A 12-Point Checklist for Public and Open Data Sites (with Examples)

Let the data run free! Government organizations, academic institutions, non-profits, and even passionate sports fans are gathering and sharing valuable data sets with the public. The topics are wide ranging, from climate change to health to inequality to happiness. It is a powerful way to support a cause and encourage data-driven analysis.

These open data sets are set loose on a website in hopes that interested visitors will come flocking. How do you make that site as effective as possible? Simply posting the data in a searchable format isn’t enough. To achieve impact, you need to make it easy to understand, manipulate, and explore.

The following checklist is a collection of best practices and reminders for your open data project.

1. State Your Purpose

To start, you need to address the question: WHY this data? And WHAT can a viewer gain from using the data? The following examples feature prominent statements about the purpose.

https://www.oecdbetterlifeindex.org/

https://blackwealthdata.org/

https://champshealth.org/

2. Segment by Audience or Topic

There are always many ways that someone could analyze and explore your data. Do them the favor of explaining HOW the data can be used. The best sites provide separate sections based on different users of the data and/or topic areas.

https://www.earthdata.nasa.gov/

https://dataunodc.un.org/

https://blackwealthdata.org/

3. Provide example insights

Naturally, you would like your visitors to find their own insights. However, they will benefit with a gentle nudge toward the types of insights that are available in your data. In the following examples, a few key insights are featured as a teaser to dive deeper into the data.

https://www.boxofficemojo.com/

https://www.nashvillehealth.org/survey/data/

https://blackwealthdata.org/

4. Let users find their own data story

Too many open data sites simply provide downloadable access to the data. This is a missed opportunity, particularly for visitors who may not have advanced analytical skills. Interactive, exploratory visualizations give your visitors a playground to find their own insights in the data. This is where the leading data storytelling platform can lend a helping hand.

https://seer.cancer.gov/statistics-network/explorer/application.html

https://www.oecdbetterlifeindex.org/

https://www.nashvillehealth.org/survey/data/

5. Encourage sharing of insights

If your site enables your visitors to find insights in the data, the natural next step is to let them share what they have found. Ideally, you’ll want the ability to capture specific visuals and share via social media.

https://www.oecdbetterlifeindex.org/

https://news.crunchbase.com/web3-startups-investors/

6. Use simple, intuitive visualizations

Keep in mind that the visitors to your data site are just coming up to speed on your data. Complex visualizations and sophisticated analysis tools are likely to overwhelm them, and cause them to bounce. You want to lower the cognitive load by finding simple and familiar ways to present the data.

https://www.nashvillehealth.org/survey/data/

https://www.movebank.org/cms/movebank-main

https://www.oecdbetterlifeindex.org

7. Include real-life examples

By its nature, data is an abstraction from reality. It summarizes and aggregates many individual data points to find trends and insights. However, this abstraction can separate your visitors from the specific things that are represented in the data. Take a moment on your site to reconnect your visitors with the actual subjects of the data.

https://data.unicef.org/

https://dmp.unodc.org/

8. Explain your metrics

For many public data sites that are focused on a particular topic, there will be a few key measures of performance. You want to ensure your visitors have a full understanding of these metrics so they can interpret the results accurately. We found many sites that do this well; others fail to provide the labeling or context to clarify what the data means.

Not so good: https://climate.esa.int/en/odp/#/dashboard

9. Explain why the data is credible

There is a lot of data out there. Why should your visitor trust what they are seeing? Take the time to explain the diligent work and research that went into gathering your data.

https://blackwealthdata.org/

10. Make the raw data accessibility for advanced users

For the novice data users, interactive visualizations are a great entry point into your data. The advanced users will have their own ideas about how they want to manipulate the data. You’ll want to give these users the ability to search your catalogue of data and download the raw data files.

https://search.earthdata.nasa.gov/

11. Provide resources to learn more

Data is great — but it is better with context. You want to include resources that give interested visitors the chance to learn more with relate research and other content.

https://www.earthdata.nasa.gov/

https://blackwealthdata.org/

12. Don’t forget the outreach and marketing

You’ve made an amazing public data website. The job’s not done. You need to make sure people know it exists. There are a lot of options: search advertising, organic search, social media, and lists of open data sources. A great starting point is to reach out to sites that relate to your topic area and make them aware of your data as a valuable resource.

Deliver more “Aha!” moments in every data presentation

The Easy Button for Dashboard Design

If you’re a Tableau fan, you should be following Lee Feinberg of DecisionViz. He’s an expert dashboard designer, adjunct professor at NYU, and hosts a deep-dive podcast with industry experts (and me).

He’s also the kind of pragmatic data practitioner that I really appreciate.

Lee recently shared a spot-on list of The 10 Tableau Dashboard Fixes You Need To Be An Analytics Hero.

Lee has inadvertently gotten to the crux of why we created Juicebox: It should be easy to create data communications that are audience-ready right out of the box.

More time conveying your message; less time fiddling layouts, charts, color, and labels.

Of course this isn’t a problem created by Tableau. Long before Christian Chabot, Chris Stolte, and Pat Hanrahan made Tableau the defacto “IBM” it is today, we spent our time fixing Excel dashboards and PowerPoint slides to make them readable. The source of the problem is un-thoughtful design options, a belief that more flexibility is always better, and little consideration of the end-user audience. As a result, you get more design decisions and more “opportunities” to fix them.

Lee offers 10 checks on your dashboard design using a 1-5 performance scale, and you shouldn’t share your dashboard until you’ve gotten 40 points (out of 50).

Here’s the good news! We can get you 40 points right out of the (Juice)box. Let me show you how by reviewing each of Lee’s criteria.

“1. The information on each dashboard ties to one main idea, and the audience should be able to read the dashboard in about 30 seconds.”

Instant Juicebox Score: 2

Focusing on one main idea is ultimate up to the author.

However, we make sure to give you a nudge in this direction: When creating a report or dashboard in Juicebox, we ask you to give it a name and description. This information shows up in an automatically-generated header.

“2. Each chart uses the least amount of space needed to see the data legibly and most importantly, to communicate the insight / intended message.”

Instant Juicebox Score: 3

Visualizations in Juicebox are lovingly-designed to emphasize the data with minimal distraction.

To do this, we have made our charts automatically responsive to work beautifully on mobile devices.

We also make visual space for the text descriptions and insights that will help your readers know what the data means.

“3. Remove visual elements that don’t add clarity, such as : too many digits or decimal places, gridlines, tick marks, axis labels, field and column labels. Less is more.”

Instant Juicebox Score: 5

Our design team has done the work to remove extraneous details. We’ve taken out extraneous ‘chartjunk’ to deliver data legibility.

We even make smart choices for number formats to ensure that large numbers are presented with the level of detail that will make it easy to read.

“4. No horizontal or vertical scrolling. Explore other chart types that do not scroll or enlarge the chart to minimize scrolling.”

Instant Juicebox Score: 5

Our charts automatically size to fit on screen. One of the best examples is our bar chart. When you have a long list of items to show, a small, scrollable version of the full bar chart is displayed on the left. Now you can see the shape of the values without sacrificing readability.

It takes some clever design engineering to elegantly handle data can be big or small. We’ve got it covered.

“5. Apply color to make information stand out, not to make charts pretty. Use a color only once and be consistent, i.e. blue has the same meaning on every dashboard.”

Instant Juicebox Score: 5

Color choices can be hard. What colors go together? How do I make color choices consistent?

Not in Juicebox.

We have an industry-leading approach to theming your dashboards and reports. You can instantly try out our pre-build color themes, or add your own. The colors will be applied consistently across everything you make.

“6. Rename or alias field names to be clear and simple, especially for Quick Tableau Calcs. Don’t accept the default name the database admin created.”

Instant Juicebox Score: 4

This is an important concept: you don’t want to expose your dashboard audience to the messy data field labels that come from your spreadsheet or database.

In Juicebox, we automatically rename field names, removing that junk and even adding plurals. We also make it quick and easy to update those labels throughout your dashboard.

“7. Avoid charts that look “cool,” e.g. treemaps, starbursts, Sankey, packed bubbles. They may be unfamiliar to your audience and can get in the way of seeing insights.”

Instant Juicebox Score: 5

A great point, and a lesson I learned long ago when I had fallen in love with treemaps. “If you are explaining, you are losing.”

Fewer choices is sometimes better. In Juicebox, we include the most common and useful charts — then we make it easy to connect those charts. The result: you can present complex data in an interactive way without having to resort to complex charts.

“8. Show brief ‘operating’ instructions, especially for action, highlight, parameter, and set filters. They may be unfamiliar with Tableau; to them it’s a website with charts.”

Instant Juicebox Score: 3

You may need to do most of this on your own. But you’ll get some built-in explanations when you use Juicebox.

For example, legends come standard and we automatically include instructions on how to interact with charts.

“9. When using a dimension on color or shape, make sure the dimension has at most seven elements, else the chart can be visually overwhelming and hard to interpret.”

Instant Juicebox Score: 5

We impose limits on how many parameters you can add into charts. This can seem draconian at time — but it is for everyone’s good.

Here’s the secret: when you can automatically link together different chart types, you can still do sophisticated things with data without having to show all the data once.

Simple parts, easily connected.

“10. Place filters, parameters, and legends that affect all charts next to or below the dashboard title. Else, place in the left column and/or within the related chart(s).”

Instant Juicebox Score: 5

As Lee points out: Context is everything. You can’t understand the numbers on a dashboard without explaining how the data has been filtered.

That’s why we created our “Sticky Bar”. When you navigate through a Juicebox dashboard, you’ll always be able to see how the values and charts are being filtered.

The 7 Stages of Data Projects

Why do data projects take so long? It’s exhausting — finding data, cleaning data, identifying problems in the data, creating presentations, hitting resistance...on and on.

I’ve seen the struggle up close for over 15 years. It is my belief that the challenges of analytics have less to do with technology limitations and more to do with people challenges. The barriers often relate to Psychology, Sociology, Anthropology, and Mindsets.

We will often have clients who are energized to get started, but then disappear for months as they struggle with their data problems. I see people bounce back and forth from optimism to pessimism.

With that in mind, I wanted to offer a framework for thinking about the journey that both people and organizations go through as they tackle data projects. The framework describes the sequence of behaviors and emotions that people express. Getting stuck in these stages helps to explain why data projects can take so long:

1. Skepticism. Like anything that is new, people will start by questioning whether it is worth their time and effort.

2. Irrational Exuberance. The pendulum swings and people get (over-)excited, about what they can do with data. Reality may not match their growing expectations.

3. Confusion. Then back to Earth. When it comes time to embark on an actual data project, the uncertain grows. Where do you even start?

4. Discovery of Purpose. Getting to this step requires finding a small piece of the data potential that can be bitten-off first.

5. Doubt. Now that you’re committed to a direction, the reality of your data comes into play. Will you be able to find value and insights?

6. Denial. Even after emerging from stage 5 with progress, now you face an audience that may not be ready to change. Their skepticism is now your blocker to progress.

7. Acceptance. Finally, the data project comes to fruition, perhaps at a smaller scope than was originally imagined. Time to find the next opportunity.

I made this infographic as a visual display of this framework:

Download the infographic as a PDF.

Why Self-Service Analytics Adoption Is Persistently Low? Hint: ⏱

Low user adoption for data solutions is the problem that won’t go away. It is the…

…sticky-wicket of Cricket

…‘Transformers’ of Sci-Fi movies

…barnacle of boats

…‘Two and a Half Men’ of TV

The data and analytics industry has struggled for decades to get more people in organizations to use the data. Re-labeling it “data democratization” didn’t fix it. The advent of visual analytics didn’t do it. Low adoption is the “last mile” problem that we’ve been talking about for 15 years. The checklist looks like this:

✅Invest in data tooling

✅Gather and consolidate data

✅Build models

😩 Use data for everyday decision-making

That’s why you see statistics like: 67% of workers have access to analytics tools. Only 26% of those people are using them.

In my experience, those 74% of non-adopters live in a world of limitations that is not fully appreciated. The non-adopters are not the data analysts who work with data as a core element of their role. They are the managers, consultants, marketers, salespeople, and front-line decision-makers. They already have a full-time job, and acting as a data analyst isn’t it. Working with data needs to fit into the cracks — not transform how they work. They have limited time and limited attention for data.

Meanwhile, the analytics vendors have been moving in a different direction. They are eager to add more features. And why not? Their users — the 26% of adopters — demand it. They want more integrations, more ML/AI, more ability to tweak and configure and manipulate across their tsunami of data.

Check out the update from Tableau. “It has a number of highlights that everyone is going to love.”

Everyone will love it if they are already on board. But this is what we hear when we talk to the 74% who haven’t adopted these increasingly complex analytical tools like this:

I don’t have time to learn a new tool

“This looks easy to use. Can you just do it for me?”

“I’d rather stick to things that I’m comfortable with, like Excel and PowerPoint”

I don’t have time to put together a great presentation

“I spend all my time gathering, cleaning data. Then I have to do the analysis.” 

“I don’t love my slides, but it take too much work and time to do a better job.”

I can’t get my audience to give the data much attention

“They don’t want to sit through a long presentation.”

“They don’t want to open my spreadsheet.”

Logi Analytics conducted a survey that hints at the gaps between the available tools and these time and attention limitations:

Are better tools the answer?

My friend Mike Kelly, CEO and founder of TeamOnUp, gives me a hard time because I like to say that the challenges of data are more about human issues than technology issues. Then he says: “If you believe that, why the heck are you selling a technology solution?”

Maaaaaybe he’s right. Maybe I’ve downplayed the importance of tools that recognize the real-world constraints of users.

We need ‘Analytics for the rest of us.’

And that’s what we set out to do with Juicebox. We wanted to make a data storytelling platform that my mom could use (she did for a non-profit), my 10-year-old could use (she did and blew her teacher’s mind), and a busy consultant could use to impress their clients.

If you are in that 74% who haven’t logged into that Cognos, Salesforce, or PowerBI account in a while, why not try something built for the busy non-analyst.

11 Data Presentation Tips and Resources to Deliver More Client Value

Whether you are a consultant, marketer, researcher, or financial analyst…a big part of your job is presenting data. It takes a special combination of skills to articulate your insights and support them with effectively visualized data. You need to be part salesperson, part data analyst, and part author.

We’ve collected 11 of the most useful tips and resources to help you improve how you present data.

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1. Visual Consistency

It can be awfully distracting for your audience to feel like your data presentation is a Frankenstein’s Monster of colors, fonts, and styles.

Many presentation tools are good at centrally managing the theme to ensure a consistent look. We recommend Beautiful.ai if you are slide-oriented or Juicebox if you are presenting more data and want interactivity.

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2. Duarte’s Data Story Tools

Duarte Design is one of the true leaders in designing impactful presentations. Their team has increasingly focused on data as an important part of the message. Check out their collection of data presentation tools to improve your next slide presentation.

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3. Simpler is better

One of the worst presentation challenges is having to explain how to interpret a chart. At that point, you are definitely off-message, and potentially losing your audience’s confidence.

We recommend sticking to chart types (bars, lines) that are familiar and easily interpreted. You can always break your story into smaller parts in order to cover complex content. Check out Chart Chooser for simple, beautifully-formatted Excel and PowerPoint charts.

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4. Set the context before diving into details

Before you dive into your data presentation, explain the problem or question you are addressing; and a brief overview of the data that is underpinning your analysis. Without setting these parameters, your audience is bound to be lost and confused.

Here are three kinds of context you’ll want to consider.

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5. Lea Pica’s Data Presentation Site

Lea is one of the leading thinkers and trainers on data presentations. She has musings on her blog and outstanding workshops such as “3 Keys Every Data Practitioner Needs to Confidently Present Insights and Inspire Action”

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6. Interaction builds trust

If you can present data in a way that responds to your audience’s questions in real-time, you’ll build trust in the content you are sharing.

Show that you can be nimble with your data while guiding your audience with the right metrics and visualizations that will deliver insights.

If you’re concerned that interaction will be distracting, stick to a more traditional static slides approach.

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7. Know your audience

Every data presentation needs to start by considering who you are looking to influence with your data. What are their priorities? What actions can they take?

For a deeper dive, check out our Data Personality Profile framework.

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8. Refine your data-rich slides

Chris Tauber’s Data for Execs Guide is a wonderful source for improving your data-rich slides. He offers numerous examples of before-and-after slide improvements with explanations for the changes he makes.

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9. Manage attention

Not all the data is equally important in order to make your point.

You want to highlight the key data points and deemphasize the rest. You can also use call-outs and arrows to make it clear what you want people to take away from your charts.

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10. Go beyond charts

A data presentation doesn’t have to be all data all the time.

Find the key messages and express them succinctly in text. Look for examples that underscore those messages. And incorporate images and other visuals that create emotional connections.

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11. Explore the “Extreme Presentation” method

Andrew Abela’s Extreme Presentation framework is thoughtful and comprehensive. He describes it as “a simple but effective design approach for creating presentations that are clear, convincing, visually captivating.”

10 Tips to Visualize Data Like a Pro

Have you nailed all the data visualization basics? Stuff like…

• ✅You know pie charts are bad except in certain specific use cases;

• ✅You can spot chartjunk from a mile away;

• ✅You confidently pick the right kind of chart based on what you want to emphasize in the data;

• ✅You use just the right amount of color to bring meaning, but not so much as to distract;

• ✅Labeling; ✅legends; ✅titles…

Those are important skills. But what does it take to get your visualizations to the next level? I want to share a collection of tips and tricks that differentiate a competently designed chart from a pro-level visualization.

1. De-emphasize to manage attention

Emphasizing text and chart elements is good. Better: Combine emphasis of the most important things with de-emphasizing elements that don’t require as much attention. In the examples below, emphasized content gets more visual attention because other items or text is semi-transparent and therefore de-emphasized.

Beautiful.ai

Juicebox

2. Points on a trend line

This one is a bit of a personal preference: I think it enhances a trend chart to explicitly show the individual points on the line. This has a couple of benefits: 1) It makes clear the frequency of the data points; 2) it is easier for users to select or interact with those points. The following examples have slightly different styles to displaying these points, achieving the same goal.

Juicebox

Ben Garratt, Dribbble

3. Label regions of visualizations

Many visualizations are displaying data positionally in 2-dimensional space. You can do your audience a favor by providing an explicit description of the meaning of different regions of that space. In this example, the reader can quickly determine the meaning of a point falling into one of the quadrants.

http://thedailyviz.com/2016/08/09/are-people-in-colder-countries-taller/

4. Use significant digit formatting on large numbers

When you are presenting data with large numbers, displaying these numbers can become cumbersome to the point of distraction. A better option is to shorten the number format by using a set number of significant digits. In Juicebox, we encourage using 3-significant digits so that

5. Bold colors with inverted text color

Readability in your data visualizations is critical. But shouldn’t preclude using bold, fully saturated colors as backgrounds. The answer is to flip the color of your text to maintain contrast in your designs. These examples show how inverted text colors can work alongside fun colors.

Juicebox

Juicebox

6. Elegant handling of many individual items

Most visualization types can easily handle 5 or 10 individual items. But what happens when your bar chart has 100 items? How do you show a map with 10,000 points? There are a few options depending on the scenario: 1) If there is a long tail of “smaller” values, they can be grouped in an “Other” category; 2) Use a hierarchy so the visualization only shows one level of detail at a time; 2) Create navigational mechanisms or pagination to avoid showing everything at once. Here is an example from Pro Publica:

https://projects.propublica.org/extinctions/

7. Few colors; good contrast

Beginning guides on data visualization focus a lot on the appropriate and restrained use of color. But there is less emphasis on contrast, color’s close cousin. Good use of contrast is necessary to make clear delineations between what is important or selected.

8. Horizontal labels

Labels are tricky little devils. Sometimes they can be too long or too small for easy readability. Expert data visualizers try to always display labels horizontally for easy reading and give them sufficient space so they don’t end up shrunk to small font sizes.

In the example on the left, both attempts to show these labels fail for the reader. In contrast, the chart on the right provides sufficient room by re-orienting the chart vertically.

https://www.pythoncharts.com/matplotlib/rotating-axis-labels/

Juicebox

9. Trend chart aspect ratios

There is research about the right aspect ratio for trend charts. The (mostly) accepted theory is “banking 45º”. From a Berkeley research paper:

William Cleveland demonstrates how the aspect ratio of a line chart can affect an analyst's perception of trends in the data. Cleveland proposes an optimization technique for computing the aspect ratio such that the average absolute orientation of line segments in the chart is equal to 45 degrees. This technique, called banking to 45 degrees, is designed to maximize the discriminability of the orientations of the line segments in the chart.

Eager Eyes: https://eagereyes.org/basics/banking-45-degrees

10. Drawing the link between the parts and the whole

Some of the best visualizations are able to represent individual things as part of the aggregate whole. It can be easy to abstract and aggregate data in a way that loses track of the individual pieces. This design approach is closely related to the concept that specificity is the soul of your data narrative.

https://guns.periscopic.com/?year=2013

Juicebox

These are the little things that take visualizations to the next level. It is the subtle bits that we try to build into Juicebox so you can look like a pro when you present data.

Data Discussion Lessons from Brad Pitt

Before Matt Damon impersonates an investigator in Ocean’s Eleven, Brad Pitt’s character delivers a little pep talk. 

Watch this 40 second clip:

Rusty Ryan (Brad Pitt) explains the rules of undercover conversation to Linus (Matt Damon). From: Ocean's Eleven (2001)

Now imagine yourself giving a pep talk to the next email, PowerPoint slide, or dashboard that you are about to send out. 

Presumably, your data is not meant to distort, yet we can gather from this short scene some practical communication tips to improve data-informed discussions.

Let’s break down the key moments.

Be natural.

[Damon takes an unnatural, stiff stance] “No good. Don’t touch your tie. Look at me.”

His first posture is fidgety and self-conscious with an overly professional stance. 

First impressions endure when it comes to perceived levels of interest and credibility. Most of us have an uncanny ability to sniff out a fake, and how data enters the discussion is no exception. We’re not computers, so we don’t enjoy an overwhelming data dump of facts, findings, and insights. Two paragraphs and 15 slides in, everyone wonders, “Where is this going? What’s the point?” Messages must be clear and focused and eliminate the unnatural, mechanical chart headings and the unnecessarily complex statistical jargon. 

Be honest.

“I ask you a question. You have to think of the answer. Where do you look? No good. You look down; they know you’re lying. And up; they know you don’t know the truth.”

Be honest with what you do and do not know and what data you do and do not have. Your audience expects to have certain questions answered in order to take your information seriously. Your audience wants to both hear and understand answers to questions like these:

• How do I know I can trust this data? How was it collected and who was involved?

• How exactly is this metric calculated?

• I see the number is X, but how do I know whether that is good or bad?

• What’s the history of this number and the frequency of its collection?

• How quickly does this number usually change?

• Why is this useful for me to know? How will it change what I care about?

These questions aren’t novel. They follow the 5W’s basics. Yet they are often either left out or overcomplicated in most data discussions. The goal here is to acknowledge these needs in the simplest, most useful way.

Start with a (very) short story.

“Don’t use 7 words when 4 will do.”

With data, as with words, precision is as much an art as a science. Still, helpful tools exist. Ann Gibson wrote a relevant post and I highly recommend reading the article for all the details, but here’s the magical excerpt:

“Once upon a time, there was a [main character] living in [this situation] who [had this problem]. [Some person] knows of this need and sends the [main character] out to [complete these steps]. They [do things] but it’s really hard because [insert challenges]. They overcome [list of challenges], and everyone lives happily ever after.”

The beauty of this frame narrative is that it provides a structure for those who are too long-winded to focus on the essence of their own message, and it helps others whose ideas tend to dart all over the place to preserve a sequential flow.

Each of these [placeholders] are candidates for data context that help satisfy the previous "Be Honest" section. I mocked up a quick scenario that demonstrates a short story with useful data context:

Set your mark.

“Don’t shift your weight. Look always at your mark but don’t stare.”

You’ve likely heard of S.M.A.R.T. goals before, but are your charts smart? Something as simple as a target value by a specific date on a chart can work wonders at moving towards something tangible. People crave purpose, so set and communicate your goals. But don’t be that presenter who stares incessantly at your metrics and goals. 

Be enjoyably useful.

“Be specific, but not memorable. Be funny, but don’t make him laugh. He’s got to like you; then forget you the moment he’s left your sight.”

“Jazz it up,” “Make it shine,” and “Make it pretty” are all phrases you’ve either heard or used yourself. Few situations are more disappointing than when a company tries to overcompensate with their insufficient, irrelevant data by lathering on the “wow factor.” Don’t succumb to making your data memorable for the wrong reasons. For businesses, the goal isn’t memorable chart-junk, but that does not mean your data should be lifeless and shallow.

Don’t leave people hanging.

“And for God’s sake whatever you do, don’t, under any circumstances…”

The worst move you can make is to omit the call to action. End with clear next steps, key questions posed, or an action button that allows your audience to engage with immediacy, while your solid ideas are fresh and ripe for action.

Automated PowerPoint Generation, or Making a “Slide Factory”

We’ve found 10 of the best options to automate and update data for recurring presentations.

The Challenge

Let’s say you need to produce the same presentation month after month, updating the data each time. Or maybe you have a set of slides that need to go to a bunch of different audiences each with their own specific market, product, business line, or industry. While there are some tools available (see below), ultimately there are some difficult choices to make.

Updating all the slides by hand can be tedious, slow, and error-prone. The presentation is basically the same, you simply want to swap out the underlying data. You need something that acts like a "mail merge" for PowerPoint. There are a few things you want to consider as you evaluate your options:

1. Easy of use. Can you create your automated presentation workflow without technical expertise?

2. Efficiency. How easy and fool-proof is it to update your data? Will you need to check your presentations to ensure all the formatting worked as expected?

3. Audience needs and expectations. There are a variety of ways to deliver an automatically-updated presentation. Does your audience want the data to be interactive? What is going to be most convenient for them to review the results?

4. Cost. Is there an affordable or free solution to deliver this capability? Or will you need to get budget from your IT department?

Based on our research, there are a few paths to consider. First, you can choose from a variety of PowerPoint plug-ins that will allow you to connect your PowerPoint slides to different data sources and automate the generation of those slides (we’ve listed three such solutions below). Alternatively, you may consider web-based solutions to deliver your report or presentation. These options offer more powerful capabilities but may not come with the traditional joys of a PowerPoint document (five solutions below). Finally, if you are a developer, there are a couple technical solutions that allow you to construction the data integration workflows you need.

PowerPoint Plug-ins for Automated Presentation Generation

Engage by Markido

“Link shapes, tables, charts, images and infographics to MS Excel. When the source data changes you can update your whole presentation from multiple sources with just one click.”

In addition to the presentation automation features, Engage comes with a ton of other capabilities (e.g. maps, templates) for improving your PowerPoint presentations.

Cost: $29/user/month

DataPoint by PresentationPoint

“Do you want to save time and money with real-time linking your slides to data sources, instant data updates, and no more copy-paste errors?”

This time-tested product has a load of data connection and automation features to make you more efficient. See it in action in this video.

Cost: $29/month

Think-Cell

“It is a powerful charting and layout software that automates your PowerPoint work, improving slide creation efficiency and quality.”

Like other solutions, Think-Cell includes features for advanced charting alongside update automation.

Cost: Starts at $20/user/month

SlideFab

Similar slides, different content?Time to automate!

Mass-create individualised copies of PowerPoint slides

based on any Excel workbook without VBA coding.

Cost: $20/month

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Web-based Solutions for Automated Presentation Generation

Juicebox by Juice Analytics

Juicebox is a web solution for creating interactive data-rich presentations.

Your “data stories” are as easy to create as a PowerPoint presentation but live on-line so they are easy to share and manage. Replace or update your data for your entire presentation in seconds.

Cost: Free for up to 3 users; $49/month for 5 editors.

Reach Reporting

“Experience the future of automated report creation. Connect your accounting software. Select a template”

Reach Reporting is a modern web-based tool which connects directly to your spreadsheets.

Cost: Starts at $149/month

Google Slides by Google

Google Slides is a free web-based presentation tool that can export slides as PowerPoint slides. Using this trick, you’ll be able to linked a Google Spreadsheet to your Google Slides and updated the data hands-free.

Cost: Free with a Google account.

Displayr

“Flexible live reporting software built to make every stage of your analysis & reporting faster and easier.”

Displayr is focused on survey data, a common use case for recurring reports.

Cost: Free (for <20 docs, 1000 rows of data); $200/month

Tableau Software by Salesforce

This powerful visual analytics and dashboarding solution includes integrated features for exporting to PowerPoint. Turn your high-intensity visualizations into impressive slides.

Cost: $70/user/month

Developer Options

Finally, if you are feeling ambitious, there are some developer tools that will let you connect PowerPoint to your data sources directly.

PowerPoint Automation Toolkit: "With the PPTATK, PowerPoint becomes a best-case union of a presentation tool and a report writer. With the Tookit, you can build presentations which combine static slides from a slide library and data-driven slides which display charts, tables, and graphs from structured data sources."

Python PPTX: “python-pptx allows you to create new presentations as well as make changes to existing ones.”

Why Your Data Presentation Solution Needs to Be Fast

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We created Juicebox to serve the citizen data analyst. We empathize with the every-person who is deep in Excel spreadsheets and presentations; data is part of their job--but not the only part.

We want to give these people the chance to create beautiful data visualizations, presented alongside their messages and insights. These information workers shouldn't have to be trained on a complex analytics tool or require the skill set of a data scientist, designer, or developer.

For this group, speed matters. It needs to be quick to learn and quick to create.

By why does speed matter? Why is it important that you can create an interactive, exploratory data presentation before your coffee is done brewing?

1. Speed of thought

Your "maker tools" need to keep up with your train of thought, not distract from it. A data presentation tool is a way to express both data, messages, and insights. If you're more focused on how to express vs. what you want to express, then the tool is getting in the way. Like when I try to play a game on Xbox.

2. Speed of attention

Let's be honest: we're all full up when it comes to sharing our attention. It is among the rarest of resources. We needed to deliver a solution that doesn't require certification to get started. In fact, it shouldn't take more than a few minutes to get going.

3. Speed of business

You've got a meeting coming up in 30 minutes and your boss wants to see results from the latest marketing campaign. You need a solution that lets you be nimble enough for the most unreasonable of asks. These are the use cases that traditional dashboards don't work for, and traditional presentation tools don’t support interactive data storytelling.

4. Speed of trial-and-error

Nothing comes out right the first time. But if it is easy to try-try-again, you can fail fast. Prototyping and rapid iteration is how you refine what you want to say. When we used to hand-craft our analytical applications, I had to warn our clients that our design is going to be wrong. With Juicebox, we can create a prototype in minutes and start gathering real feedback immediately.

5. Speed of discussion

Have you ever gone into a presentation with a buttoned-down analysis, then left with more questions than answers. Without the ability to interact and explore data on the fly, you'll end up with more work (and a less happy audience). The speed of answering questions helps avoid the long delays of rescheduling a follow-up meeting.

3 Basic Lessons on Data Visualization

These are three of the most important principles to bring to your data visualization work.

#1. Start with your audience

Before you make your first chart, think about who you are looking to serve with this data and what they do in their role.

An executive will have very different needs from an analyst. What data you choose to present and how you present it should start with empathy for your audience.

#2. Pick the right chart

Every chart type has its strengths and weaknesses. Line charts are great for showing change over time. Bar charts compare performance on one measure for a set of things.

You’ll need to understand what you want to emphasize in your data, then select the chart type that highlights that part of your data.

#3. Keep it simple

Remember that audience in #1? They are busy and have little attention. You need to keep your visualization as direct and straightforward as possible.

“Keeping it simple” means highlighting the important insights explicitly, labeling your chart clearly, and removing detail or extra data that distracts from your message. This is the hard and important work of editing.

#4 (Bonus): Looks matter

While your data visualization is primarily about the insights from your data, making it attractive can be the difference between your audience reading it or not.

Quality data visualization includes thoughtful use of color and contrast and graphical elements to bring the data to life or make the information more relevant.

We’ve been creating a data visualization solution designed with these principles in mind. It’s called Juicebox. Give it a try.