If you work in sales ops, finance, or any executive-facing analytics team, you already know this. Your vice president  won’t open Tableau Server at 9 a.m. before the board meeting. They’ll download your dashboard as an image or  powerpoint, paste it into slide 17, and present it to the C-suite.

Once I accepted this reality, I started treating this as a design problem. Here are five non-negotiable factors I learned on my Tableau journey.

1. Design for PowerPoint From Day One

Device preview matters exponentially more when your dashboard will live in a powerpoint deck.

In the early stages of redesigning an executive-level sales report, I built my dashboard in Tableau’s default “Desktop Browser” view. When I downloaded it as PowerPoint, it crushed into a single slide with illegible text — a formatting disaster right before a leadership presentation.

The fix here is using Tableau’s built-in PowerPoint layout (16:9 aspect ratio) from day one.

This ensures your dashboard fits perfectly into standard Google Slides or PowerPoint without awkward cropping or white space. Don’t design for Tableau’s default dimensions — design for where your dashboard will actually be consumed.

Pro tip: Always test your export before the final version. Click “Dashboard > Export as PowerPoint” to preview exactly what stakeholders will see.

2. Accept That 80% of Functionality Disappears

This is the hardest lesson: You must build assuming zero interactivity.

What dies in PowerPoint:

• Filters (static view only)
• Parameters (whatever was selected during download)
• Hover tooltips (invisible)
• Drill-downs (gone)
• Dashboard actions (non-functional)

This changes your design strategy. Now you have to build multiple static versions of what each filter setting your users will want to view. For example, my executives were interested in seeing  pipeline performance across sales regions, sales clusters, business units, and product lines. What would have been one dashboard filter is now separate dashboards I had to create:

• “Pipeline_Review_by_Sales_Region”
• “Pipeline_Review_by_Sales_Cluster”
• “Pipeline_Review_by_Business_Unit”
• “Pipeline_Review_by_Product_Line”

Yes, it’s more work. Yes, it feels redundant. But it’s the only way to ensure your stakeholders see what they need without interactivity.

Every critical insight must be visible on page load. If it requires a click to reveal, assume it will never be seen.

3. Use Containers for Layout Control

When your dashboard contains multiple visualizations, containers keep everything locked in place during the PowerPoint export. Without them, floating objects shift unpredictably — your perfectly aligned KPI cards end up overlapping your bar chart in the downloaded version.

PowerPoint downloads don’t tolerate white space. A minimalist Tableau dashboard might look elegant on screen, but it looks unfinished and unprofessional in a deck. Executives expect dense, information-rich slides.

Why containers solve both problems:

• They lock your layout in place (no shifting elements)
• They help you maximize space efficiently (no awkward gaps)
• They give you precise control over how information flows

This dashboard exports with excessive white space, making it look unprofessional in decks.

Best practice workflow:

1. Create a low-fidelity mockup of your dashboard layout
2. Build the container structure first (horizontal and vertical containers)
3. Drop visualizations into containers last

Pro tip: Watch this Tableau container best practices video before building your next dashboard — it’ll save you hours of reformatting frustration.

4. Establish Governance Standards for Version Control and Collaboration

If you’re working collaboratively or managing multiple dashboard versions, implement a simple visual system:

Use the color coding available for dashboards:

• Green : Production-ready, safe to download
• Yellow : Work in progress, do not present
• Red : Draft/testing only

Keep consistent and clear worksheet naming conventions. This will save your sanity.)

DON’T: “Bookings (1)”, “Bookings (1)(1)”, “Sheet 3”
DO: “Q4_Bookings_Final”, “Pipeline_Review_v3”, “Pipeline Coverage_BarChart”

5. Add Company Logos

Align as closely as possible to your organization’s standard slide deck template.

Why this matters: Your dashboard might be internal today, but it’ll be in a client presentation tomorrow. When your VP forwards it externally without asking you first (and they will), professional branding matters.

Where to place logos:

• Top-left or top-right corner (consistent with company templates)
• Footer with date/data source
• Consider adding a “confidential” watermark for internal metrics

The Bottom Line

The moment you accept that your Tableau dashboard will become a PowerPoint slide, you start designing better dashboards.

Stop optimizing for interactivity. Start optimizing for screenshots.

Use the 16:9 layout. Build static versions of filtered views. Lock everything in containers. Name your worksheets like a professional. Add your company logo.

Your stakeholders don’t care about your elegant parameter actions if they can’t paste your dashboard into their Monday morning deck.

Sometimes being a great analyst means accepting that your masterpiece will be Ctrl+C’d, Ctrl+V’d into slide 23 — and designing for that reality from the start.

The post Building Tableau Dashboards for the PowerPoint Download appeared first on Nightingale.

This oversight leaves analytical nuggets frustratingly buried for audiences. However, sparse visualizations should be supplemented with a skillfully molded context that brings framing and a real-world perspective. Suddenly, a captivating data story emerges—one flush with aha discoveries awaiting anyone who cares to unlock deeper insights within the figures.

In short, relevant details breathe analytical life into lackluster data lacking links to life beyond the spreadsheet exports or SQL tables.

What do I mean by context?

Context refers to supplementary details that add helpful perspective to data visualizations. This additional background information enriches sterile statistics by exposing surface-level what the numbers quantifiably show and the more intriguing why behind the data trends and how they impact audiences. In short, thoughtful context transforms basic charts full of lifeless numbers into captivating data stories flush with insights waiting to be uncovered.

Crafting these illuminating narratives relies on artful style and ethical principles when presenting the material. The goal is engaging communication without overwhelming readers through information overload. 

Savvy designers thoughtfully spotlight only the most essential revelations upfront using attention-grabbing visuals. More obscure technical details get tucked away as secondary supplementary metadata for those interested in diving deeper rather than distracting casual readers from key takeaways. Striking that delicate balance allows data visualizations to educate, inspire, and prompt informed action.

Types of context

From temporal trajectories to benchmark comparisons, impactful supplementary details enable insightful data storytelling to fall into five key categories:

Historical data

Viewing current stats without peeking at the past provides pretty limited analysis. However, dropping in historical data trajectories over time reveals more interesting things like long-running increases reversing course, previous peaks representing new lows today, and more. Multi-year overlays, fluctuation range markers, or callouts flagging key reference points from yesteryear help demonstrate change dynamics quite clearly. For example, the 2008 recession and recovery makes far more visual sense when unemployment’s wild monthly ride includes figures from the past 20+ years.

Related statistics

Adding secondary metrics that relate to the core focus of a data visualization serves for comparison. Providing reference points regarding scale offers helpful perspective—a 14% increase seems fairly incremental on its own but appears highly notable when learning it represents a multi-decade high figure. Benchmarks also contribute context, like comparing regional literacy rates to national averages. 

For instance, let’s apply this to everyday social media stats. Platform subscription growth trends carry little standalone meaning. But combine corresponding engagement rates and ad spending totals? Their interplay sheds light on the platform’s return on investment situation.

Explanatory Details 

Hidden behind every data point lies an insightful backstory, just waiting for its chance to shine. The well-placed annotative text offers the perfect medium for the concise yet compelling narration of relevant histories, contextual developments, and the meaningful why-it-matters implications that aid sound analysis. 

Strategically positioned labels also direct attention to specific areas of intrigue within data visualizations themselves, calling out launches of initiatives, changing dynamics, or concerning outlier scenarios demanding a closer look due to their impact potential. 

Think about overlaying a line chart of ocean temperatures over recent decades with clarifying callouts. Flagging, especially warm cyclical El Niño patterns, is useful. But even more urgently, notating an unprecedented new multi-year heatwave helps contextualize an abnormal threat that demands studying sooner rather than later for ecological health’s sake. 

Methodologies information

When it comes to trusting shiny stats, transparency around how researchers gathered, cleaned, and crunched the numbers proves paramount. Supplementary peeks at these behind-the-scenes data processing activities help confirm or question legitimacy. 

Granular specifics like population sampling parameters, confidence levels, regional aggregation philosophies, outlier exclusion rules, and more allow thoughtful scrutiny of statistical significance and the extent to which findings might extrapolate more universally. Limited respondent counts, abbreviated timeframes, or unrepresentative selected demographics rightfully raise eyebrows regarding reliability and bias risks. 

But robust, large-scale polls leveraging diversity in sampling and lengthy tracking periods? Now, those offer confidence for generalization, speaking volumes. 

Metadata  

Providing attribution details and links to original public datasets enables verification of legitimacy and deeper dives for those hungry for more. Reporting timelines, compiler names, collection tools, update frequencies, and additional dimensions increase transparency. 

I’m also talking about specifics like measurement units, calculation formulas, specialized terminology clarifications, relevant subset definitions, geographical bounds for results, and more. This supplemental metadata equips audiences to parse figures with properly informed thinking, aiding comprehension. 

For example, money metrics mean entirely different things depending on whether they express net versus per capita tallies or factor inflation and purchasing power changes over time. So, clarifying definitions proves the key to aligning analytical takes rather than creating confusion.

Presentation techniques

Of course, even the most hard-hitting supplementary context loses its luster quickly if poorly integrated into data visuals. To enhance power, focus creative energies on these key incorporation strategies:

Layered approaches

The premier contextualization format involves slimming down data displays for quick digestion upfront. At the same time, interactivity allows users to access expanding degrees of detail on demand with just a click or scroll away.  

According to recent research, multi-layered designs boost viewer engagement by 32% over simplistic presets, always showing everything at once. Talk about statistical proof that less can equal more regarding good design! 

With the help of modern interfaces, observers’ journey from condensed snapshots dipping into crucial insights in bite-size form…before having the option to drill down into support windows with additional metadata for the content completists wanting extensive backstories. 

Think sidebar synopses hitting key trends from sprawling datasets. Or summarizing graphics leading to expandable accordion folds for those hungering for historical comparisons, source links, and technical notation. Such setups balance clarity for the busy with depth for intense analytical thinkers. 

Prioritized placement

Attention spans run notoriously short these days. So, design selectively—not all supplementary content proves equally enriching for all audiences. Prioritizing only the most explanatory detail for initial prominent placement keeps things focused. 

I’m talking about reserving a room for true surprising scene-stealers, paradigm shifts, and especially clarifying definitions front and center around the principal data story anchors since that’s what every viewer needs: fast fodder for connecting dots. Then, tuck the nice-to-knows like obscure technical methodology minutiae lower in the visual hierarchy without overtaxing displays. 

Comparative juxtaposition

Data opposites attract when it comes to positioning complementary charts and graphs in near vicinity rather than disjointed separation. Their relationship dynamics visually pop more when seen side-by-side. 

Consider juxtaposing a website traffic dataset next to timelines for major marketing campaigns, press mentions, and other contextual variables that may trace notable rising and falling patterns in clicks and conversions over time. Reviewing peaks and valleys on parallel tracks makes it far easier to pinpoint especially influential factors; be they positive or concerning. 

Though be considerate of assistive tech limitations!

In our excitement to layer contextual dimensions, let’s not lose sight of accessibility challenges facing those relying on screen readers or text enlargement to parse visualizations. 

Translated textual synopses and optimized text spacing are small, thoughtful tweaks, as are thorough descriptive figure captions. 

With some creative thinking, we can still enrich understanding across audiences, like converting meaningful but complex print charts into professionally voice-recorded audio descriptions for the visually impaired.

Enhancing a public health data visualization 

Let’s explore how thoughtful context can transform a basic public health data graphic into an impactful call to action. Consider a standard bar chart comparing hepatitis diagnosis rates in AnyCity, USA, from 2000-2020. On its own, it shows concerning upward trends.

Adding historical context overlays tracing rates back to the 1980s, revealing an overall decline until a reversal around 2010. Annotations link this spike to the escalating opioid crisis and risky needle-sharing. Benchmarks highlight AnyCity outpacing declines at broader state and national levels.

An adjacent graph displays a widening gap between diagnoses and stagnant treatment capacity as annotations mark funding cuts in 2015. Pull quotes spotlight a 3x increase in deaths for at-risk 25-40-year-olds now exceeding other groups’ combined mortality. Interactive maps are filterable by neighborhood and showcase low-income community diagnosis rates 5x higher than average.

With thoughtful context, sterile statistics transform into an urgent narrative: systemic lack of services enables preventable health risks to exacerbate among vulnerable groups to crisis levels. Comparative framing elucidates causes, while thorough sourcing and methodology disclosures reinforce data integrity. Most crucially, dimensional storytelling fuels advocacy.

Bringing data insights into focus 

In the endless sea of statistics facing modern audiences, making analytical discoveries heard above the data noise relies on compelling communication as much as calculative precision. Even brilliant datasets risk drifting by unseen without the escort of insightful framing to flag figures warranting a closer look.

Thoughtful context forms data’s richest mineral deposits, which are just waiting to be mined if given illuminating extraction. The right supplements expose the measurable whats and, more enticingly, the enlightening whys fueling statistical shifts and their real-world implications. Layer context judiciously, and breathtaking data stories emerge.

Yet artistry balances science in utilizing context’s revelations responsibly. Creativity and selectivity prevent deluges of information from overwhelming observers rather than enlightening them. Editorial discretion spotlights only timely insights that inspire action, not exhaust attention.

In data’s new golden age, truth and trustworthiness rise as the foremost currency. Thus, transparency around methodologies and data processing joins contextual framing as a paramount ethical pillar. Together, they compel readers to invest confidence in statistics as springboards for analytical progress rather than stagnant records gathering proverbial dust.

Approached conscientiously, data context transforms numbers’ narrative from sterile figures into stirring calls to action. However, careless application risks muddying clarity rather than focusing on insight. Ultimately, equilibrium between creativity and principles forges visualizations that enrich public understanding and drive change. The fruits of that labor? A world made wiser thanks to data stories brought to life through context.

The post The Power of Context: Making Data Stories Come Alive appeared first on Nightingale.

There are five important elements to consider in the design phase for building great data visualizations. These elements should be incorporated while designing the dashboard and serve as a checklist once the dashboard design is complete. Using these elements help insist on high standards, especially when the target audience are business users. These five elements are:

1. Data Context
2. Visual Flow
3. Color
4. Typography
5. Appeal

Let’s look into each of the elements in detail.

Data context

It is important to make sure that the data is meaningful and aggregated correctly. If “visualization” is a motor vehicle, then “data” is its engine! No matter how fancy the dashboard looks, it serves no purpose if the wrong data is displayed. Hence, display of correct numbers in the dashboard is critical.

For example, consider an analysis of sales data in different countries. Some large countries may have higher overall sales than small countries. However, the sales percentage compared with the entire population of the country could be lower. In such scenarios consider average or weighted average for determining sales performance.

Visual flow

According to research by the Nielsen Normal Group, humans read the pages in a “Z” pattern. This pattern ensures a meaningful and logical sequence. It creates a perfect visual momentum which leads the users to transition from one visual to another in a smooth manner. If “visualization” is a home, then “flow” is its floor plan! Hence having the metrics (KPI’s) as tiles at the top, charts in the middle, and filters on the sides (or at the top above KPI’s) establishes the right flow. This logical flow and consistency from one visual to the next, will ease viewer’s entry into the dashboards. It will encourage them to spend more time on the dashboard, which is one of the important goals for the builder.

Color

Choosing the color choice purposefully is very important. The human brain reacts instantly to color presentation, and this creates an important first impression. If “visualization” is a stage, then “color” is the spotlight! Color draws attention of the audience and focuses on the most important data. Hence this step has to be deliberate, consistent, and meaningful which helps users to form valuable conclusions. 

Let’s look into some more tips on color usage.

• To simplify focus on data and insights, charts should be primarily monochromatic – using one color and a gradient of it. It helps show trends in the data across a spectrum and navigate in a smooth manner. Distinct colors should be used only to highlight key metrics or different categories. It is highly recommended to not have more than 6 or 7 colors in a single dashboard. There might be some exceptions when using bar charts or in scenarios where the intention is to be colorful. 
• When it comes to color, “less is more.” Color is a form of information encoding. So extremely colorful dashboards lose the potential signaling opportunity as the standout data points will get lost in the noise of all the different colors.  A good rule of thumb is to only use color when you want to signal something. Yes, this is ‘boring’ but ensures your signals are obvious. One can also use color to highlight potential actions. For instance, it is a de facto standard across the web that blue is used to signal hyperlinks/action buttons on web pages. This is so subtle most people don’t even consciously register it, yet sub-consciously we all ‘know’ a blue button will do something.
• Be consistent in your use of color across the entire workbook.  If ‘positive growth’ is shown as green on ‘visualization 1’, make sure you don’t use green on ‘visualization 2’ to signal the ‘sales’ category. Your job is to be user/viewer obsessed and minimize the amount of ‘work’ the user/viewer must do to interact with your workbook/dashboard suite. 
• Color is culturally encoded. For example, in China, red signifies winners and green signifies losers in the stock market. In the United States, losers are red and winners are green. Therefore, build the visualizations according to your theme and the audience.

Accessibility

Studies by Colour Blind Awareness have shown that approximately 1 in 12 males and 1 in 200 females are color blind.

Worldwide, there are approximately 300+ million people with color blindness! The most common form of color deficiency is red/green color blindness followed by blue/yellow. To serve this user group, avoid using red/green or blue/yellow color pairs in the same chart where feasible. Use gradients of colors as they are accessible for those who are color blind. For instance, red/green color-blind individuals distinguish between different shades/saturation levels of green or different shades/saturation levels of red, just not between green and red. Use an online color blind simulation tool to test how people with color blindness might see your information.

Note: If the red/green or blue/yellow colors cannot be avoided, use graphic shapes, instead. 

For example, ‘✓’ could be good and ‘X’ could be bad. Avoid dashboards that look like Skittles (too many colors). Some of the effective, accessible, and ‘non-skittles’ built-in Tableau palettes are: Miller Stone, Nuriel Stone, Superfishel Stone, or Winter. If you choose an alternate palette, do not mix palettes within a workbook. Some of the alternates are not fully accessible, so be careful.

Typography

Typography deals with overall lettering and fonts of the dashboard. It impacts the message and tone of the entire dashboard helping visually organize the information. If “visualization” is a photo then “typography” is its filter!

Tip: Referring to magazines or popular websites can help understand good typography.

Example: Using Calibri font in Tableau is a good option as it has a universal appeal. 

Note: The easiest way to be consistent in Tableau Desktop is to choose the Format –> Workbook option.  Then, change the ‘All’ (first option) to Calibri.

Appeal

Appeal element deals with user attraction, attention, and engagement. It adds charm to the overall dashboard. If “visualization” is a cake, then “appeal” is its icing! Hence, it is important to add this element to the dashboard and invoke a positive response. This helps build trust with the end user. Having interactivity with the dashboard also adds to the “appeal” element.

Some of the examples for “appeal” are stated below:

• Custom Branding – including organization/team logo and name.
• Having certain custom backgrounds, images, and simple icons

The post Tips & Tricks to Improve your Dashboard Design appeared first on Nightingale.

The goal is to identify strengths and weaknesses, as well as to offer their ideas for improvement. And each time, students surpass themselves, presenting works worthy of mention in the article. Today’s story is special because the focus was not on a Tableau dashboard, but on the highlight of the Microsoft BI gallery.

It’s very heartening to see that new beautiful dashboards are starting to emerge there, as well! Because, it seems to me, the Power BI community is not as active as the Tableau community, and it always seems to be in the shadow of corporate secrecy. There aren’t as many opportunities to see the best cases, learn from experience, and develop the tool and the field!

I’m very pleased that this example is an exception! And I hope that in the future there will be more and more such examples!

I want to talk about a project led by student Christina. I also tasked her with finding an interesting case, writing an analysis of its strengths and weaknesses, and then proposing her improved version.

She chose a dashboard in Power BI authored by damtew, from the Data Stories Gallery, which caught her attention because of its bright and visually appealing central element—the world map.

Also, the captivating headline grabs the reader’s attention, engaging them to delve deeper into exploring the project.

Let’s take a look at Christina’s main analysis of this dashboard and its description:

• At first glance, the dashboard features a pleasant color palette used in the charts, slicers, and other visual elements.
• The dashboard has a fairly classic structure: a title, summary cards, slicers, and the workspace.
• The workspace itself consists of classic charts such as line charts, grouped histograms, stacked histograms, as well as some less traditional elements like a “Sunburst” style chart and a ring chart describing an interactive map icon.

The dashboard is titled “Trend of Global Conflict Events and Mortality.” In the process of working with the dashboard, you can obtain the following information:

• The intensity of fatal conflict events worldwide from 1997 to the present (in regions and countries with statistical data).
• The total number of fatalities and conflict events worldwide/regionally/nationally for any period from 1997 to the present.
• The dynamics of the number of conflict events and fatalities worldwide/regionally/nationally for any period from 1997 to the present.
• A comparison of the number of conflict events and fatalities for the years 2022 and 2023.

After a detailed description, Kristina highlighted the project’s advantages that appealed to her specifically:

1. The presence of a map and its color coding allows for a visual representation of the distribution and intensity of conflict hotspots and mortality rates across the globe. An interesting and visual representation of the relationship between conflict events and mortality is provided by a pie chart describing the map of the selected region, country, or the entire globe.

2. Visual representation of regional breakdown in the form of icons of different parts of the world + a breakdown by all countries of the world, where you can see indicators for each specific country if there is statistical data available. This solution looks very interesting.

3. Harmonious arrangement of dashboard elements + color scheme. Despite the fact that the dashboard does not meet all the canons of the classic dashboard structure, overall it effectively utilizes space and leaves a pleasant impression.

But Kristina also noted the drawbacks here; in even the most perfect project, there are always areas for growth, opportunities for improvement. And this is excellent practice for students!

Drawbacks

1. The positioning of the color legend is shifted to the top right corner, which makes it necessary to constantly refer to it and compare it with the map; this requires constantly searching for it visually. Additionally, there is a lack of segmentation in the digital expression, meaning it’s possible to determine where the mortality rate is higher or lower on the map, but it’s impossible to determine this in absolute terms.

2. Weak color segmentation: Angola and Namibia have color shades that are adjacent to each other, meaning they differ by only one tone. However, Angola’s death toll is 44,825 people, while Namibia’s is only 71 for the same period. This is a significant disparity in numbers, but the slight color difference may give the impression that these countries have comparable statistics, which is not the correct conclusion.

3. Different color codes for the same indicator on different charts: mortality on the circular diagram describing the map is shown in dark yellow color, while the same indicator on the average histogram on the right side of the dashboard is encoded in pink color.

4. Lack of chart titles, including the circular diagram describing the world map, which wastes the precious time of the “viewer” to immerse themselves in it and understand what the author meant. The maximum that can be found on the diagrams is the legend.

5. Alphabetical arrangement of the month-switching buttons below the mortality rate dynamics chart creates difficulties in perception and usage of this tool. A chronological arrangement of the buttons seems more intuitive.

6. The non-obvious choice of data visualization using a variation diagram between sunbeams and a circular one raises questions about whether there is color coding here in the context of intensity or if the colors correspond to a specific region. If so, there is a color legend nearby, but it is very misleading.

7. Another less effective, in my opinion, way of visualization is the stacked bar chart. The indicators of the number of conflict events and the level of mortality are very widely scattered due to the fact that the time range in which the graph is plotted can be very long, as a result, there is no way to interpret the results on the histogram over a long period of time, not even the functionality with the mouse cursor and the appearance of detailed information about the block helps, because the bars of the chart are catastrophically small in themselves, and considering that one column contains two categories, there is no way to hit the cursor to the right one.

8. The last but not least drawback concerns the arrangement of filters on the dashboard, which are separated “territorially”: the year filter is located in the upper central part, the country filter in the lower part on the left, and the month filter in the lower central part of the dashboard. As a result, to see the picture of what is happening in a specific country and specific time range, one needs to refer to filters scattered in different parts of the page.

Contentious Points
The dashboard completely lacks static data labels, the perception of the picture seems to be present, but rather in terms of ratios and proportions with a weak understanding of absolute values, although some charts display a scale with values, which helps to understand the numbers, but clearly not enough. I assume that the author was seeking true harmony between design, content, readability, and effective use of space. Perhaps the recipe should be sought in visualization methods.

Well, if you’re critiquing, then offer solutions. 
I particularly enjoy this stage in student learning—after they start seeing problems in visualizations, they learn to formulate solutions and improvements! And this will be useful both in their personal projects and will improve the quality of collaborative work. So, Christina’s next steps were to formulate her suggestions for improving the dashboard and create an alternative version of the project, taking into account her recommendations!

Suggestions

Dashboard grid:

• Look for more traditional locations to place cards and legends with color coding and reformat their representation by removing unnecessary labels on the summary cards and adding segmentation indicators to the color characteristics accordingly.
• Allocate more horizontal space to the mortality rate trend graph to display data labels + on the same graph, you can show the dynamics of the number of conflict events. It is also worth working on color coding so that the same indicator has the same color code throughout the dashboard, rather than varying from chart to chart.
• Group all filters in one place for comfortable and quick adjustment, the most convenient place to move filters seems to be the upper right corner.

Diagrams:

• Add a card with the indicator “Number of countries involved in conflicts,” this indicator seems significant in the new realities, as 2023 has become a record-breaking year in 30 years for the number of conflicts worldwide according to the International Institute for Strategic Studies.

• Add names and units of measurement to all visualization elements without exception, including the world map.

• Add data labels or other means of identifying values.

And here is the result. The layout of Christina’s final improvement proposal, it is not assembled in Power BI, it is only a design project, but it is required for student work. Doing something similar with specific data in Power BI is already a technical matter! Many thanks to Christina for her work and an interesting case! Take a look at what we ended up with. The original project already looks quite interesting, but after such a redesign, its appearance has been greatly refreshed.

In conclusion! I truly hope that the Power BI community will become more active in sharing their cases, and that there will be more product diversification in the data visualization industry. I hope Power BI will provide strong competition to the excellent Tableau in terms of dashboard examples, competitions, and interesting case galleries!

The post From Analysis to Improvement: How Students Transform Projects appeared first on Nightingale.

You’ve probably encountered them before. They can whip up a bunch of tables and be confident that everyone will understand their analytics. 

“The data is all here, simple and obvious. Look, there are twenty filters right here! What do you mean, it’s not clear? Why do we need these columns and circles… You just don’t understand the content!”

In my experience working with business clients, these folks are usually finance professionals, representatives of exact sciences, engineers – they know their field of expertise well. But when it comes to conveying their ideas and insights to other departments or company leadership, their hands turn into paws. Data communication is not always their strong suit, yet many are convinced that they are doing just fine.

They are know-it-all neophytes.
They are overconfident outcasts.
They are stubborn luddites.

For purposes of this article, let’s call them data viz renegades. Throughout my career working alongside these renegades, I’ve noticed that they struggle to create clear and visually appealing reports so that the viewer doesn’t bleed from their eyes. And yet they often resist suggestions for improvement; they are either convinced that their work makes sense, or they have become complacent with their work over the years, excusing themselves because data visualization doesn’t interest them or isn’t—and will never be!—their strong suit.

So, for many years, they avoided learning and resisted improving. But now, the world has changed so much that more and more people are forced to work with data and then visualize the results of that work. In the past, only a few professions were learning this skill, but now everyone from bankers to factory workers are forced to deal with it.

My mission is to guide them toward the brighter side!

Is it possible to communicate with data viz renegades?… It’s as if they’ve closed themselves off and now deny the very possibility that something will work for them. So it’s easy to believe there’s no point in trying anymore… 

Should we give up?

Turning the Beast into the Beauty

No, giving up is not necessary at all! Here’s the approach I found for these renegades, and I recommend it to everyone dealing with such challenging learners:

The key message should be as follows:

“I don’t want to turn you into designers, but I want to teach you how to brief a designer! Because if you tell someone (who is far from your field of expertise) to do something with your raw data, the designer will likely create beautiful looking tables and charts that do not show the most important information. The designs may be something completely unpredictable, or only according to the designer’s understanding. So it’s important to take responsibility for this product and learn how to set the task correctly and get an acceptable result later.”

Surprisingly, they understood this approach because they are usually experienced individuals, often in high positions, who have dealt with task assignment and project acceptance during their careers. The initial fears of having to become a data viz expert are alleviated!

Now we can dive into the theory and basic concepts of data visualization! Just be prepared; progress will be slow. It’s a challenging path, and any teacher or trainer may feel drained and useless with such learners. Take comfort in knowing that this hard-earned skill they acquire will be retained and multiplied, bringing them much benefit. After all, it’s the development of our weaknesses that strengthens us — not polishing what we already know how to do.

The Story of Frank, a Wild, Wild Data Viz Renegade

Let me tell you more through the example of my student, Frank, who is 50 years old! He is lively, full of ideas, confident, a master in his field, and not afraid of challenges!

“I don’t need your dashboards! I’ve got everything under control!” he told me. Yet his initial attempts at dashboards are enough to scare any data viz specialist and evoke disgust towards dashboards in any manager!…

But I gathered enough patience to turn this data-viz monster into a beauty! I’ll explain in detail how I did it. And I recommend these steps to anyone dealing with difficult learners:

• First, I asked him to show what he was doing, encouraging him to explain, as it helps material better sink in.
• I tried to be compassionate and empathetic because Frank is as far from data viz as possible; it’s like something completely incomprehensible for him.
• I constantly praised him, not sparing any compliments! Every tiny step, as small as building a simple chart, was valuable! He had a long string of failures behind him, and even the slightest difficulty could shake his self-confidence.
• I lowered the bar of my expectations. A lot. I didn’t expect the successes I usually anticipate from an average student. I settled for less. It’s like a child taking their first steps; don’t expect them to salsa dance for you in a week.
• I stocked up on patience – a massive bag of patience! – I was prepared to repeat the same thing many times, tried not to get annoyed, aimed to be more tolerant and kind.

The results would come, but it would take several times longer than with an ordinary student. I tried to prepare for that and adjust my mindset accordingly. After all, I understand that if someone can grasp data viz in two days of an intensive course, then a monthly marathon may not yield the same results.

And all the efforts will pay off.

And They Lived Happily Ever After

After months of regular sessions, buckets of my tears, and his sweat, Frank chuckled and confidently opened his latest dashboard version. I could barely contain a shout of joy.

His project might not have been perfect, but it was a decent dashboard, user-friendly, and capable of delivering value.

And he did it himself – is there a greater reason for a teacher’s pride than the success of a student?

I’m sure a resourceful guy like Frank will find ways to use dashboards in his department and squeeze the maximum benefit from his new skills – he’s incredibly persistent!

And I’m happy that another person has mastered data visualization and will be able to work more efficiently with it. 

Hooray, another little star in the data-viz galaxy!

The post We Cannot Give Up on the Data Viz Renegades! appeared first on Nightingale.

So, can we make the data the guiding light it has to be without shining too brightly?  Let’s closely analyze how data is transformed into visuals, which is where I believe the real challenge arises. Unlike earlier automated stages, this step depends on human interpretation to turn pixels into actionable knowledge. It’s where noise peaks and misinterpretation risks are highest, from superfluous graphics and irrelevant metrics to data integrity issues. We also need to keep in mind that from the perspective of informational theory, which studies how to transmit signals efficiently, a dashboard is a communication channel.

Channels, in our case, dashboards, have cognitive limits, however. This limit is believed to be around 120 bits per second, the brain’s max speed of conscious processing of information. Although our brain can process up to 11 million bits, we can only consciously process 120 bits by design. The efficiency of our brains allows us to filter and compress millions of data points to only a fraction of the most critical information, which we can process immediately and consciously.

Additionally, our attention span is measured in seconds, and with the demands of multitasking, meetings, random work chat messages, and calls, the time available to process incoming signals from a digital canvas is decreasing. While our attention span used to be about 21 seconds, now it is closer to eight seconds.

As we process the incoming signals, spending our precious seconds and conscious effort, we can hold only 5-7 information items simultaneously in active memory. So even when there is time and appropriate attention to process the signals from data, these signals must come in small doses. 

Additionally, noise is amplified when there is a significant mismatch between the sender and user in the domain expertise. Managers are typically subject matter experts while analysts are not. If this mismatch is too great, the dashboard can become inherently noisy, rendering valuable information trapped and ineffective. 

Furthermore, the user often must improve their data literacy when it comes to dashboards, learning to navigate dashboards effectively, including utilizing filters and interactive features. 

Turning data into effective signals

Dashboard-ready analytics and metrics

First,  we need to maximize the informational value per visual. To do that, metrics must have three qualities:

1. Be relative or an index compared to a benchmark
2. Be filtered to the dashboard use case 
3. Be set against a comparable benchmark (i.e. plan, budget, and limit). 

Be relative or an index compared to a benchmark

Our understanding of crafting relative metrics for data-driven decisions has advanced significantly in recent decades. Techniques like the balanced scorecard, unit economics, ratios, and contribution analysis share a common thread: they all reveal how one metric relates to another. Consequently, these metrics offer threefold or more informational value for the same user attention time. For instance, a visual showing margin percent proves more insightful to a decision-maker than standalone profit figures. Across various industries, whether ratios, margins, or unit economics, a common pattern emerges—a numerator and a denominator. As these index KPIs evolve, they accumulate even richer insights by tapping into the values of underlying KPIs and their connections to benchmarks.

Be filtered to the dashboard use case

It is not enough to visualize just one base formula. It is better when a range of derivative measures is present for selection; this allows users to choose different aggregation levels and periods without interrupting the analysis flow. Five to 10 derivative formulas should support a single metric. Here, analytics becomes an instrument that maximizes user experience.

Be set against a comparable benchmark

Expected KPI values are essential to the informational value of the whole visual. They provide context even to the person unfamiliar with the subject matter. Benchmarks, plans, or limits usually come from manual analysis done by the managers. In these simple digits, a wealth of research is hidden. A good dashboard must use this wealth to enrich all data with a straightforward method–comparing the current and planned values.   

Resulting in last-mile analytics

I call metrics that have these three characteristics last-mile metrics. These metrics are delivered and handed to the decision-maker the same way a postal package is handed to the recipient on the last mile of delivery. Akin to the product’s journey, from the warehouse shelf to the back of the truck to the customer’s doorstep, data insight traveled from the source database to the data warehouse and finally to the dashboard. This last leg of the delivery process is the most critical both in the supply chain and in data analytics.   

When metrics have these three qualities, the last mile handover is now likely to be successful. 

Data modeling for best filtering and drill-downs

Second, dimension filtering options allow the user to receive more signals.  For this, a data model must be in place. Data models dictate the table structures and their relationships. The most valuable data model schema, in my experience, is the star schema. The star schema uses a central fact table surrounded and supported by a range of dimension tables. Usually, each business process should have its own star. As complexity increases, stars can become constellations.

This approach takes more time to prepare and model than just visualizing a pre-filtered queried table. Still, because it gives the user greater control of how and when to drill down, it alleviates data overload significantly.  With a digital canvas of visuals built on a star schema model, users can slice and dice the same absolute and relationship metrics across all filters.

As the visuals change, their movement captures the brain’s attention without conscious effort.
This changing canvas also allows us to mitigate the cognitive load problem when we show too many visuals simultaneously. Hence, the user can interact with the channel and request more signs once ready (by choosing filters or pressing buttons). With each interaction, as the canvas becomes a familiar setting of graphs and charts, the variety and volume of visuals can increase gradually without causing a data overload.

Data visualizations maximize the use of the user’s attention.

Third, data visualization principles must play two critical functions per informational theory. First, the visual hierarchy should control which signals (or visuals) will be noticed first and last. 

The pre-attentive attributes of color and size best create a visual hierarchy. Research studies have shown that the brain can process visual information, including color, in as little as 13 milliseconds. Visualizations that are larger and with greater color contrast will get the first milliseconds of attention. The visual system will scan for the next largest and contrasting object as it takes in information. More important signals are placed from the top left to bottom right (when users read from left to right).

Why should there be a difference in the order in which we need the information processed? As we know, the capacity of short-term memory is limited. Hence, the signal sender should not show too many visuals with equal importance simultaneously.

The second function of data visualization principles is to minimize noise. From the perspective of informational theory, noise is unwanted variations or disturbances that can corrupt or interfere with the accurate transmission or reception of information. In our communication system, noise has many ways to introduce itself:

1. Visual noise: Visual clutter is elements that do not add any informational value or distract from the intended message. It could be excessive use of colors, icons, or graphical elements that do not contribute to conveying the relevant message.
2. Data Noise: Data noise is inaccuracies, inconsistencies, or irrelevant data points that can confuse or mislead the user. The sender can introduce this noise even with error-free data if he lacks the domain knowledge to design visuals as signals.
3. Interface Noise: Interface noise refers to design or usability issues that hinder the user’s ability to interact with the canvas effectively. This could include confusing layouts, unclear labels, or intuitive navigation, making it difficult for the user to access and interpret the messages.

One of the effective ways to decrease clutter is to use Gestalt principles. There are four principles: Proximity, Similarity, Continuity, and Closure. Their use makes it easier to delete unnecessary lines, group similar items without using pixels to highlight the grouping, and reduce compleх shapes to their essential forms. Less unnecessary clutter means less noise without compromising the intended message. Using Gestalt principles to minimize noise, we use the existing universal encoding mechanism of the mind to eliminate unnecessary pixels, increasing the pixel-to-data ratio.

UX/UI design principles to customize the design to the use cases

One dashboard cannot send all of the messages, and trying to put all the information on a single canvas can overwhelm the user. Collecting interrelated dashboards and reports can solve this problem and provide insights in manageable doses. In essence, by creating a system of dashboards, we are increasing the channel’s capacity to transmit signals to each user. Each canvas communicates fewer signals, but the user can request more signals once his mind processes the first batch. The user can request more signals through interactive elements such as page transitions, buttons, pop-ups, and drill-downs.

We also can tailor each canvas to the use case of the receiver. The channels’ capacity depends on the use case. The user or receiver could be a busy CEO with 5 minutes to get the most important signals. Such use cases require specific visuals highlighting the current state versus the target. Or the user could be a middle manager tasked with looking deeper into the causes of the recent underperformance of an indicator. In this use case, we can use a table with many filters. Another sometimes overlooked channel is the mobile use case. A simple picture of key metric visuals sent regularly to a group chat of executives and managers can do wonders in terms of sending signals as fast as possible.

Conclusion

The data overload highlighted in the Oracle report can be addressed more effectively if we treat dashboards as communication channels. Here’s how:

• Establish a shared knowledge base between the sender and the user.
• Enable interactive dimensional filtering with star data models.
• Transform KPIs into relative forms by comparing them with expected values.
• Implement an organized system of dashboards with user-friendly navigation.
• Utilize pre-attentive attributes to establish a visual hierarchy.
• Reduce visual clutter using Gestalt principles.

Footnotes

[1] Csikszentmihalyi, Mihaly (1990). Flow: The Psychology of Optimal Experience.

This article was edited by Catherine Ramsdell.

The post Making Dashboards Optimal for Human Brain Processing appeared first on Nightingale.

“Create a dashboard for internal analysts on the media buying team to use to determine which ads are the most successful.”

“Create a journalistic style piece that balances text and visualization to tell the story of the impact of Tony nominations on a Broadway show’s longevity.” 

However, these kinds of specifications are generally less defined when creating visualizations for fun, or for contests, or for a client who has given more open-ended guidelines. If no audience is defined, do you aim to serve as wide an audience as possible? Or choose for yourself who your intended audience is? Do you narrow in on one question, or allow the user to explore and develop their own insights? 

There is no right answer to any of these questions, which—while part of the fun—can make it challenging to find a starting point or direction. 

When the contest opened for the World Data Visualization Prize in December 2022—a dataviz competition run by Information Is Beautiful in partnership with the World Government Summit—I immediately knew I wanted to enter the “Dashboard of the Present Future” category using Svelte and D3.js, my current favorite visualization tools. I always enjoy entering contests because they provide a deadline and limit my ability to entirely overthink.

There was a dataset of more than 30 metrics for each country with the goal for “governments to see how well they, their nation, and their populace are doing.” This provided a loose boundary to work within. However, the prompt of, “What seems relevant or interesting to you?” reiterated that I had total control in choosing the focus. This prompt also served as a gentle reminder to get outside of my traditional way of thinking. However, these are the challenges I love the most because, selfishly, I can try new things and organize the data however I see fit.

The contest description also suggested that I “get into the head of a decision maker.” While it was helpful to know that someone would hypothetically use my creation to make informed decisions, it also left out what types of decisions or in what context the decisions would be made. A president of a country would be making different decisions than the head of a government agency or a worker within an agency, for example. My first reaction to this line of thinking was that the user would need to have a starting point—a specific country they wanted to evaluate. No matter their position or goal, each decision maker had that in common. 

However, I also began to question if that was too specific or isolating. Realistically, this was for a contest where the judges were data visualization experts and not government decision makers. What starting point would benefit them? The country they lived in, or one they were considering visiting? Would it be more natural and intuitive for them to start with a visualization that showcased the whole world and then be able to drill down into a region, and then country? I wanted to keep this broader data visualization community in mind as I figured out my visualization.

Wireframing layout options

After reading through the prompts, I went on a run. I had a podcast playing, but quickly realized I was not listening to a single word and was instead arranging the data into different layouts in my head. When I returned home, I quickly sketched out the different layouts I had envisioned.

Here is a gallery of three of those sketches that illustrate the evolution of my thought process:

I realized that I wanted to further explore layout option 1 because it seemed the most concise and dashboard-like. Additionally, I worried if I layered in a “world” visualization and then a “region” visualization, it would be too repetitive and overwhelming. So, instead of adding these larger geographies as their own steps, I incorporated them into a country’s view.

I also began to think further of ways this layout could satisfy my key audiences—government officials (hypothetically) and the data viz expert judges (in reality). How could I make my visualization focus on one thing at a time while also facilitating exploration so a user could find interesting points to focus on? I thought about this through the idea of context and comparisons. If I took the approach of looking at a single country, how would the user determine what was good or bad? Additionally, if the country were deemed “bad” at something, government officials may want a sense for where to turn for advice. Could this visualization show countries that performed comparatively better? Before making any further decisions, I began analyzing the data to see what it would tell me. As someone who lives in the United States, I imagined myself as a government decision maker to determine what information was most useful to generate action or provide a starting point for further research.

Analyzing the data

I started by looking at a single metric—extreme poverty—and saw how it differed by geography. This immediately helped me realize that viewing data at a regional level would add a layer of context. From the view of the United States government, I did not think it would be helpful to say, “Yeah, we are good because less than 1% of the U.S. population lives in extreme poverty while Zimbabwe’s stands at 13.5%” Instead, a more helpful comparisons would be to see that the United States is only slightly better than Mexico and is actually worse than some other countries in the Americas. 

Next, I looked at CO2 equivalent emissions (also known as CO2e) per capita. Knowing that more industrialized, wealthy countries historically produce more emissions, I wanted to find a way to bring income level in. It is helpful in some contexts to see the United States is one of the worst in the world. However, from a decision making perspective, being able to isolate countries with similar resources and see which have lower emissions could be a useful guide for policymakers who are focused on climate policies or technologies.

My last step in the analysis process was to view all metrics pertaining to the United States. I determined the country’s percentile for each metric. This view seemed helpful because the viewer could clearly see where the United States was falling short and where it was doing well. This helped me verify that I could categorize the metrics as “doing well” or  “opportunity for improvement.”

As I was analyzing the data I realized there were limitations of my own knowledge that could prevent me from accurately understanding or displaying a metric. Was I overlooking some obvious reason why a country was better or worse? Was not weighting by population the wrong strategy? I didn’t want this to hold me back or prevent me from continuing on, but I wanted to make sure everything was as accurate as possible. I went to the source of each metric and read the formal definition (which I then captured to include in my visualization) to ease my worries.

Selecting visualizations

With my layout in mind, and using region and income level metrics to play around, I began focusing on which visualizations to choose. I pretty quickly landed on the idea of using a radial with one spoke for each metric. However, I knew for the radial to be effective, I would need to minimize the number of points on each spoke. I also knew that radials weren’t a common dashboard visualization—instead, one would opt for standard dot plot grids—but I was set on making it work because it gave me the ability to sort and layer in a donut chart on the outside.

My initial thought was the user could choose between two views:

1. World view: for each metric, see the average of each of the six regions or the average of each of the three income levels.

2. Country view: for each metric, choose a country and compare it to countries sharing its income level, region, or the world.

But I struggled with the world view. On the country view, the focus was very clear and each country could be highlighted (and designed) differently than what it was being compared to. However, on the world view, it would be hard for the user to make a holistic assessment of the metric. And despite only having three dots per spoke to simplify the view, it still was a lot of information to take in!

Once I made the low income and middle income circles smaller and the same color, the high income circles popped more.

This solidified my theory that the user should choose one thing to focus on: one country, one region, or one income level.

I returned to my usual running grounds in Central Park and began thinking through how I would turn my ideas into a higher fidelity mockup. I realized that I was overcomplicating things by having the user make a lot of decisions and I needed to simplify (much like my favorite activity is just to run and not have to constantly decide where, what pace, how long, etc.). I wanted there to be no sorting or no choosing between rank versus average versus median. Just choose the focus and go. My hope was that this would make it feel more approachable.

I still wanted there to be the ability to dive deeper—if one chose to. So, I created the ability to click on a single metric and view it for all countries. Whichever countries were part of the “focus” would then also be highlighted to allow users to see outliers or gaps.

Adding interactivity

I still felt like something was missing. How was the user to choose which thing to focus on? Given that this was a dashboard, I wanted the experience to be highly interactive. I already had a dropdown to choose the country, income level, or region to be the focus, but this didn’t feel like enough. The user still had to know what to find in the dropdown. This was sufficient for government officials who wanted to look at themselves, but it wasn’t as useful for general exploration.

My first thought was to use tooltips to switch the focus from one type of comparison to another (for example, the United States could switch to a regional comparison of the Americas or an income comparison for high-income countries). If the user found an interesting data point, for instance, they could hover over it and have the ability to change the whole dashboard to focus on that metric. This allowed for on-the-fly exploration. If a government official were looking at their country and wanted to expand to their broader region or income level, they could. However, this still required the user to engage, interact, and be happy with the default selection.

A shuffle button to bring it all together

To address this problem, I considered a shuffle button. As I was looking for inspiration, I stumbled upon The Climate Pledge. They have a shuffle button written into one of the main headers of the page. My first reaction was to click on it and find out what it did. It instantly clicked that this was a way for people to use my dashboard in a fun, intriguing way that still offered a lot of potential for exploration.

I thought the usefulness of the shuffle button was two-fold. First, it allowed for exploration of the entire dataset. Most of the time when given options to choose, people will choose what is familiar to them, such as where they live or like to visit. However, a shuffle button opens up ways to learn about new countries or regions outside of what is known to them. This could be used to offer new perspectives to government officials, but more importantly, it makes the dashboard more exciting, digestible, and usable for those outside of government or decision making roles.

Would a shuffle button ever be seen in a business dashboard? Probably not. However, finding solutions for a wide audience opens up the opportunity to explore new ways of interacting with data that meet a variety of needs.

Personally, I was very happy with what I created (check out my final project here) and I enjoyed experimenting with taking risks by adding a not-so-common dashboard feature. As I was finalizing development and looking for bugs, I found myself continually hitting the shuffle button. I was so fascinated each time the new data transitioned in and I learned about a different place, that I was digesting the information and diving deeper instead of looking at it from a testing perspective. I had spent weeks analyzing and looking at the data and yet I was still able to find insights I hadn’t seen before! Business dashboards are often crafted with speed-to-insight in mind, however creating this visualization helped me realize slowing down and adding engaging ways to “play” with the data can also aid insight.

The post Finding The Right Elements For an Award-Winning Dashboard appeared first on Nightingale.

Why visualise data?

To quote Andy Kirk, “we can look at data, but we cannot really see it. To see data, we need to represent it in a different, visual form.” So, in an attempt to make data more accessible, you may create more visual representations – dots, lines, shapes, and colours. These building blocks combine to create all sorts of charts and pictures helping readers understand numbers. 

Although the purpose of visualising data is clear (and universal), the reasons can be different. The reason you visualise data, will help you determine the appropriate visual.

What’s your reason for visualising data?

In business, data is visualised to:

• Discover insight through data analysis
• Inform about specific data
• Educate about specific data

The reason you visualise data largely depends on your audience, so understanding more about who the visual is for will help you to design it.

How to determine your reason for visualising data 

Designing a data visual for yourself is easy

Sometimes, you will be the audience for a visual you design. When designing for yourself, there are no rules. The visual can look how you like, because it’s only you who needs to understand it. Despite this, be clear on your visual’s intent; will it be used to analyse or track data? A visual used for analysis (to discover) may have a shorter lifecycle – and therefore not need to be built as robustly – as a visual used for tracking data (to inform).

Designing a data visual for others is harder

Outside the analysis process, visuals you design will likely be for someone else. In this case, how much the audience already knows about a subject determines your reason for visualising the data. If the audience understands the implication of a high or low metric, it is a distraction to show anything else. Alternatively, if the audience has little subject knowledge, they will struggle to understand a data visual without context. 

Your reason will influence your design

When visualising data to discover insights, you can design to your own liking. From experience, this is a messy, unordered, colourful process. But as long as the visual is acting as an analytics tool (rather than a communication tool), it doesn’t matter what it looks like… with one exception – if you share it. Sharing a discovery visual changes its reason for visualising data, and therefore your design is likely to require changes, too. 

When sharing a data visual, you need to consider the preferences, knowledge, and data literacy of an audience other than yourself. The idea that a visual can be used by an audience to either explore or explain data is widely understood. But this design choice is not binary; your visual can sit anywhere on the explore/explain spectrum. The reason you’re visualising data helps determine how exploratory or explanatory your design needs to be. 

Data visualisation reasons on the explore/explain spectrum

The same data can be visualised (differently) for multiple reasons before enabling a data-driven decision

Using one design to discover, inform, and educate, will most likely not succeed for all reasons. For example, visuals created to analyse data often fail to communicate it. Being clear on why you’re visualising data will result in more fit-for-purpose design. 

So, before designing your next data visual, try and clarify the reason you need it. Will analysing a series of charts help you to discover insight? Will a dashboard inform you (or your audience) of changing metrics? Will a data story educate your audience about the data’s significance?

Don’t be surprised if you need to visualise the same data (differently) for these three reasons. This understanding shows the progress you’re making on the pathway to a data-driven decision. 

Data visualisation reasons within the data insight pathway

The post Reasons to Visualise the Same Data, Differently appeared first on Nightingale.

So, I am dedicating this article to some of my favorite ways to visualize change between two time points that go beyond the clustered bar chart.

Before diving into the new, we are going to take a walk down data design memory lane.

First, let’s set the scene.

The data

For this post, I generated education data for a fictitious school district. This school district has four schools. The superintendent asked the lead data analyst for the school district to produce a display showing the change in the percentage of students across the four schools who were proficient in math between 2018 and 2019:

Bar chart basics

After a first pass of the data, the analyst produced a regular, old, clustered (horizontal) bar chart:

Clustered Horizontal Bar Chart

The (horizontal) clustered bar chart is a tried-and-true design choice. These charts display data categories on the vertical axis, while values are plotted (in horizontal bars) on the horizontal axis.

A clustered bar chart emphasizes differences in a variable by visually grouping related items. When visualizing change using this chart type, some choose to display the newest time point bar above the oldest time point bar.

Sure, it’s simple…and a little boring. I would even go as far as to argue that it is a bit confusing to interpret, no matter which order you place the bars in or how you choose to label the data points. So, back to the drawing board.

After another look at the original design, the analyst thought rotating the chart might spruce things up.

Clustered Column Chart

The clustered column chart is another conventional visualization. These charts display data as a series of grouped vertical bars across the horizontal axis, with values plotted on the vertical axis. But like the clustered bar chart, clustered column charts are not particularly creative and can become cluttered and confusing when many categories are shown.

Now that we have gotten those commemorative charts out of the way let’s look at alternative chart types for visualizing change.

Alternative designs

This is the part of the story when the analyst consults a data designer, and they graciously share their knowledge. And trust me, they are EAGER to expand the analyst’s chart repertoire.

First on their list: the dumbbell plot.

Dumbbell Plot

A (horizontal) dumbbell plot is a visualization that shows the differences between two points. Here, the focus is on the distance between the points. Values are displayed across the horizontal axis, while data categories are shown along the vertical axis. Dumbbell plots are a fantastic choice as their straightforward design minimizes visual clutter, making them easy to read and understand.

Another option shared by the designer is the slope graph.

Slope Graph

Slope graphs are line charts showing (the magnitude and direction of) change between two points for multiple categories. When using this graph, the steepness of the slope of the lines connecting the two points is of interest; a steeper (or flatter) slope indicates a larger (smaller) change.

Since the designer was not sure how many schools would be included in the display, they also taught the analyst about the ‘small multiples’ approach.

Slope Graph Small Multiples

A small multiple is a series of similar graphs or charts arranged in a grid layout. Each chart in a small multiples series shares the same axes, scales, size, and shape. Using the small multiples approach, each school in the original display has its own chart.

This technique is preferred by designers when a chart has many categories. The uniform design and the increased white space simplify interpretation and allow easy comparisons across categories.

A final choice is an icon.

Icons

Icons are graphical symbols that communicate a concept. For example, an upward arrow or triangle pointing upwards could indicate ‘increasing.’

Okay. Okay. Icons are not a chart type per se. BUT icons are a powerful way to communicate information, AND they can easily be incorporated into a variety of formats, including reports, dashboards, and stand-alone tables.

When visualizing change, I love to embed icons within a larger data table. Why? It’s an easy way to appeal to folks who not only need to see the numbers, but also people who want to know whether something changed (and whether the change is positive or negative).

Now, I want to go on record and say that bar charts ARE NOT BORING. I get A LOT of use out of bar charts.

But you know what is boring? A report, slide deck, or dashboard that ONLY includes bar charts.

There are so many visualization techniques at your disposal for displaying change over two time points. Depending on your goal and audience, one option may be more effective than another.

So, don’t limit yourself. Venture beyond the clustered bar chart.

The post Beyond the Bar: Alternative Methods for Visualizing Two Points of Change appeared first on Nightingale.

About a year ago, I was asked to comment on using a version of this chart for a client dashboard.

The person requesting the review called the chart a rose diagram based on the name in their software library, but I don’t believe that they used an area encoding, so I think it is actually just a stacked pie chart stretched around a circle, and not an actual rose diagram. If you look at the chart closely, you will see that it contains exactly the same data as the (much more readable) stacked bar chart, but the requester was feeling pressured by a client to add the rose diagram to the dashboard because it needed to be more “fun.”

If you’ve read my previous article, you already know how I feel about fun in data vis. If adding some visual interest doesn’t conflict with the primary purpose of the chart, then I’m all for it. Visual flair is fine in its place, as long as it doesn’t interfere with the job of a chart in this context: to support a user task. In an analytical setting such as a business informatics dashboard, I take a pretty hard line on this distinction.

In my experience, the request to make things “more exciting” usually has less to do with meeting an end user’s need than it does with enforcing compliance — it’s often middle management’s first answer to the question, “how do I get people to actually look at this thing that we spent so much time and money creating?” As often happens, examining the underlying need rather than the request often reveals a much more interesting question of company culture, combined with the perceived relevance and usefulness of the information presented, and is almost always better addressed in a different way.

As a designer, it is your job to care more about your user’s comfort and ability to successfully complete their task than you care about your own aesthetic impulse. To me, wavering on that commitment is self-indulgent, especially in the type of task-focused work where you will find most data vis. There’s always a balance to strike here, but when you choose to put your own creative urge before your user’s needs, then you are making art, not design. There is nothing wrong with art: it is a beautiful and valid pursuit that illuminates and enriches the human condition. We could all use more art in our lives — at the appropriate time, and in the right context. 

There is real value in using aesthetics to create an experience or inspire a comparison that someone might not otherwise get from a chart: work that pushes the edges of what counts as “data” and whose perspective matters can provoke important conversations about bias, representation, and the dominant power structures that undergird our definitions of truth. I personally delight in indulging and encouraging the eccentric perspective; it just depends on the situation.

Of course, aesthetics and function absolutely can (and absolutely should) work together toward the same goal. Aesthetic appeal performs a critical function in making design that delights, which is something that we should all strive for, even in a business dashboard. Analytically-focused solutions that dismiss design as unnecessary “window-dressing” often overlook its functional contributions. Disregarding the human experience of your users will always diminish the quality and success of your work. 

Great design means finding that elusive area of overlap where function and form align to create something that supports a task and considers the needs and desires of the human using it, in the context for which it is intended. Function that disregards form is simply not as good (or as useful) as a design that takes the user into account. In cases where the aesthetic impulse overrides the primary purpose of the chart, you’re more likely to create frustration than delight.

To me, the primary function of a dashboard is to be able to read the data, not to be excited by its visual form. The user can and should be excited by the content, because it gives them valuable information in a digestible form that they might not get another way. But the dashboard itself does not need to be exciting, and certainly not — I say this with a shudder — “sexy.” It just needs to help a user complete their task, as seamlessly and pleasantly as possible, and preferably without any inconvenient obstacles, frustrations, or other unnecessary arousal. Making a chart harder to read actually works against that aim.

I do not see the role of a data vis designer as that of a gatekeeper, separating good charts from bad, and telling my clients or users what they should want. My job is not to tell people to eat their broccoli and like it: it is to ensure that the visualizations I build do the best possible job of helping my users succeed, and to deploy every skill in my toolbox to support their needs.

Let’s take a look at some of the pitfalls and stumbling blocks that this particular chart puts in the user’s way, as soon as they try to engage with the chart and actually read the data that it contains.

Uncertainty about scaling. Should I read values as area, or as radius? How can I tell? Florence Nightingale’s original rose diagram used area to scale the chart elements, but people are terrible at estimating areas, particularly for distorted or irregular shapes. As I mentioned before, this chart actually appears to be a radial stacked bar, which would use a length encoding that scales radially out from the center. Without getting into the code for scaling the data, it’s hard to tell what it’s doing. Most people will try to use radius as a proxy for area when they estimate values anyway, so a radial version would probably be the better choice, but the bottom line is that the user simply can’t tell without more information. A good legend and clear chart labels are critical here.

Comparing proportions. It is incredibly hard to compare irregular areas that have very different sizes or shapes. Consider the difference in how you perceive the relationships between the blue and green areas when you view them as radial stacked segments compared to slices in a pie chart (a visualization that is itself notoriously bad for comparing proportions). 

Radial distortion. The hardest thing about radial/polar graphs is that the circumference of a circle gets bigger as you go out, and it gets bigger faster than the radius. No matter how you encode the information (length or area), people will tend to misjudge the importance of some arcs — it’s just a question of which direction the distortion goes. Length encodings will unduly emphasize the outer rings, where the area grows faster than the data because the arc width is larger. Area encodings will emphasize the inner rings, where the radial distance increases quickly because the arc width is so small. Both scaling methods create distortions, and you can see how much this affects your perception of the chart by comparing the same data side by side (the gridline values are meaningless here, and only serve to guide your eye). I don’t believe that we do our users any favors when we grab their attention with a flashy chart that also makes it harder for them to do their job. As a user of your dashboard, it is my job to read and digest a lot of information as quickly as possible, and to use it to make correct and informed decisions. In this context, I care most about speed, accuracy, and precision. A visualization that causes me to trip over visual artifacts or easily misread the numbers will quickly transform my emotion of delight at the novelty of the chart into one that all designers should work to avoid: frustration. 

In a more casual environment where there is less emphasis on accuracy and the ability to ingest data quickly,¹ the area-scaled rose diagram might actually be more successful. If the goal is a quick general overview that minimizes small differences (supported by the exaggerated sizes of the area mapping), grabs the user’s attention, and provides an interesting visual display rather than ensuring legibility of the data, then the rose diagram could be a reasonable choice. The original rose diagram is famous for having done just that, and it’s not an exaggeration to say that it helped to change medical history — for the better. There is also a clear rhetorical bent to this visualization choice, and someone who wants an undistorted view of the data is well advised to beware.

Let’s say that you are determined to go ahead with the rose diagram. For your context, the chart’s novelty is worth the price that you pay for a less accurate visual form, and you (or your client) really, really believe that this exciting new chart does exactly what your user needs and wants. Are there things that we can do to make this chart better? Absolutely.

Minimize distortions where you can. Adding a white center circle helps to reduce radial distortions by avoiding those tricky center wedges where the difference in scaling is largest. The distortions are still there, but at least they are smaller. Note that the empty center pushes me more toward interpreting the chart as a radial stacked bar, though, which may or may not be correct.

Provide cues to tell the user how to read the chart. The biggest challenge with a novel visualization is that people simply don’t know how to read it. For some people, new things are exciting. For others, they are disconcerting, overwhelming, or even scary. Depending on the analytical skills of your user group, an unfamiliar visualization might actually scare people off rather than pull them in. Show these users how to read your chart. Even something as simple as adding an axis can help.

Reinforce the visual encoding with values whenever possible. Most of the problems with this chart come from the user trying to estimate values. If you remove the need to “eyeball it,” you improve the accessibility of data in the chart. The user’s overall impression of the data might still be somewhat distorted by the chart, but the numbers are right there to fix it and support understanding. 

Include interactions or annotations that clarify the visual form and reduce common confusion. Let’s face it: you know that this isn’t the best chart for its analytical purpose. Once you’ve acknowledged that and understood its limitations, you can actively counteract them with thoughtful design. Create affordances (such as interactions) that provide additional support for user tasks whenever possible, and make sure to include clear signifiers to indicate that an action is available as well. The designer who requested my initial review did an excellent job of adding labels and interactions to support interpretation of an otherwise difficult chart, which made the design much more usable than if they had just relied on the chart alone.

Use styles to clarify the chart. Going with a less accurate visualization(voluntarily or otherwise) doesn’t mean that you simply throw in the towel and walk away. As designers, this just means that you have more work to do. There are several things you can do to make a weak visualization more readable. Even small decisions like adding lines between the chart wedges can strengthen groupings and help to clarify how the chart should be read. Dust off your UI skills and play around a bit: there’s probably something you can do to make this chart work better.

Explore alternate visual forms. Once you’ve decided to prioritize novelty, next comes the due diligence. Are there alternate encodings that would keep the novelty, but improve the visualization and reduce the conflicts between function and form? Here’s where you get to be creative: put on your design/data vis hat and get to work riffing off the basic structure of the chart to find other solutions that might fit the bill. 

To me, these last two options are where the real fun and creative opportunities lie for data visualization designers. Shaking things up is not about simply choosing a chart that no one has seen before, but in thoughtfully optimizing that chart for the task it is meant to serve.

Incidentally: the team working on this dashboard did decide to use the requested chart in their initial implementation, but during usability testing, it emerged that users were confused and could not read the data (even with detailed labels to support the task). We’ll take a look at some of the alternatives in more detail a future installment, to illustrate how different chart options support users in different ways, and how we can choose between different chart types by focusing on the user task. In the meantime, you might also want to look at this article on balancing client expectations by Benjamin Xiao. 

[1] Note also that this context means that a user is less likely to pause and check the values listed in the chart, and so any distortions added by the graphic are more likely to escape notice and may go uncorrected.

The post Why Can’t We Have More Fun? appeared first on Nightingale.

But how does this actually work?

What is it about seeing numbers that nudges people to action? What separates an admirable, “actionable” dashboard from all the B.I. “data vomit?”

To understand effective, motivational data design, you need to understand the psychology of feedback. So let’s look at a few examples of (quantitative) feedback in information design.

• Tufte v.s. Robinhood. Twovery different charts demonstrate the opposing forces of feedback.
• Indiegogo and Fundraising Progress. Is positive or negative feedback better? Depends on the audience.
• Dynamic Speedometers. How just two numbers create contrast (and safe drivers).
• Atom’s Meditation Forrest. Why counting things feels good.
• Withings’ Weight Graph. Balancing contrast and commitment for difficult health behavior interventions.
• Benchmarking Worklytics. Why do business people love benchmarks?

Tufte, Robinhood, and the fundamental forces of feedback

Let’s start with two examples of quantitative feedback: 1) Robinhood’s investment portfolio chart, and 2) Tufte’s patient status chart.

On the left: A line graph from the Robinhood app. As a testament to my investing savvy, you can see that I’ve grown my portfolio 8.75 percent (to a whopping $25). As charts go, this one’s a dumpster fire and is Fox-News-level manipulative. But it’s quite encouraging!

On the right: A segment of Tufte and Powsner’s “Graphical Summary of Patient Status,” showing that a patient’s blood glucose is elevated above the expected range. Though Tufte now discourages this design (he recommends sparklines), it’s a powerful example of critical feedback.

These charts have more in common than you might expect. They both forgo y-axes. They’re both data-ink efficient. They both give quantitative feedback.

Where they differ: Robinhood’s chart uses feedback to create commitment and motivate users to carry on. Tufte and Powsner’s chart uses feedback to offer contrast, enabling users to adapt and change course.

Robinhood → Commitment

Robinhood’s chart demonstrates how feedback influences our commitment to a goal (e.g., day-trading Gamestonks and Dogecoin until you’re super rich).

Efficacy / Expectancy

For new Robinhood users, their graphs shout, “OMG, you’re great at this!”

For example, by blurring the lines between users’ deposits and their investment returns and starting the plot from y=0, users are never more than a few taps from a bright-green graph of their portfolio, showing a sharp, satisfying uptick. Rosy feedback like this builds users’ self-confidence (efficacy) and encourages higher expectations for future returns (expectancy), thereby increasing their commitment to continued trading.

Feedback’s effect on efficacy applies outside of Robinhood. Positive feedback increases goal pursuit for students (src) and logistics employees (src); negative feedback, when it erodes confidence, can knock people completely off the wagon (src); it might even explain why some progress bars feel more satisfying than others (src).

Reinforcement

While Robinhood’s overall impression is rosy, the day-to-day experience feels more like a rollercoaster. The first thing users see is their portfolio’s performance today. The chart itself is tall, with a zoomed-in Y-axis to maximize the distance between the plot’s min and max. When markets are open, it updates in real-time (to mesmerizing effect). And, regardless of how much your portfolio is up or down, the whole app is either bright green or bright red based on the direction of change.

A positive spin on this: it makes day-trading more visceral, fun, and emotional! It encourages trading the same way a “runner’s high” encourages marathon training — it just feels good! Slightly darker: it’s using the random walk of the stock market as variable reinforcement, hijacking user’s anticipatory responses, and gently nudging them toward addiction (like a slot machine).

While Robinhood pushes it to a predatory extreme, the principle remains: when feedback itself is rewarding, humans learn to associate those positive experiences with pursuing the goal, therefore reinforcing motivation toward the goal (src).

Expectancy + Reinforcement → Commitment

By creating an early impression of confidence and progress, then drawing users into a realtime rollercoaster, Robinhood’s charts use feedback to increase users’ commitment to day trading.

Tufte → Contrast

Powsner and Tufte’s “Graphical Summary of Patient Status” demonstrates feedback as a source of contrast and a signal to change course.

Discrepancy

The chart above says, “Uh oh, the patient’s blood glucose is higher than it should be.” It does this by comparing two values: 1) the dots are the patient’s blood glucose measurements, and 2) the series of vertical lines are the target range for those measurements. Ideally the dots fall inside the lines, but for this patient, they’re just above. Separately, neither the dots nor the target range provide much useful information. What matters is the contrast (discrepancy) between the current and goal states.

This contrast is what makes a chart “actionable.” Specifically, the size of the discrepancy supports one of two possible actions:

1. If the discrepancy is small, things are good, so the implied action is, “Keep doing what you’re doing.”
2. If the discrepancy is large, things are bad, so the implied action is, “Do something different.”

Charts can’t tell you what to do next. That’s not their job. What they can tell you is when action is required (and how urgently).

Contrastive feedback highlights the gap between current and goal states. To the extent that you’re committed to achieving the goal, you respond by adapting your approach toward closing the gap (src, src, src, src, src).

Attention

The blood-glucose chart is one of many. A holistic view of a patient requires considering many similar indicators in parallel. This highlights another important aspect of feedback: no metric stands alone. So, in a sea of competing goals, which ones need your attention right now?

Contrast plays a role here as well. Assuming equally important metrics, the metrics with the largest discrepancies have the most potential for improvement, and are likely worth prioritizing.

Powsner and Tufte’s design amplify this in two ways: 1) the individual charts convey contrast at a glance, making it quick to determine if additional attention is required, and 2) each graph’s y-axis is scaled so that the normal range is a constant height, making the magnitude of discrepancy comparable between the charts, so the (globally) extreme values will look the most extreme on the page.

Contrast → Change

By comparing metrics’ current states and target states, Tufte and Powsner’s charts use contrastive feedback to direct physicians’ gaze toward areas that most need their attention. This contrast creates change by alerting physicians (and patients) to the need for action, enabling them to close the gaps.

Indiegogo and Fundraising Progress

Is positive or negative feedback better? Yes.

The tricky part: “commitment” and “contrast” are often at odds. Feedback that improves commitment can relieve the tension created by contrast, whereas feedback that highlights contrast can damage our commitment.

Balancing commitment and contrast matters when choosing between positive and negative feedback. For example:

• If you’re teaching a child to play piano, you might lean toward positive feedback. Encouraging and rewarding their successes builds their commitment, whereas focusing on mistakes may cause them to give up before they have a chance to improve (children, dolphins and husbands have this in common).
• However, if your student already plays for the philharmonic, negative feedback might be more effective. If they’re already committed and confident in the instrument, highlighting their mistakes surfaces the gap between their current playing and latent virtuosity, helping them improve.

Let’s look at another example. Consider the following common wisdom about fundraising…

Fundraising and the “Green Bar Effect”

If you’ve ever considered crowdfunding, you might know about the “green bar effect.” The non-profit accelerator Fast Forward offers the following advice for fundraisers:

“Set an attainable goal: people want to fund a project that has made significant progress toward its goal. If you set your initial goal too high and haven’t fundraised enough, strangers are less likely to donate (this is the “green bar effect” or the “bandwagon effect”: a progress bar showing 40 percent project funding versus 15 percent is more successful).” (src)

Koo and Fishbach tested the green bar effect in a related experiment. They found that when sending fundraising letters to low-commitment donors, they could increase donations by emphasizing how much money had already been raised (src). Because other people have put up money, it signals that the goal must be important. This perception of importance then, increases donors’ commitment, leading to more donations.

But, what works for low-commitment donors has the opposite effect with high-commitment donors. The most effective letter to the latter cohort highlights how far they still need to go.

The folks at Fast Forward address this indirectly with another tip:

“Pro-tips: aim to have about ⅓ of your goal locked down via commitments from your network before you even open up the campaign. Planning prior to campaign launch is crucial — give yourself at least a month of prep time.”

That is, by getting early donations from your network — presumably from those who are most committed to you and your goal — you’re solving the “green bar effect” problem, but you’re also asking these early donors at exactly the right time to maximize their contributions, when the discrepancy between the current and goal state is largest.

Fundraising Thermometers

The fundraising thermometer is one of the more common visualizations you’ll see implemented with construction paper. But don’t let their humble execution fool you! There are two aspects of this that make it powerful for commitment building:

1. The metaphor allows small, incremental notches toward a larger goal. When tracking progress toward a larger goal, these intermediate goals give users a focal point that’s closer and easier to achieve. This proximity boosts confidence. Then, as users accomplish more of these smaller goals over time, they act as a record of past commitment, further propelling their efforts forward.
2. The size of these is also an influential factor. By giving this such a large physical presence, it reinforces the importance of the fundraiser to the organization, further building commitment.

Dynamic Speedometers

Feedback can be surprisingly simple and still effective. Not all instances of feedback need to balance between commitment and contrast.

My favorite example of contrastive feedback is the dynamic radar speed sign. Using just two numbers (hardly a visualization), these clever signs significantly improve safe driving. You can see an example above. The number on top shows the current speed limit. The number at the bottom shows the passing driver’s speed (as determined by a radar gun attached to the sign).

Notably, neither of these signs offer drivers new information. All cars have speedometers built into their dashboards. All (most?) roads have speed limit signs. But, by putting the two numbers side by side, it invites drivers to compare their speed with the speed limit. It creates contrast, encouraging drivers to slow down.

According to Thomas Goetz’s reporting in Wired, officials in Garden Grove, California, used these signs to great success in reducing speeds near school zones. After failed attempts at more heavy-handed approaches (e.g., writing lots of tickets), city officials deployed these driver feedback signs across five different school zones and saw speeds drop 14 percent in nearby areas. According to Goetz, dynamic speedometers have similar effects elsewhere, showing 10 percent speed reductions overall.

A few years later, Stanford researchers Kumar and Kim demonstrated similar results by inverting the roadside dynamic speedometer: they moved the speed limit sign inside the car. Their prototype was a small display, mounted near the car’s dashboard, showing the speed limit for the car’s current location right beside the car’s speedometer. In their (simulator) experiments, their test subjects drove 13 mph slower.

Atom Trees and why counting is encouraging

Data visualizations don’t need to be actionable to be influential. Similarly, feedback doesn’t need to be contrastive to encourage positive change. Instead, you can help users strengthen commitment toward a goal by visualizing their past progress.

The meditation app Atom offers a recent example of this. In addition to offering guided meditations (ala Headspace), Atom offers two simple ways for users to visualize their progress: a weekly timeline with a simple checkbox for days you’ve meditated (left) and a small, growing forest that adds a new tree for every completed meditation session (above).

Above you can see my tiny grove expanding to nine trees for the nine sessions I’ve meditated. Whereas the timeline shows the gaps in my meditation practice, the trees are purely a count of my successes.

A number of studies have explored metaphorical visualizations for progress tracking (e.g., with fish, gardens, monsters) but there are even more examples where simply counting something is quite encouraging (consider Fitbits and other pedometers, Wii Fit stamps, Snapchat streaks, bullet journals).

• One possible explanation: people like to see themselves as consistent (src), so seeing our past actions might remind us of our past goals which we’d like to carry forward.
• Another reason: it’s fun. It feels good. Atom’s little trees are pleasant and seeing a new one pop up is satisfying. It works the same way chocolate motivates you to open the next door on an Advent calendar. When the feedback itself is rewarding, you associate that positive affect with pursuing the goal.

Feedback can reinforce commitment by memorializing previous activity and by offering a generally pleasant experience.

A Forgiving Weight Graph

Contrast creates change. Highlighting the gap between the current state and a goal state creates awareness and tension that you can then work to resolve. But if closing the gap feels unattainable, people can lose their commitment and give up entirely. This dynamic is critical in the context of weight loss.

Maintaining Commitment

Weight graphs are common in personal fitness apps. They’re typically a line graph, plotting the measurements of users’ weigh ins, helping them visualize progress toward a long-term weight goal. Above you can see three different examples from Noom, Withings, and Google Fit.

Despite the ubiquity of this feature, weight tracking is somewhat controversial. Even though it’s associated with improved outcomes (src, src), many worry that simply stepping on the scale might discourage people enough to give up entirely.

This is called the “what the hell” effect. When you’re trying to do something new and difficult, all it takes is a few examples of discouraging feedback for us to lose our confidence and give up on the journey all together. (This is particularly challenging with weight loss. Our weight varies naturally throughout the day, but this natural variation can look a lot like failure. You haven’t gained weight, you might just need to poop!)

This highlights the tension between commitment and contrast-oriented feedback. The contrast between a user’s current weight and target weight makes weight tracking a useful intervention. It helps build intuition about the relationship between actions (e.g., what we’re eating and doing) and outcomes (e.g., changes in weight). But in the context of weight loss, commitment can be especially fragile.

Presenting contrast is straightforward. The graphs present users’ weight measurements relative to their target weight (e.g., on the Withings chart, middle, you’ll notice a horizontal goal line).

They also preserve commitment in a few subtle ways:

1. To build in forgiveness, the graph only shows weigh-in results as small points on a desaturated line. They give the most visual emphasis to a moving average line, designed to rise and fall more slowly, reflecting a more stoic view of users’ weight.
2. The graphs all have somewhat exaggerated y-axes for the given values. This further dampens the vertical distance a user would see for any given weigh in.
3. At least in Noom, after every weigh in, the app offers positive, process-oriented text feedback, to help users maintain a sense of efficacy and commitment.

Based on user comments, the design seems to be appreciated:

“Another reason that I weigh in every day, quite honestly, I love the way the daily weight line has been changed. I love how it irons out the outliers of the highs and lows and helps you feel not quite so bad about a day where you weighed a few pounds more than before. I really like that.”

Contrastive Feedback at Work

Benchmarks create contrast

Worklytics helps “people analytics” teams understand what’s happening within their firms by quantifying traditionally hard-to-measure organizational dynamics (e.g., collaboration, communication, employee experience, etc).

For example, following the pandemic, every big company in the world wanted to know: “Are we doing this ‘remote’ thing right?!” Worklytics’ “Remote Work Analysis” answers that by giving clients visibility into how surprisingly-consequential behaviors like “emails after 6 pm” changed throughout the pandemic (and how they can affect employees’ work/life balance).

(Full disclosure: I work with Worklytics, designing reports like this.)

Previous versions of this slide only included the blue trendline, but not the yellow benchmark range. As you might expect for a B2B data product, when we showed charts like this to clients, the first thing they’d ask — inevitably — was, “Okay, so it’s up, but is that normal?!” Before including the benchmarks, Worklytics’ co-founder, Phil Arkcoll, spent half of every client presentation answering some variation of that same question for every slide.

Is this because business people love benchmarks? Yes. 100%.

What drives this affection? Benchmarks’ popularity stems from contrast. They give users a reference point for comparison, turning a lonely metric into contrastive feedback, therefore making it actionable.

Specifically, benchmarks trigger one of three responses:

1. If the metric is outside (worse than) the target range, we’re doing poorly (e.g., worse than the majority of peer firms). The gap between the trendline and the benchmark range highlights: a) there’s room for improvement, and b) how much you can improve. Awareness of this gap creates tension, which leads to conversation, which leads to change.
2. If the metric is within the target range, you’re doing okay and this is one less thing you need to worry about. (This might seem trivial, but it’s actually huge. More on this below.)
3. If the metric is beyond (better than) the target range, not only do you not need to worry, you have something to brag about! (When “actionable” ⇒ “getting users promoted” you’re on the right track!)

Feedback Determines Focus

Benchmarks help us prioritize.

When presenting performance feedback, it’s easy to forget that you’re not just answering the obvious question (“Are we good/okay/bad?”). There’s also an implied question for every metric: “Do I need to worry about this more or less than the 20 other KPIs I’m tracking?!”

This is especially true for executive teams. When even attention is a scarce resource, it’s actually quite useful for a graph to say, “Nothing to see here!” It lets viewers quickly (and safely) put the question to rest and move on to the next thing (i.e., the other areas with larger opportunities for improvement). Research suggests this phenomenon applies to non-execs as well: When you hear you’re doing well at one goal, you tend to shift your focus to other goals.

This is another area where Worklytics’ benchmarks are effective. Their reports, as you can see above, are comprehensive. Similar to Tufte and Powsner’s “Patient Status” viz, Worklytics’ reports use a common visual language to denote: a) the measurements (blue-ish lines), and b) the benchmarks (yellow areas). So on every slide, users can answer that first implied question at a glance (i.e., “Do I even need to worry about this?”), then quickly move on and identify the areas that need their attention the most.

Since adding benchmarks to their WFH reports, not only has Phil been saved from endlessly answering “is that good or bad?,” clients have also remarked on how the additional contrast helps exec teams make faster, more confident decisions about where to focus their attention.

All organizations do silly things. The bigger they are, the sillier they can get. Given an infinitely-long list of silly things to improve, aligning leaderships’ focus on the right problems is a crucial first step toward positive change.

Recap

“What you measure you improve” is backed by the psychology of feedback. Understanding this can make us more persuasive data designers.

Effective dashboards provide effective feedback and rely on two dynamics: 1) they increase our commitment to a goal, ensuring we’ll stick with it, and 2) they draw contrast between our current state and goal state, helping us see how far we need to go and our biggest opportunities for improvement.

When designing dashboards (or other quantitative feedback systems), there are a lot of different tactics for influencing commitment and contrast:

• You can build commitment by highlighting past progress (e.g., Atom, fundraising progress bars), making the feedback experience itself rewarding or fascinating (e.g., Atom, Robinhood), downplaying failures (e.g., weight graphs), and shrinking the perceived distance to a larger goal (e.g., fundraising thermometers).
• You can draw contrast with as little as two numbers (e.g., speed limit signs), long distance goals (e.g., weight graphs, fundraising progress bars), social comparisons, and various benchmarks (e.g., Tufte and Powsner, Worklytics).

This article was originally published on 3iap.com.

The post Dashboard Psychology: Effective Feedback in Data Design appeared first on Nightingale.

Grafana’s history

Grafana was first released in 2014 by Torkel Ödegaard as a “spinoff” of a project at Orbitz. It targeted time-series databases such as InfluxDB, OpenTSDB, and Prometheus, but evolved to support relational databases such as Oracle, MySQL, PostgreSQL, and Microsoft SQL Server. As a visualization tool, Grafana is a popular component in monitoring stacks like Sensu, Icinga, Checkmark, Zabbix, Netdata, and PRTG.

Before you continue, it is important to highlight that I’m in no way trying to sell that Grafana is the best tool ever for data visualization, neither I have any personal gain in presenting the tool here. As a data visualization enthusiast, I think it is important to know tools that can help us to understand our data.

Currently, Grafana defines itself as a multi-platform, open-source analytics and interactive visualization web application. On the Grafana site, you can find their mission:

We believe in breaking traditional data boundaries, making metric visualization tools that are more accessible and easy to use across the entire organization. And most importantly, open source.

How to start with Grafana

The first thing to know about Grafana is that there are three ways to use it:

• Using the Grafana Cloud will give you access to a free version of Grafana, with some limitations related to the number of dashboards and users that can create/update dashboards;
• Installing on-premise will remove the limitations above, but you’ll need to find a way to publish on the web;
• If you want it for your company, get the Enterprise version.

In this article, you will use Grafana Cloud. Please create your account (at the time of this writing, they are promoting 10 days free of the PRO version with new registrations) to follow along. After you have logged in, you should see the following screen:

Then, click on the Login button on the Grafana box, and you’ll see the screen below:

This is the home page for your Grafana cloud. It can be customized with panels and visualizations, but you will address that later. Throughout the next sections, you will learn how to build a dashboard, step-by-step.

Creating a data source

Any dashboard needs data to show, and in Grafana you need to create a data source before you start. It is simple.

Click on the Configuration icon and then select Data Sources. You’ll see the screen below.

As you can see, there are some pre-defined data sources available. These data sources allow you to create some observability dashboards about your Grafana cloud instance. You can see the usage logs, traces, alerts, and more. But remember, your objective is to create other types of dashboards, so click on the Add Data Source button to configure your new data source.

In the list, you can see many of the available types of data sources. Grafana uses a plugin system, which allows you to add other types, as you see fit. For your dashboard, select the PostgreSQL data source.

The instructions to follow are simple, you just need your database connection information.

Just fill the fields with your database information or the information above and click on Save and Test. You should see something like this:

Now, click on Back, and create your first dashboard.

Anatomy of a Grafana dashboard

If you have reached this point of the article, it means you are ready to created dashboards in Grafana. First, it is important to understand a few concepts about dashboards in Grafana:

• Dashboards are made of two other smaller units: Panels and Rows.
• Dashboards may have links between them.
• Dashboards may have variables that can act as filters on your visualizations.
• Dashboards may be imported/exported as JSON.

Panels are our charts, or visualizations if you prefer. Rows are a way to organize your panels inside the dashboard. Rows can help you avoid a lot of duplicated effort. Your recently-created dashboard should look like the one below.

Before you continue, save your dashboard, so you can give it a name. Click on the Save button and fill out the name of the dashboard, as in the image below.

Adding your first Panel

Now, click on Add Panel and get started.

When you select Add Panel, a new Panel will be created and you will enter the Panel Edit page, which will look like the image below:

Looking at this screen, you should be able to identify three areas of interest:

• The drawing area (1)
• The query area (2)
• The configuration area (3)

The drawing area is where the results will appear as you make changes in the two other areas. In the drawing area, you will also find the time filter and the button to refresh results. The query area is where you select the data source to work, write your query, and apply transformations when needed. The configuration area allows you to select the type of chart and all the layout configurations.

Creating your chart

Note that when you start a new panel, it defaults to a sample line chart. Line charts are the standard chart type in Grafana. If you want to change the chart type, you need to go to the configuration area and change that on the Visualization option.

For this tutorial, you will select the Bar Gauge chart. Observe that as you select it, the other options in the configuration area change.

Your next step is to change the query. The first change is on the data source. Right now, it should be showing something like ‘ — Grafana — .’ Change that to your recently created data source, called ‘PostgreSQL.’ This will change the text box right below.

Click on the ‘Edit SQL’ button and this will convert into a text box.

Now, you can write your own query. It is important to understand that you can write two types of queries: Time Series or Table.

Time Series queries need to have a field called time, which is a timestamp. If you click on Show Help, you can learn more about how to use a Time Series query. Here is a simple query to demonstrate:

Your query area would look like this:

Your query area would look like this:

The query provoked an immediate change in the drawing area, as you can see below.

Now, to finalize, you need to make a few changes in the configuration area.

Change the chart to show one element for each answer

Change the chart by selecting the Display option.

There, you will change from Calculate to All values. The Calculate option was designed to display a single value, which is the result of a computation with all rows returned by the query.

This change will provoke an alteration in the drawing area.

Change the label of each element

As you can observe in the drawing area, you have now multiple bars, which is a good indication that you are on the right track. But, all the bars have the same label as ‘value.’ To fix that, you need to go to the Field Panel.

The standard options will already be opened, so you can make the following fixes:

• set the Min field to 0;
• set the Display name field to $__cell_0. This is a Grafana variable that indicates that you are going to get the value from the first column of the query, in this case, the column answer.

Again, looking at the drawing area, you can see what has changed. Woohoo! Your chart is almost perfect!

Setting the chart title

It is easy to title the chart, just click on the Settings and fill the Panel Title field.

Change the title to Age Range.

Note the change in the drawing area below.

Changing the style of the chart

Now, you can spend a little more time in the Display option on the Panel tab, making a few adjustments to improve the layout.

The main change here is to remove that gray area reserved for the bars. To simplify your visualization, remove that point of focus. To do that you just need to click on Show unfilled area. The result will be as follows:

Now, to finalize, click on Apply (at the right upper corner) and … voilá! Here is your first chart on your dashboard!

Now, you can edit the chart, add other charts, and move the charts around for better positioning. In my next article, I will cover chart configuration and variables.

Conclusion

In this article, you have learned how to create your first dashboard in Grafana and now you can add it to your data visualization toolbox. You have also explored the potential of Grafana beyond Observability dashboards. This link will lead you to the complete version of this dashboard for you to view. If you are interested in creating your own Grafana account and experimenting for yourself, this link will lead you to my Gitlab repository with the dashboard to be imported.

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