How-to use data viz in products and a few constructive critiques

Data visualization or data viz has become common place and more and more products have incorporated analytics and often data viz into their apps. This article is going to explore the good, bad, and ugly of data viz in product. I have talked on this topic over a dozen times and as a person that has a foot in the product world and a foot in the data world it really appeals to me.

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

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 as is 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 a certain action or not do a certain action.

Before getting into the a mini-dissection of examples of what we think was done well and what opportunities exist, lets get into a framework if you are in 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 not a customer need or pain point seeking to be filled then there is no reason to include a 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 it doesn’t mean that every organization should fill it. Does it align with core strengths and strategy of an organization is the question and 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 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 doing 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 ongoing basis to ensure the data viz is functioning as planned.

I call this the Data Viz in Product framework and as a person that spent many years in product and data viz, this framework works helped me and hope it does so for you. Feel free to use it how you like and 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 there might be questions we have. Our belief is that all data viz is part art and part science so there is 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 and in a way that is both convenient and also sometimes more emotionally resonate. 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 includes 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 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 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 graphic from my phone is below.

Source: Audible app screenshot

Source: Audible app screenshot

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 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 the number of books I have seem pretty high.

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

What does Audible do well?

  • Audible uses an acceptable data viz given it is books over time. I might have chosen to go with 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 it users and if appropriate use data viz to help its users in understanding users 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 overall product experience. This is not just making it look visually aligned (which it does somewhat) but also just aligning into overall experience from user. I feel like this data viz was incorporated as basically a check-the-box around showing books purchased over time and a thought lets incorporate into a data viz.

  • Audible could make a 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) as respects the Data Viz in 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 that are curious people like myself. One thing I might have interest in understanding is when I listen, how much I listen compared to others, etc.

Ring App: Ring is a doorbell where there is a video camera and also a 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 is taking in a ton of data while seeking to provide a service to end users. Ring allows me as a home owner 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 be fascinated with the opportunities.

Image Source: Ring app screenshot

Image Source: Ring app screenshot

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 that has a diversity of cultures, diversity of residential and commercial, and great location. I and many others 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 as respects 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 judgement 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 to scare me into buying a more premium ongoing monitoring service. Maybe it is a check-the-box effort and only limited amount of effort put on this.

What does Ring do well?

  • Ring provides fairly comprehensive data related to the area 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 pattern 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 around the neighborhood. For example, my Pocket Casts podcast catcher app provides some humorous items like 11.47 Trillion emails were sent during the time you listened to podcasts. Things like this can provide humor and delight in an app and in 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 a 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.

Source: Fitbit app screenshot

Source: Fitbit app screenshot


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 that just 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 where they want to be able to dive into detail about the information. In addition to the the detailed screens Fitbit provides, it provides 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 process of trying to understand it but seems like 0 is worst and 100 is best so it provides you a sleep number in addition to hours. This is sort of allowing a novice to go a little deeper into sleep understanding without breaking down the different phases of sleep and number of total hours.

What could Fitbit do better?

  • Fitbit UI prior to 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 recent up date but 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 on the data viz and more on how notification 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 have talked to dozens of people that are so I am speaking at this from a less biased perspective maybe.

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 on total budget for month along with how you are tracking on 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 spend 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 could better use hues to ensure there are not 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 load 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 for 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 in a more useful form similar to the screen on the right or put it in a waterfall chart even.

Overall I think Mint does a solid job with data viz in its product and incorporating it into its overall experience and helping around customer problem and helping modify behavior but at 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 and also adjustment for each side of 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 there is the ability to look 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 laying 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 sales people have a limited time to close a sale and are often of mixed sales expertise and by leveraging this data viz it helps empower its sales people 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?

  • There are no clear opportunities as respect this data viz in my opinion. Certainly allow the sales person to print or email this data viz to the person especially in the case where sale didn't happen so maybe potential follow up.

  • Not sure if this data viz can be used in the hope but if so having that ability also would 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 to help 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 because I wanted to end on a good note but also I think we can learn from good and bad from others.

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 this makes a lot of sense assuming these cards are indeed the most important items for consumers.

  • Cards are aligned together on dashboard to tell the "Google Analytics story" which is how could are the metrics measured performing.

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



What could Data Pine do better?

  • 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 labels in the cards at the top of the dashboard and charts below. They are using 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 opinions above are just that opinions and not meant to be endorsement of products or capabilities.

If you are a product person then we hope in the future you will take a 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, think 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 hope you leverage the six-step Data Viz in Product framework above so you more consistently deliver valuable information in your products that align to your users and your desired experience.

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What are your Data Principles?

First off, you might be saying, “Data Principles, WTF?” Ok, think of Data Principles as your manifesto or by-laws in how your organization uses data. Data Principles could be about how you go about collecting data. How you go about sharing data within the organization. What your view of data in the decision making process. What are your views around Data Governance. What is transparent and what is hidden to your consumers, partners, employees, etc.

Wow, this is a lot you say. Let’s take a step back though and provide a little more context on Data Principles and we shamelessly adapted this concept in part from Ray Dalio’s Principles book that is a great read. Ray Dalio applied Principles that he used in his personal life and professional life. While we recommend you read the book we think the concept of putting together your Data Principles for three reasons:

  1. Process of developing principles helps engage in a meaningful conversation and ensure there is buy-in.

  2. Provides an immediate reference point so that everyone in the organization can understand how data should and should not be used within the organization.

  3. Better provides accountability in using data well in an organization and at the same time helping democratize the message around the importance of data.

There are certainly other reasons that these Data Principles matter. One important point when putting Data Principles together this should be done by a diverse, cross-functional, and cross-seniority level group. It can’t just be leaders. It can’t just be data people. A broad group of input and agreement on Data Principles is needed. Further, the result of this effort needs to be even more broadly communicated so that everyone understands the result.

The process of coming up with Data Principles is just as important as the Principles themselves. Don’t cut the process short and use it as an alignment and communication effort around how your organization cares about data and will use it strategically and ethically. Having an outside facilitator helping provide the Data Principle development sessions is often beneficial so that it is not too driven by one area or another of the organization. It is important that all areas feel and actually do have input in the Data Principles coming together.


Now you might ask, "what should our Data Principles be?” That we cannot answer blanketly but instead each organization’s culture, maturity level, risk tolerance, and other factors come into play. What is important is that the conversation happens and the Data Principles result. One important point in coming up with Data Principles is don’t lie to yourself as an organization. Data Principles should align with culture or if different than culture than align with the direction that the organization is broadly going to put effort into shifting culture.

Data Principles are only as good as the effort you put into creating, communicating, and reiterating. Got your Data Principles defined and want to share then we would love to see them.


Dave Mathias

Co-Founder of Beyond the Data
Passionate about Data, Analytics, Product Management & Communities

Dave has been advising clients on strategy and data for over 12 years, helping them elevate their products and services with data and analytics. His passion is bringing together people, process, technology, and data to make the world a better place.

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How Data is Helping Leaf Peepers Plan the Perfect Fall Trip

Happy Fall! Fall is my favorite season and living in the Northeastern region of the Continental US I look forward to chillier days and leaves changing. I enjoy seeing what Albert Camus describes as “Second Spring” where the leaves are a vibrant red, yellow and orange! The challenge I have every season is finding that right time to drive up to that scenic overlook to capture a picture perfect landscape of colorful leaves with just the right amount of blue skies. It looks like a data scientist, Wes Melton might have solved our problems by determining the precise future date that the leaves will peak in each area of the Continental US!

fall foliage.png

Wes Melton along with his co-founder David Angotti have created the Fall Foliage Prediction Map. According to Melton, this is one of the only fall leaf tools that provides accurate predictions for the entire continental US. Looking at the snapshot above, a user can take the slider across the 12 week period to see when and where fall foliage will peak in a given region. Looking at the week of October 5th, we can can see patches of the Northern US hitting peak season like Maine, Vermont and New Hampshire. Another great area that might not come to mind are the aspen trees in Colorado. 

The story behind the creation of this map is interesting. At the time that they were starting their Smoky Mountains Cabin rentals six years ago, they were getting questions on the best times to experience the fall colors. They started conferring with meteorologists and the predictions were accurate. The advice was well received and they have been updating the tool ever since!

Autumn is a second spring where every leaf is a flower
— Albert Camus

To create the map, a complex algorithm was developed that carefully analyzes several million data points and outputs approximately 50,000 predictive data pieces. This data then enables the program to forecast county-by-county the precise moment when “fall peak” will occur. As time goes on and the algorithm is fed more data, it will only become more and more intelligent.

Some of the data points processed by the prediction algorithm include National Oceanic Atmospheric Administration (NOAA) historical temperatures, precipitation, forecast temperatures, and forecast precipitation; historical leaf peak trends; and peak observation trends.

I am excited about this story and this tool because it was birthed from customers looking to rent cabins and Melton and Agnotti went above and beyond to accommodate them. They didn’t stop at the Smoky Mountain Region but went on to answer this question for the whole continental US. They were also able to compile data points from several sources to piece together this aesthetically simple visualization.

Check out the map and learn a little bit more on how and why the leaves change color. I would love to hear about your favorite leaf peeping location!

About the Author


Allen Hillery


Allen serves as part time faculty at Columbia University’s Applied Analytics program. He has extensive experience in developing and executing data analysis and integrating results into marketing programs and executive presentations. Allen is very passionate about data literacy and curates an article series that focuses on the importance of creating data narratives and spotlighting notable figures on how their use of storytelling made major impacts on society.

You can sample his work here: Three Reason Why Storytelling is Important in Business

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Data Literacy is Not One Size Fits All

“You don’t have to be a data scientist to be data literate.”

In fact less technical colleagues are welcome to help narrow the data literacy gap! As I mentioned in my last post, there are 2.5 quintillion bytes of data created daily. I love Chartio’s view of the current BI landscape - The world has gotten really good at collecting data, now the largest bottleneck is our ability to understand the data and make informed decisions based on it.”

You don’t have to be a data scientist to be data literate

There’s a lot of data to process and most companies have been hiring the most technically oriented people they can find to build armies of analytics and data science teams to analyze data. The one thing they have ignored is the data professionals’ ability or desire to communicate with a general audience.

“The world has gotten really good at collecting data, now the largest bottleneck is our ability to understand the data and make informed decisions based on it.” — Chartio’s view of the BI landscap

I’ve worked on several analytics teams and while I choose to champion the capabilities of data, I’ve seen my peers struggle working with business teams or fall short in explaining their analysis. I’ve also found the expectations placed on analytics teams to be unrealistic at times. Analysts are expected to wrangle data, analyze it in the context of knowing the business and its strategy, make charts and present them to business stakeholders with short turnaround times. Wash, rinse and repeat.

The bump in the data road lies right at the last mile - when it comes time to explain the analysis to decision makers. In a question on Kaggle’s 2017 survey of data scientists, to which more than 7,000 people responded, four of the top seven “barriers faced at work” were related to last-mile issues, not technical ones. Here they are in the word cloud below.


I’ve experienced all of the above; I can assure you all that it’s not fun. What I have come to realize is that data is not just for the uber technical.  The opportunities in data can be harnessed by many with liberal arts backgrounds. Before you gasp in disbelief, hear me out. The identified bottleneck has been the ability to understand data and make informed decisions. Combined with the four barriers that have been cited above we need individuals who can narrow the data literacy gap by:

  • Framing questions correctly

  • Bringing together cross functional teams to work effectively in analyzing data

  • Communicating results to decision makers and the public

Analytics teams need all the help we can get at the last mile! While you may have believed that without knowing what an R package is there is no way you can contribute to a data project, you couldn’t be more wrong. When it comes to analyzing and presenting data, critical thinking is crucial. If you’re on the business side of the organization, you are closer to the key performance indicators that the company is striving to obtain. You could potentially  be a project manager with a proven track record of meeting deadlines. These are all skills that are much needed to drive data analytics pass the finish line!

One trend that has been growing in data driven organizations is hiring of liberal arts talent. These individuals possess a lot of the key skills needed for analysis - critical thinking and context setting. I like what William Cronon writes in his article, “Only Connect”. He defines a liberally educated person as someone who can: 


Data folks, I’m not taking anything away!  The data presentation piece of the puzzle needs to catch up to all the advancements we’ve made in ingesting and processing data. These additional talents will complement our teams and the symbiotic relationship will advance our cause. In Scott Berinato’s article, “Data Science and the Art of Persuasion”, he points out that one of the steps to building a better data science operation is to define talent not team members. The core set of talent that Berinato describes is qualities I’ve seen in past teams. They include:

As Berinato pointed out in the article, there’s a difference between talent and team members. A team member can possess a few of the talents listed above. I know that I’ve strived to be an ambassador in my organizations and bridge marketing and analytics folks to move projects along. I’m also very passionate about presenting data insights as a story. 

About the Author


Allen Hillery

Adjunct Professor at Columbia University,
Writer and Editor at Nightingale, a Publication

Allen serves as part time faculty at Columbia University’s Applied Analytics program. He has extensive experience in developing and executing data analysis and integrating results into marketing programs and executive presentations. Allen is very passionate about data literacy and curates an article series that focuses on the importance of creating data narratives and spotlighting notable figures on how their use of storytelling made major impacts on society.

You can sample his work here: Three Reason Why Storytelling is Important in Business

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Why We Should Be Excited About Data Literacy

Allen Hillery

Hello! My name is Allen Hillery and I’m happy to be teaming up with Matt and Dave to get you excited about Data Literacy. I’m a data champion who has worked with business and data teams throughout my career playing the role of ambassador and coaching them on how to better leverage data. I’ve had the opportunity to work in companies with varied data maturities ranging from reactive to more thoughtful on executing results. Like most of you, I aspire to work in a truly data informed organization where everyone is literate to understand the context of their data they’re analyzing and the value it brings internally and externally. 

So my question to you is - How comfortable are you with data? Does the thought of getting your hands dirty with data excite you or make you want to cringe? According to Forbes, there are 2.5 quintillion bytes of data created daily. If you think about it, data is a major part of our lives.  Each one of us, generates data as we move from google searches to shopping with a club card at the supermarket, not to mention data created by Internet of things. In the office, are you the go to dashboard expert or maybe you’re resident data whisperer who massages insights out of your analytics teams? 

Being data literate means you have the ability to read, understand, create and communicate data as information. We are on the precipice of an exciting time, as we have superfluous data available to analyze.  This data can present information that provides better customer experiences and enables your team to identify which segment would be best served by your products. While the amount of data being created can sound daunting, the evolution of the tools and infrastructure to help us navigate this landscape is intriguing! 

People aren’t going to go to BI, BI has to go to to the people.
— Nick Caldwell

Tech executive, Nick Caldwell said, “People aren’t going to go to BI, BI has to go to to the people. This is already happening in a big way.” The staggering amount of data that has been made available to us has hit a tipping point where data analysts have to enable non technical business partners to develop insights on their own. This trend has caused a shift towards more intuitive self-serve tools.  At the same time, the proliferation of opportunities to learn query language are seemingly ubiquitous.  

In addition to trends pivoting our work cultures to being more data informed, the growth and learning opportunities that will come from leveraging both data and data literacy have me really psyched!  Companies are beginning to realize the importance of investing in their employees’ data literacy. AirBnB is a shining example of investing in data literacy through the creation of their data university. This effort was made with the belief that every employee should be empowered to make data informed decisions. It took roughly two years to launch but one of the amazing results is a reported 50% increase in active use of their internal data platforms. Another benefit is that it frees up data teams to concentrate on more complex tasks. 

AirBnB Data University

Sharing success stories, like AirBnB illustrate the importance of empowering employees and customers with data. Think of all the apps and services you use right now. You’re leveraging data when you are booking that next AirBnB, searching Yelp for food recommendations and hailing your lyft to get around. BI is coming for you and you’re more acquainted with data than you realize. So maybe you’re the resident data wrangler on your business team who realizes that data is not as aloof or mysterious as you once thought? Maybe your knowledge of the business combined with your new found data sleuthing skills has put you on a direct path to being a data champion lobbying for more training? Then you’re at the right place! We’re here to reassure you that you don’t have to be a data scientist to be data literate! You just have to be open to getting your hands a little dirty with understanding how to leverage data!

About the Author


Allen Hillery

Adjunct Professor at Columbia University,
Writer and Editor at Nightingale, a Publication

Allen serves as part time faculty at Columbia University’s Applied Analytics program. He has extensive experience in developing and executing data analysis and integrating results into marketing programs and executive presentations. Allen is very passionate about data literacy and curates an article series that focuses on the importance of creating data narratives and spotlighting notable figures on how their use of storytelling made major impacts on society.

Follow Allen:

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