Augmented Reality: A New Tool in the Data Scientist’s Toolkit

By: Brenna Hoffman

In the summer of 2016, millions of players in the United States and around the world were infatuated with one game. This game was called Pokemon Go, a game where the real world mixed with the virtual world and players walked around the world with their nose in a phone trying to catch Pokemon (my favorite was Eevee). At its peak in July of 2016, there were 28.5 million players a day just in the U.S alone.[1] Pokemon Go is an example of a very successful augmented reality (AR) application. According to Porter and Heppelmann’s Harvard Business Review article, augmented reality “transforms volumes of data and analytics into images or animations that are overlaid on the real world.”[2]

            Even during this unprecedented time of quarantines and isolation due to the COVID-19, AR can bring joy into people’s lives. One example of the joy it has brought to me is through the use of Facebook filters. Since Facebook filters augment the user’s reality, it is considered to be an AR application. I spent over an hour with my family laughing as the filters changed our faces and our backgrounds from bunnies to aliens.

The use and implementation of augmented reality is growing every year and will continue to grow. Statista predicts that the market size worldwide will grow from $3.5 billion in 2017 to $ 198 billion by 2025.[3] This article will briefly explain the different types of reality (virtual, mixed, and augmented), give examples of how augmented reality is being used today, and explain the potential pitfalls of augmented reality. Lastly, this article will introduce how augmented reality can help solve a problem that data scientists are quite familiar with: the last mile problem:  the inability of a data scientist to communicate her analysis to the people to whom the findings matter most (business users, executives, etc.). [4]

Different Types of Reality

There are three different types of reality besides actual reality: augmented reality, virtual reality (VR), and mixed reality (MR). In order to prevent confusion, I am going to briefly explain the difference between the three. Forbes explains the difference between these three realities: VR “immerses users in a fully artificial digital environment,” AR “overlays virtual objects on the real-world environment,” and MR “not just overlays but anchors virtual objects to the real world….and can be considered an advanced form of AR.” [5]

Examples of Augmented Reality

            One example of augmented reality (AR) was the Pokemon Go example given in the introductory paragraph. In addition, AR is being used in a wide array of other areas and industries from manufacturing to training. Below are three more examples of AR usage.

Google Glass[6]: According to CNN,Google Glass first launched in 2013 for the general public at a price tag of $1,500. Google Glass was a pair of glasses with a screen in the top right corner where the user could access information such as looking at the time and sharing photos. For reasons such as high price, clunky design, and privacy concerns, the first Google Glass was discontinued in 2015. Despite the negative fall-out for Google Glass, Google has turned its focus towards businesses with a new and improved model for $999 called the Google Glass Enterprise Edition 2 which includes a better camera and faster charging capabilities.

Why it matters: AR glasses are going to be needed in the future to allow a more seamless integration between the digital and real world, but to be adopted by the general public, they will need a more sleek design and a better integration with the real world. With many companies, including Microsoft, Magic Leap, and Google, competing to have the best AR glasses, the hope is that cheaper and easier-to-use glasses will be introduced soon.

Boeing Training Program[7]: According to the article “Why Every Organization Needs an Augmented Reality Strategy,” airplane manufacturer Boeing used AR training to help trainees complete “[an aircraft wing section] in 35% less time than trainees using traditional 2-D drawings and documentation.”Boeing produced this result by the use of interactive 3-D holograms and access to a remote expert.

Why it matters: Augmented reality (AR) training techniques, such as access to a remote expert and 3-D holograms (either by AR glasses or using a device pointed at a machine), has the potential to improve performance and reduce costs and risks, especially in dangerous jobs. The Boeing example illustrates a struggle between looking at something in 2-D and trying to translate it in 3-D. With the use of AR, people no longer have to convert a 2D image to a 3D image in their head. They can just put on a pair of glasses and see exactly what they need to do. AR can help eliminate this struggle.

Vehicle Heads-Up Navigation[8]: When trying to get from point A to point B, a driver previously had to use either a map or a GPS (on the phone or on a device in their car). I do not know how many times I have missed a turn because it was hard to tell how close the turn was on the map. Luckily, there is a new feature called an AR heads-up display. This display “lays navigational images directly over what the driver sees through the windshield.”

Why it matters: This use of AR can help drivers from missing turns and prevent fewer distractions. Also, this example will be used in the next section to describe one potential consequence of AR.

Even though AR offers compelling applications, it does have a dark side that has to be addressed.

Dark Side of Augmented Reality

“Imagine a world where your greatest fears become reality” was the introduction to Fear Factor, a TV show where contestants would decide if they had the courage to face their fears. Even though Fear Factor is no longer airing its introductory quote aptly captures the potential dark side of augmented reality (AR). For example, AR glasses can be lifelike and if someone’s greatest fear is spiders, then AR can be used to show spiders to them. They can be a tool to help get rid of someone’s fear, but also can be used for dark purposes. AR has many potential dark sides ranging from privacy issues (recording everything with or without user consent), hacking, and torturing. There is a fear that groups or individuals can “misinform and deceive, to augment reality very specifically and very cynically.”[9] The risk of hacking is potentially possible in the Vehicle Heads-Up example explained earlier. If a person is able to hack into the augmented reality (AR), s/he could potentially put a very life-like deer in the road. The driver may not be able to tell real from fake and could swerve and cause an accident, causing harm to both himself and others.

The risk of torturing might be seen if someone uses AR to “unleash a phobia on the victim.”[10] Imagine a terrorist using AR glasses to make someone think they are surrounded by something like venomous snakes. Again, the user may not be able to tell the real from fake.

Lastly, there are some major privacy concerns. For example, AR glasses could be used to record everyone, including the user, without her consent. This information could be sold to third parties and used for a variety of purposes. For example, individual data are logged and used regularly to place ads. One could imagine AR might provide yet another platform for advertisers to reach customers whether they want to be reached or not.

Since the use of AR is not widespread, laws have not yet been put in place to deal with some of the consequences of AR. The dark side of AR must be addressed by lawmakers and the makers of AR before these worries become too widespread. However, despite its dark side, AR offers a large potential role in the world of data.

Augmented Reality Could Help the Last-Mile Problem

Scott Berinato, author of “Data Science and the Art of Persuasion,” explains the last mile problem as the inability of a data scientist to communicate her analysis to the people to whom the findings matter most (business users, executives, etc.). While he offers several solutions to solving the last-mile problem (i.e. cross-disciplinary teams, empathy, sharing work),[11] I propose that another solution to help alleviate this problem could be the usage of AR.

            Imagine Jane, a data scientist, has been tasked with giving a presentation about her findings on pollution (it is going to get worse) to the board of directors of a coal factory. Her goal is to provide them with data about the problem, and how reducing production of coal will reduce pollution. Instead of giving a traditional 2D PowerPoint presentation, Jane walks in and hands out augmented reality (AR) glasses to everyone. She dons a pair of gloves that will help her interact with the screens everyone sees. Through voice commands and hand gestures, she moves through her presentation. What she sees, everyone else sees. What she changes on her screen simultaneously changes for everyone else.

When a board member asks a question about a particular subset of data, instead of saying that she will have to go back to her data and look into it, she can show the visualization and answer instantaneously by moving her hands and speaking commands. To end her presentation and to really cement her findings linking coal production to pollution problems, she uses the AR glasses to make it look like the room they are in is filled with smoke and dust particles. The board members cannot see each other or even their hands in front of their faces. The compelling experience helps them realize they must cut back on production or change the way their factory works, so this “smoke-filled” room does not become reality. Jane has achieved her goal with the use of AR. Using AR to successfully communicate their data-driven stories is something all data scientists could benefit from.

By providing an interactive and meaningful way to tell the story of the data, AR can potentially alleviate data scientists’ last mile problem. Similarly, data science skills can be used to enhance augmented reality.[12] Hard skills such as understanding machine learning, user interfaces and user experience, and digital modelingare just some of the skills needed to develop complex AR applications.[13] No matter if they use AR to communicate data or use data to enhance AR applications, data scientists need to understand and build their knowledge of how AR works.


            AR will continue to develop and become a fundamental part of how humans view the world. While AR has many useful applications, its dark sides cannot be ignored. These dark sides must be addressed by both lawmakers and makers of AR in order for AR to be more widely adopted.

Augmented reality (AR) can be more than just games and 3D modeling; it also can be used to provide compelling visualizations to tell the story of data like never before. In this regard, AR can help alleviate the last-mile communication problem for data scientists and business users alike. Finally, if data scientists can add the understanding and knowledge of AR to their “tool belts,” they will differentiate themselves with their cutting-edge skills.

[1] Iqbal, Mansoor. “Pokémon GO Revenue and Usage Statistics (2020).” Business of Apps, 24 Mar. 2020,

[2]Porter, Michael E., and James E. Heppelmann. “Why every organization needs an augmented reality strategy.” Harvard Business Review (2017): November/December.

[3]Liu, Shanhong. “Global Augmented Reality Market Size 2025.” Statista, 13 Dec. 2019,

[4] Berinato, Scott. “Data Science and the Art of Persuasion.” Harvard Business Review, 2019,

[5] Tokareva, Julia. “The Difference Between Virtual Reality, Augmented Reality And Mixed Reality.” Forbes, Forbes Magazine, 2 Feb. 2018,

[6]Garcia, Ahiza. “Google Glass Lives on in the Workplace. The Latest Pair Costs $999.” CNN, Cable News Network, 20 May 2019,

[7]Porter, Michael E., and James E. Heppelmann. “Why every organization needs an augmented reality strategy.” Harvard Business Review (2017): November/December.

[8]Porter, Michael E., and James E. Heppelmann. “Why every organization needs an augmented reality strategy.” Harvard Business Review (2017): November/December.

[9]Basu, Tanya. “The Dark Side of Augmented Reality Is Immersive Misinformation.” Inverse, 6 Oct. 2016,

[10] Basu, Tanya. “The Dark Side of Augmented Reality Is Immersive Misinformation.” Inverse, 6 Oct. 2016,

[11]Berinato, Scott. “Data Science and the Art of Persuasion.” Harvard Business Review, 2019,

[12] Meredith, Joel. “Here’s How Big Data Is Transforming Augmented Reality.” SmartData Collective, 8 Feb. 2019,

[13] Porter, Michael E., and James E. Heppelmann. “Why every organization needs an augmented reality strategy.” Harvard Business Review (2017): November/December.