“You can have data without information, but you cannot have information without data.” – Daniel Keys Moran
When you think of big data, you usually think of applications related to banking, healthcare analytics, or manufacturing. After all, these are some pretty massive industries with many examples of big data analytics, and the rise of business intelligence software is answering what data management needs. However, the usage of data analytics isn’t limited to only these fields. While data science is a relatively new field, more and more industries are jumping on the data gold rush.
In this post, we will help you put the power of big data into perspective by offering a range of real-world applications of big data for multiple industries. Let's dive into it!
What Is An Example Of Big Data? Discover 14 Real World Success Cases
The best examples of big data can be found both in the public and private sectors. From targeted advertising, education, and already mentioned massive industries (healthcare, manufacturing, or banking) to real-life scenarios in guest service or entertainment.
What’s the motive? Well, as we will explore here, bustling entertainment and hospitality entities, including casinos, restaurants, and bars that are embracing the power of digital data, including it in their management reporting practice, and predicting customer behaviors and patterns, are reaping the rewards of increased efficiency, improved customer experiences, and ultimately, a significant boost in profits.
While these industries are traditionally slow in adopting innovations, some front-runners are leading the pack. And while a mere 22% of marketers state that they have a data-driven marketing strategy that is achieving significant results - by leveraging the right insights in the right way, success is inevitable. And when you consider that by the year 2025, 181 zettabytes of data will be generated, the potential for data-driven organizational growth in the hospitality sector is enormous.
Big data can serve to deliver benefits in some surprising areas. Here, we’ll examine 14 big data use cases that are changing the face of the entertainment and hospitality industries as well as other industries, including banking and education, while also enhancing your daily life in the process.
1) Big Data Is Making Fast Food Faster
The first of our big data examples is in fast food. You pull up to your local McDonald’s or Burger King and notice that there’s a really long line in front of you. You start drumming your fingers on the wheel, lamenting the fact that your “fast food” excursion is going to be anything but, and wondering if you should drive to the Wendy’s a block away instead.
However, before you have time to think about your culinary crisis too deeply, you notice that a few cars ahead of you have already gone through. The line is moving much quicker than expected… what gives? You shrug it off, drive up to the window, and place your order.
Behind the scenes
What you may not have realized is that big data has just helped you get your hands on those fries and burgers a little bit earlier. Some fast-food chains are now monitoring their drive-through lanes and changing their menu features (you know, the ones on the LCD screen as opposed to the numbers on the board) in response. Here’s how it works: if the line is really backed up, the features will change to reflect items that can be quickly prepared and served to move through the queue faster. If the line is relatively short, then the features will display higher-margin menu items that take a bit more time to prepare.
Now that’s some smart fast food.
2) Augmented Furniture Shopping
Next in our list of big data applications, we have an example from the furniture industry. You just moved into your dream apartment. You already have a vision of what you want your interior decor to look like, but you are not sure if all your crazy ideas will go together. You’ve been to dozens of shops, but you haven’t brought yourself up to buy anything because you are afraid it might not look great in your new space.
Until one day, a good friend of yours talked to you about the IKEA app. There, you can browse for your favorite furniture style and virtually place the objects in your house to see how they would look in person! And not just that, you can also choose from a range of wall colors to see what matches your style better. You can finally see how that beautiful sofa or coffee table will look without the need to buy and then return a bunch of items, all from the comfort of your own home.
Behind the scenes
IKEA has always been known for providing the best experiences for its customers. The retail giant uses both qualitative and psychographic data to understand its customer’s behaviors on a deeper level and offer them the best experience. For instance, they observed that most of their clients go to the store with their kids, often making it harder for them to shop. To solve this issue, they implemented supervised play areas so that parents could shop without distractions.
In 2017, the company wanted to take its shopping experience one step further by creating an augmented reality app that allowed users to test a product without leaving their homes. The app automatically scales products in real-time based on room dimensions with a 98% accuracy. The issue they faced at the moment was that people needed to close the app and go into IKEA’s shopping app or website to buy the desired product.
A few years later, with the advancement of AR technology, the retail enterprise decided to mutate its app into a new version called IKEA Studio. This time it reimagined the whole virtual experience by allowing users to plan an entire space with different pieces of furniture, shelving systems, decorations, and even wall colors. The designs can then be exported in both 3D and 2D to share with family and friends.
While this proved to be especially useful in COVID-19, using augmented reality powered by big data has also allowed IKEA to boost its sustainability actions. By preventing customers from driving to the store location and buying or searching for their needed items, the company can focus more on diminishing its environmental footprint by optimizing its shipment and packaging processes—definitely amongst the greatest big data applications in the modern shopping world.
3) Self-serve Beer And Big Data
Another great big data example in real life. You walk into your favorite bar. The bartender, instead of asking you, “What’ll you have?” hands you a little plastic card instead.
“Uhhh… what’s this?” you ask. He spreads his hands. “Well, the folks upstairs wanted to try out this new system. Basically, you pour all your own beer – you just swipe this card first.”
Your eyebrows raise. “So, basically, I am my own bartender from now on?”
The bartender snorts and shakes his head. “I mean, I’ll still serve you if you’d like. But with this system, you can try as little or as much of a beer as you want. Want a quarter glass of that new IPA you’re not sure about? Go right ahead. Want only half a glass of stout because you’re a bit full from dinner? Be my guest. It’ll all get automatically added to your tab, and you pay for it at the end, just like always.”
You nod, starting to get the picture. “And if I want to mix two different beers together”
“No,” the bartender says. “Never do that.”
Behind the scenes
You might think this scenario is from some weird beer-based science fiction book, but in reality, it’s already happening. An Israeli company by the name of Weissberger has enabled self-serve beer through two pieces of equipment:
- “Flow meters” which are attached to all the taps/kegs in the bar
- A router that collects all this flow data and sends it to the bar’s computer
By using this system, a lot of cool things can be made possible. For example, you can let customers pour their own beer in a “self-serve” style fashion. However, there are other profitable possibilities as well that come from the use of big data. Bar owners can use these flow meters to see which beers are selling when, according to the time of day, the day of the week, and so on. Then, they can use this data to create specials that take advantage of customer behavior.
They can also use this data to:
- Order new kegs at the right time since they know more accurately how much beer they are serving
- See if certain bartenders are more “generous” with their pours than others
- See if certain bartenders are giving free pours to themselves or their buddies
In Europe, the brewing company Carlsberg found that 70% of their beer sold in city bars was bought between 8-10 p.m., while only 40% of their beer sold in suburban bars was bought in that period. Using this data, they could develop market-specific prices and discounts.
Carlsberg also found that when customers were given a magnetic card and allowed to self-pour beer, they ended up consuming 30% more beer than before. This increased consumption came from customers trying small amounts of beer that they wouldn’t have bought before when they were limited to buying a full pint or larger.
4) Consumers Are Deciding The Overall Menu
Have you ever seen those marketing campaigns companies use where consumers help them “pick the next flavor?” Doritos and Mountain Dew have both used this strategy with varying levels of success. However, the underlying philosophy is sound: let the customers pick what they want and supply that!
Well, big data is letting customers speak even more directly (without having to go to a web page). An article titled “The Big Business of Big Data” examines some of the possibilities.
Behind the scenes
One of our big data analytics examples is that of Tropical Smoothie Cafe. In 2013, they took a slight risk and introduced a veggie smoothie to their previously fruit-only smoothie menu. By keeping track of their data, Tropical Smoothie Cafe found that the veggie smoothie was soon one of their best sellers, and they introduced other versions of vegetable smoothies as a result.
Things get deeper: Tropical Smoothie Cafe was able to use big data to see at what times during the day consumers were buying the most vegetable smoothies. Then, they could use time-specific marketing campaigns (such as “happy hours”) to get consumers in the door during those times.
5) Personalized Movie Suggestions On Netflix
Moving on with our list of industry examples of big data, we have streaming services. It’s finally Friday. You sit down on your couch after a hard work week, ready to watch a movie while drinking a beer or a glass of wine. You don’t know which movie or TV show to watch, but Netflix has you covered. The app offers a range of options from all your favorite genres based on what you usually like to watch. In just a few minutes, you have picked a perfect movie and are ready to start enjoying your night.
Behind the scenes
Being a large enterprise, Netflix deals with massive amounts of data from its over 150 million subscribers. With the streaming industry becoming increasingly competitive, the subscription-based company uses all this information to their advantage to offer targeted experiences to their customers. According to Data And Analytics Network, the data they collect includes:
- Viewing day, time, device, and location
- Keywords and number of searches
- The number of times you paused, rewound, fast-forwarded, and rewatched content
- Browsing and scrolling patterns
- And even how much time a user takes to finish a movie or a TV show
By applying a series of algorithms to the massive amounts of customer data they possess, Netflix can predict what the user will watch next and offer a range of options based on the aforementioned data. This method has proven to be very successful for Netflix, as 80% of the content being steamed is based on their recommendations algorithm.
But this is not all. Netflix also selects the cover image that is being shown in certain movies or TV shows, depending on the user profile. It does this by using Artwork Visual Analysis (AVA) “a collection of tools and algorithms designed to surface high-quality imagery from videos. It can predict which merchandising still will resonate most with individual users based on their age and general preferences”. Like this, you will likely see a different promotion on your favorite TV show than the one your mom or friend will see on their own profiles.
6) Big Data Makes Your Next Casino Visit More Fun
Another interesting use of big data examples in real life is with casinos. You walk into the MGM Grand in Las Vegas, excited for a weekend of gambling and catching up with old friends. Immediately, you notice a change. Those slot machines that you played endlessly on your last visit have moved from their last spot in the corner to a more central location right at the entrance. Entranced by fond memories of spinning numbers and free drinks, you walk right on over.
Behind the scenes
“Our job is to figure out how to optimize the selection of games so that people have a positive experience when they walk through the door… We can understand how games perform, how well guests receive them, and how long they should be on the floor.”
This quote is from Lon O’Donnell, MGM’s first-ever director of corporate slot analytics. An article titled “Casinos Bet Large with Big Data” expands on how MGM uses data analysis tools to measure performance and make better business decisions. Think about business from a casino’s point of view for a moment. Casinos have an interesting relationship with their customers. Of course, in the long run, they want you to lose more money than you win – otherwise, they wouldn’t be able to make a profit. However, if you lose a large amount of money on any one visit, you might have such a bad experience that you stop going altogether… which is bad for the casino. On the flip side, they also want to avoid situations where you “hit it big”, as that costs them a lot of money.
Basically, the ideal situation for a casino is when you lose more than you win over the long run, but you don’t lose a horrendous amount in any one visit. Right now, MGM is using big data to make sure that happens. By analyzing the data from individual slot machines, for example, they can tell which machines are paying out what and how often.
They can also tell things like:
- Which machines aren’t being played and need to be replaced or relocated
- Which machines are the most popular (and at what times)
- Which areas of the casino pull in the most profits (and which areas need to be rearranged)
7) We Missed You!
The next of our examples of companies using big data applies to restaurants. Imagine this: you’re relaxing at home, trying to decide which restaurant to eat at with your spouse. You live in NYC and work long hours, and there are just so many options. The decision takes longer than it should; you’ve had a long week, and your brain is fried.
Suddenly, an email arrives in your inbox. Delaying your food choices for a moment (and ignoring the withering glare of your spouse as you zone out of the conversation), you see an email from Fig & Olive, your favorite Mediterranean joint that you were a regular at but haven’t been able to visit in more than a month. The subject line says, “We Miss You!” and when you open it, you’re greeted with a message that communicates two points:
- Fig & Olive is wondering why you haven’t been in for a while.
- They want to give you a free order of crostini because they just miss you so much!
“Honey”, you exclaim, “I know where we’re going!”
Behind the scenes
The 7-unit NY-based Fig & Olive has been using guest management software to track their guest's ordering habits and to deliver targeted email campaigns. For example, the “We Miss You!” campaign generated almost 300 visits and $36,000 in sales – a seven times return on the company’s investment into big data.
8) The MagicBand
The MagicBand is almost as whimsical as it sounds, as it’s a data-driven innovation that’s been pioneered by the ever-dreamy Disneyland.
Now, imagine visiting a Disneyland park with your friend, partner, or children and each being given a wrist device on entry - one that provides you with key information on queuing times, entertainment start times, and suggestions tailored for you by considering your personality and your preferences. Oh, and one of your favorite Disney mascots greeting you by name. It would make your time at the park all the more, well, magical, right?
Behind the scenes
With an ever-growing roster of adrenaline-pumping rides, refreshment stands, arcades, bars, restaurants, and experiences within its four walls - and some 58 million people visiting its various parks every year - this hospitality brand uses big data to enhance its customer experience and remain relevant in a competitive marketplace.
Developed with RFID technology, the MagicBand interacts with thousands of sensors strategically placed around its various amusement parks, gathering colossal stacks of big customer data and processing it to not only significantly enhance its customer experience but gain a wealth of insights that serve to benefit its long-term business intelligence strategy, in addition to its overall operational efficiency - truly a big data testament to the power of business analytics tools in today’s hyper-connected world.
9) Checking In And Out With Your Smartphone
These days, a great deal of us humans are literally glued to our smartphones. While once solely developed for the making and receiving of calls and basic text messages, today’s telecommunication offerings are essentially miniature computers, processing streams of big data and breaking down geographical barriers in the process.
When you go to a hotel, often you’re excited, meaning you’ll want to check into your room, freshen up and enjoy the facilities, or head out and explore. However, sluggish service and long queues can end up seriously eating into your time. Moreover, once you have passed the check-in desk, you risk losing your key - creating a costly and inconvenient nightmare.
That said, what if you could use your smartphone as your key, and what if you could check in and out autonomously, order room service, and pre-order drinks and services through a mobile app. Well, you can at Hilton hotels.
Behind the scenes
At the end of 2017, the acclaimed hotel brand rolled out its mobile key and service technology to 10 of its most prominent UK branches, and due to its success, this innovation has spread internationally and will be included in its portfolio of 4,000 plus in the near future. In addition to making the hotel hospitality experience more autonomous, the insights collected through the application will help make the hotel’s consumer drinking and dining experience more bespoke.
This cutting-edge big data example from Hilton highlights the fact that by embracing the power of information as well as the connectivity of today's digital world, it’s possible to transform your customer experience and communicate your value proposition across an almost infinite raft of new consumer channels.
And, as things develop, we expect to see more hotels, bars, pubs, and restaurants utilizing this technology in the not-so-distant future.
10) A Nostalgic Shift
Amusement arcades were all the rage decades ago, but due to the evolution of digital gaming, many traditional entertainment centers outside the bright lights of Sin City simply couldn’t compete with immersive consoles, resulting in a host of closures.
But with a sprinkling of nostalgia and the perfect coupling of old and new, you might have noticed that the amusement arcade has somewhat of a renaissance. It seems that those who grew up in a time when arcades reigned supreme are craving a nostalgic trip down memory lane, taking their children for good old retro family experiences. You might have also noticed if you’re amongst those people, that while there are all of the offerings you remember as a child, there are a sprinkling of cutting-edge new amusements and tech-driven developments that make the whole experience more fun, fluid, and easy to navigate.
Behind the scenes
A shining example of an amusement arcade chain that has stood the test of time is an Australian brand named Timezone.
By leveraging the big data available to the business, Timezone gained invaluable insights into customer spending habits, visitation times, preferred amusement, and geographical proximity to their various branches. By gathering this information, the brand has been able to tailor each branch to its local customers while capitalizing on consumer trends to fortify its long-term strategy.
Speaking to BI Australia, Timezone’s Kane Fong, explained:
“By leveraging the big data available to the organizations, Timezone gained invaluable insights on customer spending habits, visitation times, preferred amusement, and geographical proximity to their various branches. In gathering this information, the brand has been able to tailor each branch to its local customers while capitalizing on consumer trends to fortify its long-term business strategy.”
11) Reducing school drop-outs with big data
As you’ve learned from the previous examples, big data has permeated several industries, and the education system is no exception. For decades, academic institutions have tried to give their students the best environment and tools to complete their courses successfully. However, despite their best efforts, college dropout remains among the biggest challenges for colleges in the United States and across the globe.
In fact, according to recent research, the USA experiences 40% of college dropouts, with only 41% of students graduating after four years without delay. Due to such conditions, educational organizations started to turn to big data to find a solution to these concerning rates. That is where “Course Signals” from Purdue University was born. A system to predict which students are more likely to not complete their courses.
Behind the scenes
In 2007, Purdue University generated a predictive model that analyzed individual student grades, demographic information, historical academic performance, and more to help teachers and administrators identify students who are at risk of not finalizing their courses at an early stage. The system categorized each student within a “risk group” with colors red, yellow, and green, mimicking traffic sign lights.
To use the system, the teacher needs to manually run it and analyze the student signals to offer personalized feedback and resources to those struggling the most. What makes this initiative so valuable is that the feedback can be provided in real-time, starting from the second week of a course, meaning students can receive the support they need from the beginning. It is believed that the predictive model helped Purdue achieve a 21% increase in retention of students who took at least one course using the system. A successful application of big data in education!
12) Enhancing the musical experience at Spotify
The next real-life example of data analytics comes from Spotify. The way we consume music has changed throughout the years thanks to the rise of new technologies. In just a few years, we jumped from owning our favorite artist's CDs to listening to their new albums on our iPods or other portable devices. Today, it is all about streaming music in popular apps such as Spotify or Apple Music, which compete day to day to offer the best experience to their users.
Imagine you are about to take a road trip. You connect your phone to your car speaker through Bluetooth but realize you are tired of listening to the same 50 songs on your playlist. You would love to meet some new artists in the same genres you usually listen to but don’t know where to start looking. Your friend, who is currently sitting next to you in the car, tells you to look at your Spotify’s “discover” feature. Speechless, you go into your app and find a complete section suggesting new artists and playlists with music based on your own preferences. Happy with this discovery, you spend the next three hours listening to some awesome new music!
Behind the scenes
For years now, Spotify has put improving customer experience at the center of their work. In 2012, they launched their “Discover” feature, which offered new artists, songs, and playlists to users based on their historical preferences. Eventually, the feature mutated into “Discover Weekly”, which offered the same format but every week. This brought immense value for music lovers who wanted to explore new tunes and artists to listen to but also opened the doors for several smaller artists to a wider audience. Within the first five years of implementation, users spent 2.3 billion hours listening to music from the Discover Weekly playlists.
This is just one of the multiple initiatives Spotify has developed using big data. Among some of the most popular ones, we can find “Wrapped”, which gives users a roundup of their year through the music they listened to. Every December, all Spotify users can see how many hours of music they listened to that year, their favorite artists, and the most listened-to song, among other things. Through the years, “Wrapped” has turned into the most expected event as users share their findings on their social media. One of the best examples of data analytics in daily life!
13) Wimbledon improves fan experience with data
Moving on with our examples of big data in everyday life, we will cover sports. It's finally time to take a holiday, so you and your partner decide to visit London. While searching for fun activities, you notice that, during your stay, the Wimbledon tennis championship will be carried out. You are not precisely an expert in the sport, but after the pandemic, you developed a new interest in it, and this is the ultimate event to go to for any fanatic. So, you decide to purchase two tickets.
After a long wait, the day is finally there. You are sitting at Centre Court to watch an exciting match. The issue is you are not really that familiar with the players, and you wonder who is more likely to win and other relevant information. So, you decide to go into their website and encounter “Win Factor”, a section that provides fans with all the necessary data to follow the match, get to know the players, and even make predictions about who you think will win. The section is so complete that you decide to recommend it to other friends who also like tennis so they can follow it in real-time with you. The experience ends up being everything you expected!
Behind the scenes
In 2022, after the pandemic and a Netflix hit show increased interest in F1, many other sporting industries felt challenged to increase the fan experience and engagement. This is what happened at Wimbledon, where organizers realized that many of the fans that were attending the event didn’t know a lot about the players and did not watch other tennis matches the rest of the year, putting a toll on engagement. That is how the idea to develop Win Factor came to life.
Win Factor is a tool that aggregates data from several different sources to offer fans a range of stats, including strengths and weaknesses about players, match predictions, profiles for rising stars, and much more. The tool was generated as an effort from the organization to get fans “closer to the sport” and increase their level of engagement. In fact, fans can even make their own predictions based on the information they just learned, making it even more exciting for them.
The idea was developed by the Wimbledon organization in collaboration with a long-term partner of 33 years, IBM. The technology giant uses artificial intelligence to gather detailed insights online and in the court to provide fans with this enhanced experience. It is definitely a great initiative to boost this traditional and widely regarded sport!
14) Personalized coffee at Starbucks
Last but not least, in our list of examples of big data analytics, we have an application related to everyone's favorite drink, coffee. You are an avid Starbucks drinker. After various weeks of collecting stars in their Rewards Program, you are finally entitled to your free reward. Since you are in a good mood, you decide you want to try something new. However, you have been drinking the same black coffee for the past five years and don’t know what to try next. So, you decide to open your Starbucks app and find a range of recommendations that, surprisingly, you think you’ll like. Decided, you go into your closest store and get a free iced coffee that you really enjoy because it is a hot summer day. You never got one of those before and are happy you decided to try something new.
Behind the scene
With an estimated 90 million weekly transactions in their 25,000 stores worldwide, Starbucks is an undisputed leader in the industry. That is because they have been able to boost customer experience and engagement through the use of various big data-related initiatives. The most popular and successful is their reward program.
Starbucks's reward program is integrated into the company’s app, allowing users to gather stars whenever they buy a product. Customers who manage to gather around 100 stars can collect a reward, including free drinks or pastries. What makes this program so successful is the fact that, from the 17 million Starbucks App users, 13 million are using the program, allowing the company to gather massive amounts of data regarding customer preferences. The company uses that data to then offer personalized suggestions to customers about what new products they could try using a complex cloud-based AI engine, going as far as suggesting products based on the customer's current location, that day’s weather, or if it's a holiday.
The company also uses big data to determine new store locations. They do this by using mapping and online BI tools, like datapine, to determine proximity to other Starbucks locations, demographics, traffic, and more. So, if you are wondering if two stores that are very close to each other compete, the data has already told them they won’t. A great big data in business example!
Key Takeaways From Big Data Applications
Big data is changing how we eat, drink, play, and gamble to make our lives as consumers easier, more personal, and more entertaining.
What’s even more amazing is that we’re only at the beginning of the adoption of big data in the hospitality and entertainment industries. As we as humans evolve the way we gather, organize, and analyze data, more incredible big data applications will emerge in the near and distant future. We are living in exciting times.
This also permeates into the business area, where organizations of all sizes turn to powerful online data analysis tools to boost their data-driven efforts and ensure sustainable growth.