with Emma Williams

On LinkedIn: https://www.linkedin.com/in/emma-williams-7144aa7/

The post Fewer Colors & Report Templates by Emma Williams appeared first on Depict Data Studio.

For me, it was discovering pivot tables and their ability to run circles around painstaking formula execution.

What are Pivot Tables?

Simply put, pivot tables sort a contiguous array of quantitative data by multiple planes of information simultaneously.

This is an example of a contiguous dataset (with pretend data). All the cells are touching/sharing a border.

After you fiddle with the rows (x axis)/ columns (y axis)/ values (summary), filters (sorting)… Tah da! The magic happens. 

Here’s how quickly you can summarize data with pivot tables:

Improving Your Interactive Dashboards

To build professional and reliable interactive dashboards, pivot tables are your best friend.

As a data designer who’s recently completed Ann K. Emery’s Dashboard Design course, I’m excited to share three techniques that have elevated my dashboard game. 

(1) Helper Tables

First, I’ve learned the value of creating “helper tables.”

Think of these as your dashboard’s backbone.

While pivot tables are constantly shifting as users interact with filters and slicers, helper tables provide a stable foundation for your key metrics.

I create these on a dedicated ‘Ref’ sheet, where they quietly but efficiently pull data from the pivot tables using direct cell references (=).

(2) Consistent Cell References

Second, cell references need to be consistent.

By using absolute references (=$row$column), I ensure my helper tables always pull exactly what I need, regardless of how the underlying pivot table shifts and sorts.

(3) Naming Pivot Tables

Third, name your pivot tables intuitively!

Sure, Excel is happy to call them “PivotTable1,” “PivotTable2,” and so on, but meaningful names make maintenance so much easier. (Just remember: no spaces allowed!)

Organizing Your Dashboard Spreadsheets

My finished dashboards now follow a clean, organized structure with five key sheets:

1. Overview (where users find instructions)
2. Data (the raw information, typically hidden)
3. Pivot (where the magic happens, also hidden)
4. Dashboard (the beautiful final product)
5. Ref (my helper tables’ home)

Dashboards Need Functionality and Performance

One of the most valuable lessons from Ann’s course was learning to balance functionality with performance.

Multiple pivot tables can strain Excel’s resources, but with these techniques, I can create sophisticated dashboards that remain lightning-fast and reliable.

What started as a simple appreciation for pivot table magic has grown into a comprehensive approach to creating dynamic, professional dashboards that my clients love.

Your Turn

What’s your favorite pivot table trick?

I’d love to hear how you’re using these powerful tools in your own work!

Connect with Anna Pfaff

Reach out to guest author Anna Pfaff on LinkedIn.

The post 3 Ways to Elevate Your Pivot Tables and Dashboards in Excel appeared first on Depict Data Studio.

Creating a Customer Service Visual Framework

I present a lot and have given customer service presentations in different forms and fashions for over 15 years. I usually get compliments on my presentations, but I wanted to make sure the information I was presenting was sticking with people. I needed to refresh a customer service presentation and decided to use it as an opportunity to apply the lessons from Powerful Presentations I was learning.

I had my framework in my head, but I was skeptical that with my skill level I could create something useful and decent to look at.  I am a librarian, not a graphic designer.

Begin at the Very Beginning…..It’s a Very Good Place to Start

Despite my doubts, I began.  Focusing on my outline, I had 5 areas I wanted to touch on.  I matched icons to each and then thought about what I could construct to bring them all together.  I realized that the Center Humanity portion was a target with four sections.  So that is where I started. 

Be Literal

I decided to be very literal and created a circle and wrote center humanity and put it in the center of the target icon.

This was the central point I wanted people listening me to take away.  That each person they interact with is another human being and we should remember that first and foremost in every customer service interaction.

Combine the Elements

I then put one section near each of the quadrants on the target and added the wording below.  I gave each its own color so that in my slide deck each would have a separate color to help tie people’s brains to that section. 

At this point, I had done nothing too hard, downloaded a few icons, recolored them, and added wording.  I could have stopped here and I think it would have been ok.

Pause for Reflection

But I chose to get some feedback to see if I could make it better.

At this point I paused and brought it to Office Hours for suggestions. 

Shout out to all the fabulous people who shared ideas with me and helped make this visual framework more meaningful for my presentation.

Tweak for More Impact

I received lots of great ideas and my head was spinning with all the different ways I could possibly update my visual to resonate more with my audience.  I decided to start by making the target look more like a target while keeping the center humanity in the center. 

Add More Visual Cues

I wanted to make the four other sections clearer and tie things together. I took a duplicate of the target icon and recolored it and then used the crop tool to shrink it to only one quadrant.

This is what it looked like when I was finished. 

Rinse & Repeat

I then did the same for Green, Red, and Purple.

Four Quadrants

This is what it looked like when I was finished with all the quadrants. 

I was liking where it was going and thought this would stick in people’s heads better than the original. 

Keep Centering Humanity

I was hooked on keeping the circle and plopped it on top of all the different graphics I had just created. 

This would be an easy graphic to chunk and use in my slide decks and I felt like I was on the right track.

Adding Icons

I changed some of the icons after thinking through what I was trying to convey and added them by each section. 

I was getting closer, but it still didn’t feel finished to me.

Librarians Love Words

I thought I could get away with leaving the words off and just having the icons, but it looked too bare to me.  I used Word Art and after a bunch of trial and error got the words to curve at the right angle. 

It now felt complete. 

I used the Group tool to group all of the separate graphics together and saved it as an Image.

Success!

And while I thought it was pretty great, I didn’t know if it would be helpful for attendees.  But it was! 

In the feedback, one attendee wrote that they printed off the framework and put it on their desk as a reminder to them to follow the 5 steps. 

That made my day and solidified for me why having a visual framework is so helpful.  We don’t want to just give people presentations, we want what we share to stay with people and be useful. 

The post Unlocking Creativity: Simple Steps for Non-Designers to Build Powerful Visual Frameworks appeared first on Depict Data Studio.

I got the opportunity to work with a public health agency which I was really exited about.

Until I had to present my research to a group of policy makers…

Before: The Dusty Shelf Report

Condensing two full chapters—73 pages of my thesis—into a short report for the policy making group seemed like an impossible task.

That’s when Report Redesign came to the rescue!

As this research was being conducted in an academic setting, I couldn’t entirely do away with all the technical details (or what Ann would call the Dusty Shelf Report ).

But I did manage to apply the 30-3-1 principles to summarise the two chapters into:

1. a shorter 23-page report (with appendices) and
2. 11 slides for a 10-minute presentation to the policy making group.

Choosing the Final Outputs: A Short Report and a Slideshow

Working with the public health agency, I realised that although they were expecting the technical details on the study methods and results to be included, the overall format expected was different compared to what I had been used to in academia.

They wanted slides which were to be presented to the policy making group and an accompanying report with more details on the study in case some of the members wanted more detailed information.

I thought this would be a good opportunity to apply some of what I had learnt during the Report Redesign course.

Choosing Which Findings to Include

We had a few meetings with the research group to identify the most important findings to include in the presentation and report.

Given the audience was technical, we agreed to include:

• An overview of the study
• A sentence on what the goals/aims of the study were
• Survey respondent characteristics
• Results section that highlighted responses to main questions in the survey
• Limitations

Just focusing on these areas, I was able to whittle down the two thesis chapters into 23 pages with some additional information in the appendices.

The Shorter Report

In the original version of the write-up, I did have some tables, but they were too technical (too many decimal places; statistical terms like p-values).

I also had some graphs that used the default settings made within my software program without any editing.

For the report, I aimed to have one or more visuals on every single page (a goal covered in Report Redesign).

This included flow charts, graphs, tables, text boxes, and icon arrays. Whatever was needed to best communicate the takeaway finding from the research.

The agency was going to use their own design team for the final branding and layout, so I didn’t have to bother with that.  

The Presentation Slides

I further had to whittle down the report into 11 slides for the presentation.

I decided to limit the background information and focus on the key results.

The post Redesigning a Thesis Chapter appeared first on Depict Data Studio.

My first chance came in the form of a front-end evaluation project for a children’s museum planning a new exhibition on dinosaurs.

Measuring What Kids Already Know about Dinosaurs

The museum wanted to understand what children and families already knew about dinosaurs – including whether they knew what other types of animals and plants existed at the same time.

I designed a fun card-sort activity, where parent-child pairs were asked to work together to sort 19 cards with images of different plants and animals into two piles:

• one pile for those they thought lived at the same time as dinosaurs, and
• one pile for those they thought didn’t live with dinosaurs.

Here’s a sample of a few of the cards we gave to families:

Draft 1

For my first stab at a graph showing the results, I applied several of the best practices I learned about in Great Graphs:

• I sorted my data from largest to smallest.
• I applied color meaningfully – using the client’s brand orange to show the animals that did exist at the time of dinosaurs and gray to show those that didn’t.
• I eliminated the unnecessary visual clutter from the Excel default graph and made some simple modifications (for example, increasing the width of the bars and the text font size).
• I even added annotations highlighting interesting findings.

Here’s what my first version looked like:

Draft 2

I thought I was off to a pretty good start, but I wasn’t sure if my graph was clearly explaining that some of the answers were correct and some were incorrect, so I decided to bring my graph to Office Hours with Ann to see what else I could do.

Ann offered me three simple ideas that took this graph from good to great.

1. Group the bars to better show which responses were correct or incorrect.

Rather than order all the bars from largest to smallest, Ann suggested that I group all the correct answers together (ordered from largest to smallest) and similarly group all the incorrect answers together.

2. Add space between the groups to create a visual distinction.

Although the same effect could be achieved by creating two separate graphs, Ann showed me how to add a gap between two sets of bars in a single graph by simply inserting one (or more) blank rows in the source table. (Note from Ann: Learn more about adding blank rows in this tutorial, and view another example of intentional gaps here.)

To make the difference between the two groups even more obvious, we also added subtitles to indicate correct and incorrect responses.

3. Add icons for visual interest and whimsy.

This graph is for a children’s museum project about dinosaurs. This is the type of graph that is just calling for a touch a playfulness.

We found an adorable dinosaur icon in the free icons that are included with all Microsoft Office products.

We added an orange dinosaur icon to highlight the correct answers and a grey one with a slash through it to highlight the incorrect answers.

Here’s the final version of the graph that I included in my report:

A big improvement made in three simple steps and less than 30 minutes.

There’s a reason the course is called Great Graphs.

Connect with Maia Werner-Avidon

On LinkedIn: https://www.linkedin.com/in/maia-werner-avidon/

Learn more about Maia’s work at www.mwainsights.com.

The post 3 Simple Steps that Took My Graph from Good to Great appeared first on Depict Data Studio.

However, the spacecraft never finished its mission. In fact, upon reaching Mars the next year, the $125 million spacecraft promptly crash landed into Mars, disintegrating in the atmosphere.

What could have caused such a crash landing?

Was it a freak meteor strike?

Faulty equipment?

ALIENS, perhaps?!?

The answer, surprisingly, is that the crash was caused by a classic case of BAD DATA.

That’s right–this spacecraft, this wonder of science, was rendered useless by bad data being entered into its flawless system. The Mars Climate Orbiter was designed to work on metric units, but unfortunately commands for the spacecraft were being sent from Earth in English units.

The result was a $125 million conversion error.

Collecting Survey Data at HOPE International

So what exactly does this have to do with spreadsheets? I’m glad you asked. I work with the nonprofit HOPE International as a Listening, Monitoring, and Evaluation Analyst. The mission of HOPE is to invest in the dreams of families in the world’s underserved communities as we proclaim and live the Gospel.

My team contributes to that by facilitating listening to those we serve, primarily through administering surveys and analyzing the data. Our surveys focus on many things–impact, experience, satisfaction, etc.–but regardless of the focus area, I always can’t WAIT to dive into the results.

When you spend so long crafting a questionnaire, translating it just right, and training enumerators to administer the survey, it’s nearly impossible to resist jumping into analysis once the results are in.

However, I’ve found that this is precisely what I must do–resist the urge to jump straight into analysis.

This is because, just as in the case of the Mars Climate Orbiter, a perfectly designed analysis system with flawless pivot tables will amount to nothing (or worse, a $125 million error) without proper data flowing into the system.

That’s right–I’m talking about DATA CLEANING.

Why Data Cleaning?

Data cleaning is an essential part of our survey process.

There have been many real-world situations where the results would have been biased or even completely incorrect had we not first taken the time to clean the data.

Here are a few situations we’ve encountered in the past:

1. Duplicate survey responses caused by system error, or a respondent accidentally taking a survey twice.
2. Pretest/training responses being included with the actual data from survey administration.
3. Surveys being completed in an extremely short amount of time, where most if not all of the answer choices were blank.
4. Data entry errors, such as accidentally copying a response in Excel across multiple rows and erasing original responses.

As you can see, the issues above would cause drastic differences if not corrected through a data cleaning process.

As tempting as it is to jump straight into crafting pivot tables and analyzing the results of the survey, engaging in a thorough cleaning and recoding of the data is vital to ensuring accurate results.

6 Data Cleaning Steps to Save You Millions

I’d like to show you what we do for our data cleaning process, and how Simple Spreadsheets helped to make this process even stronger.

In this article, you’ll learn:

1. How to check for duplicates (for example, if someone accidentally took the same survey twice);
2. How to check the survey for changes (for example, if translation typos were found after going live);
3. How to check for outliers in survey duration (how long it takes someone to complete a survey);
4. How to Use COUNTA and COUNTBLANK;
5. How to Recode Variables with IF Statements; and
6. How to Combine Datasets Together with VLOOKUP.

Yes, all of these data cleaning steps can be completed in Microsoft Excel.

(1) How to Look for Duplicates

One of the most important steps in our data cleaning process is to look for “duplicates.”

Duplicates are two (or more) entries that are either exactly the same, or match on a critical piece of information (like ID number or name).

It’s crucial that we identify these duplicates and resolve them before starting analysis. Otherwise, our results will not be accurate, and will instead overrepresent the duplicated entries.

Which Variable(s) Should Be Unique?

To check for duplicates, first identify the key variables in your data set that should be unique for each respondent.

For instance, our clients have an identification number which is unique to them. This field should not be duplicated in a data set.

Highlight the Duplicates in a Different Color

Once you determine your key variables, there is a simple Excel process that you can follow in order to identify and sort through your duplicates:

• Step 1- Highlight the column of interest.
• Step 2- In the Excel ribbon, select “Home” > ”Conditional Formatting” > ”Highlight Cells Rules” >”Duplicate Values.”
• Step 3- In the pop-up window, choose a highlight color of your choice and press “OK.” This will highlight all of the cells in the selected column that contain duplicate values.

Once these steps have been followed, any duplicates for the criteria you selected will be highlighted.

Manually Examine Each of the Duplicate Entries

I like to then filter the column where it only contains the duplicate values, sort in ascending order, and then manually go down the list to analyze each duplicate pair (or trio, etc).

Doing this manually really helps you to get a feel for the data, and understand whether the duplicates are truly duplicates, or whether there is some other systematic issue at play.

If the duplicates match exactly in all fields in the survey, then they are “true duplicates.” We usually keep the response that was entered first and remove the other response.

If they don’t match exactly in all of the fields, then we connect with our team that administered the survey and try to determine together how to handle the entries, whether removing them entirely, keeping some, or keeping all.

(2) How to Check the Survey for Version Changes

Another important step in the process is to check survey versions for any notable changes.

When we are administering a survey, we do everything we can to test the survey beforehand, in order to not make any changes during the administration.

However, unforeseen changes to translation, wording, or even whole questions sometimes need to be made during the administration process, and it’s important to check if any of these changes could impact how data is interpreted.

For instance, if the first 10 respondents to a survey saw this question:

“How satisfied are you with the training curriculum?”

• Very satisfied
• Satisfied
• Neither satisfied nor unsatisfied
• Very unsatisfied
• Very unsatisfied

And the rest of the respondents saw this question:

“How satisfied are you with the training curriculum?”

• Very satisfied
• Satisfied
• Neither satisfied nor unsatisfied
• Unsatisfied
• Very unsatisfied

Then the fourth answer would mean two different things, depending on when the survey was taken.

In a large survey that is being translated into multiple languages, it is quite possible that small details like this go unnoticed, even through quality checks and testing.

Compare Spreadsheets with the “Compare Files” Add-In for Excel

In order to avoid having to meticulously analyze each version of the survey row by row in Excel, we utilize the “Compare Files” function.

This is located in the “Inquire” tab as an add-in for Excel, but I highly recommend you download it.

It saves a considerable amount of time comparing two spreadsheets.

To use this function:

• Simply open the spreadsheets you want to compare at the same time.
• Click “Compare Files.”
• Choose the files you would like to compare.
• Press the “Compare” button.

Excel will then open a third document which lists all the differences (and their categories).

Our team then goes through this document to see if any critical changes were made to the survey during administration, and we account for these changes accordingly in the analysis.

(3) How to Check for Outliers in Survey Duration

Lastly, a simple but important step in our data cleaning process is to check the duration of a survey.

Usually, we determine the average time it took to complete the survey, and then manually investigate any responses that were much faster or much slower than that average length.

These could just be outliers, or they could be surveys that weren’t finished, system errors, data entry errors, etc.

We also look for “straightlining,” which is when a respondent answers the same response to each question (usually in order to just get the survey over with faster).

Removing any responses that are errors and accounting for straightlining is an important factor in our analysis.

(4) How to Use COUNTA and COUNTBLANK in Excel

The Simple Spreadsheets course both affirmed the current steps in our data cleaning process (particularly in the area of handling duplicates), and added new tools into our toolbox!

One simple tool that I’ve found helpful is the COUNTA and COUNTBLANK functions.

These functions are two sides of the same coin.

• COUNTA returns the number of cells that are not empty in a specified range.
• COUNTBLANK returns the number of cells that are blank in a specified range.

We’ve used these two functions to quickly assess whether our data passes the “sniff test.”

For instance, if there is a question that we designed as mandatory for everyone in the survey but only half of the cells are populated, there is something wrong with our dataset and we need to investigate further.

Some of the possible causes could be that the question was not marked as mandatory in the survey software, the data was entered incorrectly, there was an error in translation, etc.

Basically, by using these two functions for each column in our dataset, we can get a bird’s-eye-view of the pattern of responses to each question in the survey.

(5) How to Recode Variables with IF Statements in Excel

Recoding was a game-changer for me in the data cleaning process.

Before taking Simple Spreadsheets, I didn’t know how to make the data do what we needed it to do for our analyses.

For instance, maybe the geographical information in our database was captured in cities, but I needed to organize it into regions for our stratified random sample.

Or, maybe the data contained registration dates for clients, but I needed to organize them into different categories of tenure.

I didn’t know any method to do this besides manually going through the data and recategorizing by hand.

Needless to say–WOW did Simple Spreadsheets save me time!

The IF function allowed me to recategorize data by using a simple formula.

For a practical example, I had a list of bank branches that I needed to group together into different regions. Instead of doing this manually, I was able to use the IF formula to create different groupings for the regions all at once.

(6) How to Combine Datasets Together with VLOOKUP in Excel

VLOOKUP was also an extremely helpful formula for me to get the data sets to do what we needed them to do.

Often we will have multiple datasets that we need to merge together, because we have different sources of information.

Because most of our clients have Client ID numbers, I was able to use these numbers as the common source of information in the VLOOKUP function, thus merging together datasets in minutes with confidence.

Save Yourself $125 million

I honestly can’t count the amount of times that the data cleaning process has brought us helpful insights that both ensure we have accurate results, and helped us to improve our processes in the future so that we avoid/account for any potential errors.

Simple Spreadsheets was a great help in affirming and bolstering our data cleaning process, and I hope that this article gives you a jump start into creating a similar process that suits your needs.

It’s not always the most fun process (although I’ve grown to really love it and have earned the title of “Detective” on my team ), but it is CRUCIAL to ensuring a good result.

Just ask NASA…a million dollar data cleaning system would still have saved them $124 million in the long run.

The post Watch Out for Mars! 6 Data Cleaning Steps to Save You Millions appeared first on Depict Data Studio.