Data visualization or data viz has become commonplace, and more and more products have incorporated it and analytics into their apps. This article explores how you can better implement data visualization as a product manager or product designer, which will hopefully lead to greater product adoption, product use, and product value from your users. We will go through some of the good, bad, and ugly of data viz in product and some steps you can use to ensure yours is good.

First off, let’s talk about what we mean by data viz in product. In product could mean actual data viz in the product, but it could also be something that is used in marketing, support, or other components where the end user of the product uses the data viz. Further, data viz doesn’t mean that it needs to be digital data viz with interaction but simply could mean data viz that is used in a cardboard display or a product sheet.

Secondly, let’s take a quick step back on what data viz is and why data viz matters. We define data, viz., as the representation of information in a graphic or visual form, often in the form of charts, graphs, tables, pictures, etc. Our goal is that any data viz should seek to have a defined user(s) and defined objective(s). The objective may be to inform the user or get the user to do or not do a certain action.

Before getting into the mini-dissection of examples of what we think was done well and what opportunities exist, let's get into a framework if you are in the product and looking to incorporate data viz. There are six things that we think every product person, whether a product manager, product owner, user experience, or product marketer, should think about:

1. What is the customer need seeking to fulfill with the data viz? If there is no customer need or pain point seeking to be filled, then there is no reason to include data viz. Remember, if you are not providing your customer value with data viz, then why are you using it? This may seem obvious, but I think even from our examples below, you will still see that this item can be missed.

2. Does satisfying the identified customer need align with your organization’s strategy? Just because there is a need doesn’t mean that every organization should fill it. Does it align with the core strengths and strategy of an organization question? this is the same question all product people should be asking with whatever product-related effort.

3. What customer behavior do you seek from your data viz? Once you have identified a need and that your organization’s strategy aligns with that need, then the question is, what is the behavior you seek as a result of the data viz. Generally, this means either wanting a customer to do something or stop doing something, but it could also be having the customer feel something.

4. Does the data viz fit into the overall product experience? The overall product experience is more important than ever as customers become more sophisticated. Understanding how analytics and data viz fit within that product experience is important for maximum benefit to be received.

5. Is the data viz good? You can’t forget to do good data viz. No, 3D pie charts with 20 slices. Yeah, the best practices of data viz still come into play. Don’t do the hard work upfront and drop these at the end.

6. Are the assumptions and behaviors still as anticipated? It is critical that assumptions and behaviors be tested and retested on an ongoing basis to ensure the data viz is functioning as planned.

I call this the Data Viz in Product framework, and as a person who spent many years in product and data viz, this framework has helped me, and I hope it does so for you. Feel free to use it how you like, and I certainly love to hear your stories if you do on what worked and what didn’t.

Now let’s get into data viz and product examples. We will first go through a series of examples and do a mini-dissection of what we think was done well and what opportunities exist. Sometimes, we might have questions. Our belief is that all data viz is part art and part science, so there are no right answers.

Audible App: Starting out, I want to say that I am a huge fan of Audible. It has allowed me to consume more information in a way that is both convenient and also sometimes more emotionally resonant. In fact, I have used Audible for over 10 years consistently. But, sorry Audible, but your data viz sucks, in my opinion!

They have several uses of data viz in their app and include a badge page which, while aesthetically pleasing, seems less useful and more ego wall, but I have heard from others that this is indeed a feature they like. There is also a basic listening time analytics page that provides a rudimentary understanding of listening and provides it on a daily, weekly, monthly, and total basis. There is also a “Listening Level” page, which again seems more like a badge-like page. It appears from this page that simply the number of hours I listen in total is equal to a “Listening Level” and uses terms like Newbie, Novice, Pro, Scholar, and Novice. But, the data viz I am going to dissect here is the “Audible Titles” page, and the graphic from my phone is below.

What is the purpose of this data viz? Honestly, I don’t know, and here are some questions I asked myself:

Is it meant for me to feel better about myself? Maybe it does a little, but it also makes me feel bad at the same time because I know a number of these books I started and didn’t finish, or there are some I didn’t even start.

Is it meant for me to buy more books? I don’t think so because my number of books seems pretty high.

Is it meant for me to enjoy the app more? I don’t think so because I feel there is not much to do in this visualization.

What does Audible do well?

• Audible uses an acceptable data viz given it is booked over time. I might have chosen to go with a line graph or something else instead of an area chart, but an area chart can work.

What could Audible do better?

• Audible could better understand its users and, if appropriate, use data viz to help its users understand their problems. An individual data viz needs to start out with a desired audience(s) and a desired purpose(s). I don't clearly understand either in this data viz.

• Audible could better integrate its data viz into the overall product experience. This does not just make it look visually aligned (which it does somewhat) but also aligns it with the user's overall experience. I feel like this data viz was incorporated as basically a check-the-box around showing books purchased over time and thought, let’s incorporate it into a data viz.

• Audible could make better data viz by both enhancing the color and size of the labels on the axis and also reducing visual fatigue of the orange in the area chart.

I think Audible has failed on both (1), potentially (2), (3), potentially (5), and likely (6) with respect to the Data Viz in the Product Framework outlined above. Maybe instead, Audible needs to think about its different user personas and break down how data viz could benefit them and align with Audible’s brand. Audible has loyal customers who are curious people like myself. One thing I might be interested in understanding is when I listen, how much I listen compared to others, etc.

Ring App: Ring is a doorbell with a video camera, speaker, and microphone. It is a product that falls into the Internet of Things (or IoT) space, which I find fascinating and have been involved with in the Twin Cities over the past 5 plus years. Ring is a classic IoT product in that it takes in a ton of data while seeking to provide a service to end users. Ring allows me, as a homeowner, to understand when someone is at my door and can even communicate with that person no matter where I am. It gives me peace of mind when I travel, but it also allows my technical side to be fascinated with the opportunities.

Now let’s get to the data viz in the app. Ok, yes, this is my neighborhood, and really, it is a great neighborhood in the Twin Cities with a diversity of cultures, residential and commercial, and a great location. Many others and I look at Richfield as a safe, close suburb of Minneapolis.

From the Ring data viz you might be highly concerned about living in this area. It reports all the crime that is reported and does not delineate between violent and non-violent crime easily; it does not delineate between residential and commercial crime easily, it does not delineate anything with respect to time of day or relative population count and other similar metro areas. Basically, it gives me a map with points laid on it to make a judgment for myself.

I wonder how many people use this data viz for Ring. I also wonder what type of people use this data viz for Ring. I further wonder how many people would value Ring higher if they had a more meaningful level of data viz.

What do you think Ring’s objectives are in showing me this data viz in this format? Maybe it will scare me into buying a more premium ongoing monitoring service. Maybe it is a check-the-box effort, and only a limited amount of effort is put into this.

What does Ring do well?

• Ring provides fairly comprehensive data related to the area where I live and not only in the app but also sends out a notification that the report is ready.

• Ring arguably leaned in most with its audience that likely uses its product out of fear and protection and guessing a desire by its customers to know all crime around them.

What could Ring do better?

• Ring could help users be able delineate data for users to understand their area both as a novice and a nerd. Things like: a) violent vs. nonviolent crime; b) commercial vs. residential; c) give me context on time of day; d) allow me to incorporate traffic patterns or allow Ring to understand my traffic patterns and give recommendations based on these; and e) context to whether the crime is a lot per person for type of area, increasing/decreasing, and other information that may help me make better decisions.

• Ring could also help not just lean into fear, though, and maybe provide other delightful information about the neighborhood. For example, my Pocket Casts podcast catcher app provides some humorous items, like 11.47 Trillion emails that were sent during the time you listened to podcasts. Things like this can provide humor and delight in an app and help users that might help bring users back from undesired negative tendencies like unwarranted fear and potential biases.

Fitbit App: I love my Fitbit. I actually used to love my Fitbit more and what I am showing was the prior UI prior to a recent update that I actually think is a worse use of data viz in their product. No matter what, though, Fitbit is something that I have used for years, and it tracks things like your steps, your sleep, and other habits related to your health and wellness. I can easily say it is the data viz in an app that I use more than any other where data viz is not the primary purpose of the app.

What does Fitbit do well?

• Fitbit understands what users like me seek to use it for by quickly making metrics easily understandable and how I am tracking to my goals and does this on a daily basis. It even provides some easy-to-use insights where I can provide feedback on if I liked it or not.

• Fitbit understands that some of its users are novices who will use it to understand if they hit their step goal or other goals, but it also understands some of its users are nerd users who want to be able to dive into details about the information. In addition to the detailed screens Fitbit provides, it provides the ability to download the data into .csv even. Great for a nerd like me, and yes, I have done this.

• Fitbit understands that sometimes you need to use multiple encoding to help ensure the user easily understands what you are conveying. When saying multiple encoding, it means relaying the same information in multiple ways. For example, you may encode information with color, shape, size, etc. Fitbit goes in and uses color and shape in how it fills items to let me know when I have hit goals, for example.

• One newer feature not shown above is Fitbit has created a sleep number itself on how well you slept. I am still in the process of trying to understand it, but it seems like 0 is the worst and 100 is the best, so it provides you with a sleep number in addition to hours. This sort of allows a novice to go a little deeper into understanding sleep without breaking down the different phases of sleep and the number of total hours.

What could Fitbit do better?

• Fitbit UI, prior to the recent update, had the top cards be able to flip days without flipping corresponding data below, which meant a misalignment of data showing different items for different dates. This has been resolved, though, in a recent update, but it is a good reminder for us to have interactivity be well thought out.

• Fitbit could do a better job leveraging notifications in tandem with the data viz. This is less a critique of the data viz and more on how notifications of information are often more meaningful than the data viz itself because the notifications, in theory, should be more meaningful nudges.

These are just some of the ways Fitbit has gone in and designed data viz in its app in a thoughtful way that provides me both delight and value.

Mint App: Mint is an app where you can bring your financial information together, and it helps you understand your financial information and better plan and make decisions. I am not a Mint user myself, but I have talked to dozens of people who are, so I am speaking of this from a less biased perspective.

When looking at the screenshots below, you can see there is a lot of good information, with the screen on the left giving you an idea of where you are tracking the total budget for the month, along with how you are tracking individual categories and subcategories. They use color to help you understand how you are tracking. Then, you jump to the middle screen, and it is a donut chart where you have your monthly spending broken down. Lots of colors, not completely labeled, and no delineation of categories that are variable versus fixed costs. The screen on the far left is a simplified view of how your credit score is and an indicator of where it falls on a credit score scale.

What does Mint do well?

• Mint does a lot of great tracking of financial data and helping display it in generally good data viz (ignoring bad donut chart in middle).

• Mint uses generally intuitive colors, although it could better use hues to ensure there are no issues for those with color blindness.

• Mint does a good job of separating out data viz in different screens and not forcing those together because different cognitive loads can be placed on each.

What could Mint do better?

• Mint could help differentiate between fixed and variable expenses because I generally cannot make fixed costs changes easily but variable costs I can.

• Mint could help me better assess how I do against others in my peer group and relay this information to me. Not to make me feel overly good or bad but to understand where I compare and maybe also against a type of person I want to compare against.

• Mint could drop the donut chart in the middle and put it in a more useful form similar to the screen on the right or put it in a waterfall chart.

Overall, I think Mint does a solid job with data viz in its product and incorporating it into its overall experience, helping around customer problems and helping modify behavior, but at the same time, I think it can improve.

Sleep Number: Sleep Number is a high-end smart mattress company that does some really cool things with its product, but it also does cool things in how it uses data viz. Sleep Number is probably best known for what its name indicates, i.e., its sleep number. It simplifies the sleeping experience to a sleep number and believes it is different for different people, and the beds allow adjustment for each side of the bed.

Sleep Number's use of data viz in its product is kind of unique in that what I am showing is not even its use in the product itself, but instead, it is a data viz that is part of the Sleep Number selling experience. You go into a Sleep Number store, and as part of it, you can look at how you sleep and how adjusting the sleep number could relieve the pressure points you have while sleeping. It is a simple data viz where it shows the two people lying down and where pressure is and uses color to differentiate pressure. There is also a Sleep number that is displayed. There is also a before and after view of things.

What does Sleep Number do well?

• Sleep Number understands that its customers are more sophisticated and want to feel they are buying a premium mattress that helps them sleep better. Harnessing data viz, in this case, is to help them do just that.

• Sleep Number understands that in-store salespeople have a limited time to close a sale and are often of mixed sales expertise, and leveraging this data viz helps empower its salespeople on both fronts.

• Sleep Number adds a level of credibility and authenticity to its product, and seeing is believing. Using data viz in this capacity helps Sleep Number better support its product value proposition.

What could Sleep Number do better?

• In my opinion, there are no clear opportunities with respect to this data viz. Certainly allow the salesperson to print or email this data viz to the person, especially in the case where the sale didn't happen, so maybe potential follow-up.

• I'm not sure if this data viz can be used in the hope, but if so, having that ability would also be great so people could adjust this with their app.

Data Pine Google Analytics Dashboard

Marketing dashboards are common, and this one is Data Pine’s Google Analytics Dashboard, which helps users understand and monitor Google Analytics data for one or more sites.

There are a ton of examples of good and bad dashboards out there, and this is a good example.

What does Data Pine do well?

• Right across the top are cards that are labeled but also have relevant icons and colors. Knowing that items at the top of a page get the most eye attention makes a lot of sense, assuming these cards are indeed the most important items for consumers.

• Cards are aligned together on the dashboard to tell the "Google Analytics story," which is how the metrics measure performance.

• Good use of data viz practices in carrying out visuals.

What could Data Pine do better?

• The size of text is fairly small in some areas, so enhancing the size of text and, if it doesn't fit, for example, then allowing a shortened view of text that is larger would be beneficial.

• There seems to be some confusion with the labels on the cards at the top of the dashboard and in the charts below. They are using the same label but seem to show different data.

Hopefully, these examples were helpful for you. Remember, data visualization is part art and part science, so talented people may disagree. The above are just opinions and not meant to endorse products or capabilities.

If you are a product person, then we hope in the future, you will pay closer attention to how information is being relayed in or with your product. Not everything needs to be a chart or a dashboard to relay data. However, thinking about leveraging concepts of gamification, behavioral science, and user experience as part of relaying this information is essential.

Good luck in incorporating data viz in your product, and I hope you leverage the six-step Data Viz in Product framework above so you more consistently deliver valuable information in your products that align with your users and your desired experience.

- Dave Mathias

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