Companies That Use Big Data Technologies

Even with financial uncertainties, 56% of data leaders chose to raise their budgets for big data and analytics initiatives.

This is due to improved decision-making, operational efficiency, and enhanced customer understanding. About 69% of companies cite better strategic decisions, and 54% point to improved control of operational processes as key benefits of big data.

This article explores how industry leaders leverage big data to transform their business models.

Let’s get into it.

Walmart

Walmart was one of the first companies to integrate big data on a large scale. The retailer began using data analytics in the early 1990s by introducing Retail Link.

When it was launched, the system primarily focused on optimizing Walmart’s supply chain. It collected data from Walmart’s point-of-sale systems and made it accessible to suppliers. So, merchants could track how their products were selling and adjust production and distribution accordingly.

Back then, the company had the highest sales rates per square meter. It achieved the fastest product turnover and boasted an operating profit of $22.8 billion.

Today, Retail Link is a sophisticated, AI-driven system. In particular, it provides suppliers with the following capabilities:

• Sales performance tracking to monitor their product sales across all Walmart locations.
• Inventory management to track inventory levels in real-time for managing replenishment and avoiding stockouts or overstock situations.
• Demand forecasting to predict future demand based on historical sales data, seasonal trends, and other external factors.
• Store-level information to tailor product offerings and marketing strategies to geographic regions or store types.

Walmart Luminate

However, Retail Link is being gradually phased out by Walmart Luminate, a suite of products within Data Ventures. It provides merchants and supplier partners with insights on channel performance, shopper behavior, customer perception, digital landscape, and more.

Let’s look at one of the cases.

A Walmart merchant introduced a larger bleach bottle to appeal to price-sensitive customers. They aimed to expand the category without affecting other brands’ sales. The seller used Walmart Luminate Shopper Behavior tools to analyze performance and customer behavior. The data revealed that the category saw a 9% growth as 54% of buyers were new to this group of goods.

The seller obtained these insights thanks to the data Walmart collected.

Walmart Connect

Walmart Connect, fueled by big data from Walmart Luminate, helps build meaningful connections with buyers. This platform uses audience targeting based on customer behavior to improve the relevance of media campaigns, further driving product and category sales. As a result, Walmart’s marketplace and advertising platform have seen significant growth—40% year-over-year as of 2023.

Private cloud & data hub

Walmart has built the world’s largest private cloud to support its operations, which processes 2.5 petabytes of data every hour. It also drives Data Café.

The Data Café is an analytics hub located at the company’s headquarters in Bentonville, Arkansas. It processes information from stores and external sources (more than 200 data streams in total) to help merchants solve their business problems. This center processes around 25 thousand requests per hour. It takes only two seconds to analyze 90% of those requests.

Netflix

Netflix is a brilliant example of companies that use big data. Since it has been long enough in the streaming business, it has stacked up loads of user data.

And thanks to this very data, no two dashboards are the same at Netflix. That’s because teams of data engineers analyze what and when we’re watching, search patterns, pause behavior, and so on. All to make each dashboard match your interests.

To achieve this, teams of data engineers deploy the Recommendation Engine.

Netflix Recommendation Engine

The recommendation system is responsible for 80% of the content people watch on the platform. In fact, Netflix estimates that without its Recommendation Engine, it could face over $1 billion in annual losses from subscribers leaving the service.

Netflix’s Recommendation Engine (NRE) uses various algorithms to filter content based on user profiles. It analyzes over 3,000 titles using 1,300 recommendation clusters to match content with individual preferences.

Here are the key algorithms and techniques employed:

• Collaborative filtering. If users with similar tastes enjoy a movie, the algorithm suggests it to others with comparable profiles.
• Content-based filtering. This method suggests movies similar to those a user has already watched or liked. It analyzes metadata (genre, director, actors, and thematic elements) of a movie to recommend similar content to users.
• Reinforcement learning. The algorithm adjusts its recommendations based on how users interact with the platform, refining its suggestions and marketing tactics over time.

Netflix organizes recommendations into rows (“Trending Now” or “Continue Watching,”) with each row personalized in three ways: the row’s name, the titles shown, and the order they appear. For this, the system tracks various user interactions:

• How long you watch
• Your viewing history
• The time of the day you watch
• Ratings
• What you search and how often
• Device usage
• Whether you pause, rewind, or fast-forward a show
• If you resumed watching after pausing
• Title information (genre, actors, release year)

This detailed personalization helps Netflix maintain a 93% retention rate, much higher than competitors—Hulu (64%) and Amazon Prime (75%).

But there are other ways Netflix makes use of customer data. To be specific, they use it to produce original shows. As of spring 2024, its originals have won 22 Oscars in 13 categories from 132 nominations.

Originals production

Netflix’s big win at the Emmys for The Crown shows just how successful its original content strategy has been. With Disney+, HBO Max, and Peacock competing for attention, Netflix has been boosting its spending. In 2024, Netflix is going to put roughly $17 billion into content.

Back in 2012, Netflix realized that traditional studios would eventually keep their content for their streaming services. And it started preparing early. By 2020, originals made up 37.8% of Netflix’s content budget, and it’s planning to push that to 50% by 2025.

So, how exactly is this company using big data?

1. Analysis. Netflix looks at trending genres, themes, actors, or directors to identify potential ideas for new shows. For instance, data from Weeds led them to greenlight Orange is the New Black because it had a similar fan base​.
2. Predicting success. Netflix uses predictive analytics to determine whether a show will resonate with its audience. For example, the decision to produce House of Cards was backed by data showing a high likelihood of success due to its star power and genre appeal​.
3. Marketing. The company shows personalized trailers and promotional content to different user groups.
4. Post-release tracking. Netflix tracks how many people watched a show, how long they watched, and whether they paused or rewatched certain scenes.

Marriott International

Marriott International runs over 8,300 hotels in 139 countries under 30 hotel brands. To keep up with rising competition, Marriott turned to big data. With data-driven strategies, the company improved operational agility, enhanced customer satisfaction, and contributed to revenue growth.

Let’s look at how big data is used by this company.

1. Dynamic pricing. Marriott uses a Revenue Optimizing System (ROS) that integrates real-time data from internal and external sources to adjust room rates. For instance, pricing fluctuates based on events, weather, and demand in a particular location (during holidays or large local events). This system has led to a 5% increase in revenue per room​.
2. Personalized customer experience. Through its Marriott Bonvoy loyalty program, the company recommends hotel packages, travel experiences, and loyalty rewards. 200 million visitors have already joined the program. Marriott reports that its top 1% of loyalty members generate 35% of its gross fee revenue. 
3. Predictive analytics for expansion. To determine where to build new properties, the company analyzes guest demographics, travel frequencies, and local demand. For example, through data from partners, Marriott can predict which types of hotels (e.g., luxury or budget) would thrive in a given market.

To make things work, Marriott used Netezza and Hadoop with IBM’s BigInsights at first. But the setup was difficult to manage, expensive, and required a lot of infrastructure work.

So, they moved to Snowflake’s cloud-based platform. They also added real-time data with Kafka and used Snowpark for better management. The change paid off—data processing times dropped from 5 hours to 1, collaboration improved, user experience got better, and costs went down.

Uber Eats

When Uber Eats entered the food delivery market in 2014, it was already a crowded space. At the time, the global food delivery industry was valued at $30 billion, and Grubhub controlled more than 50% of the U.S. market. So, Uber Eats needed to differentiate itself in this saturated landscape. 

As of 2024, Uber Eats is the second-largest player in the U.S. food delivery market, holding 23% of the market share. Uber Eats has expanded its services globally and stands out for its integration with Uber’s driver network.

The company leveraged big data in several ways to carve out its market share:

• Delivery time estimation. Uber Eats uses real-time data to predict delivery times by analyzing traffic conditions, restaurant preparation times, and driver availability.
• Dynamic pricing. This delivery service applies surge pricing, adjusting delivery fees based on demand, location, and weather conditions.
• Personalized marketing. By analyzing user behavior, the company delivers tailored restaurant recommendations and promotions.

Uber Eats uses Apache Kafka, Spark, and Hadoop to process data in real-time. This allows to match drivers to orders and reduces processing delays.

How do companies use big data?

Based on the examples we provided, companies use big data to make their operations smoother, improve customer engagement, and shape business strategies.

For instance, Walmart uses it to manage inventory and predict demand, while Netflix tailors content recommendations and produces original shows. Marriott uses big data for dynamic pricing, adjusting room rates in real-time based on demand, and planning new hotel locations. Uber Eats applies data for delivery time estimates, surge pricing, and personalized marketing.

These real-world examples match the trends highlighted in industry reports. In particular, Why Companies Use Big Data Analytics by BI Survey. Those organizations that use big data report better decision-making (69% of businesses) and improved control over operations (54%). Many also see better customer insights and personalization, as seen with Netflix and Marriott.

Beyond the examples mentioned, big data is used in more ways. Businesses rely on data for new product development and innovation. Take Netflix, for instance, which uses viewer data to guide decisions on new original content. Meanwhile, many organizations are working to create data-driven cultures, where data isn’t just for analysts—it’s accessible across the entire company to help everyone make smarter decisions​.

Conclusion

Across industries, companies that embrace big data are seeing real benefits—higher profits, increased customer retention, and streamlined operations. Walmart, Netflix, Uber Eats, and Marriott—we’ve seen how big companies use big data.

But not every company is there yet. In fact, only about 30% have fully integrated big data into their processes. That’s a huge missed opportunity.

So, what’s next?

It’s time to take action. If you’re a business leader, assess where your company stands with big data. Maybe it’s time to invest in better analytics tools. Or upskill your team. Even partnering with data experts could be the key to moving forward.

Whatever the case, now’s the moment to embrace data and turn it into a competitive edge. After all, the future is data-driven, and the sooner you start, the better positioned you’ll be for long-term success.