How to Benefit From Big Data Analytics: 7 Real-World Examples
90% of the world’s data has been created in the past 2 years, and businesses spend more than $180 billion annually on big data analytics.
Since our first ancestors began writing on parchment, data has been an integral part of the human experience. From monitoring the complex movements of the planets to more fundamental tasks such as bookkeeping, data has shaped our evolution. Today, due to the internet, software development companies collect such vast quantities of data that we have coined a new term for it: “big data.”
Big data is not only captured online, but the Internet is also its most abundant source. From social media likes to emails, weather reports, and wearable devices, enormous quantities of data are created and accumulated every single second of every single day. But how is it used? Let’s discover the way that leading brands are taking advantage of big data analytics.
What is big data analytics?
Big data analytics is the process of identifying trends, patterns, and correlations in massive quantities of unstructured data to facilitate data-driven decision-making. These processes employ well-known statistical analysis techniques, such as clustering and regression, to larger datasets with the aid of more recent tools.
Big data has been a buzzword since the early 2000s when software and hardware advancements enabled organizations to manage large amounts of unstructured data. Since then, new technologies, such as Amazon and smartphones, have significantly increased the quantity of data available to organizations.
For the storage and processing of big data, early innovation initiatives such as Hadoop, Spark, and NoSQL databases were developed in response to the data explosion. This field continues to develop as data engineers seek to integrate the enormous quantities of complex data generated by sensors, networks, transactions, smart devices, and other sources. Even now, big data analytics methods are combined with emerging technologies such as ML to uncover and scale more intricate insights.
Tools for big data analytics
Big data analytics cannot be reduced to a singular technique or tool. Instead, multiple types of tools collaborate to help you collect, process, cleanse, and analyze big data. The following are some of the main players in big data ecosystems.
Hadoop
Hadoop is an open-source infrastructure for storing and processing large datasets on commodity hardware clusters. This framework is free and capable of handling large quantities of structured and unstructured data, making it an indispensable component of any big data operation.
NoSQL databases
NoSQL databases are non-relational data management systems that do not require a fixed schema, which makes them an excellent option for large, unstructured, unprocessed data. NoSQL stands for “not only SQL,” and these databases support various data models.
MapReduce
MapReduce is an essential Hadoop framework component that serves two functions. The first step is mapping, which filters data to various cluster nodes. The second is reducing, which organizes each node’s results in response to a query.
YARN
YARN stands for “Yet Another Resource Negotiator.” It is another component of Hadoop’s second iteration. The cluster management technology facilitates task scheduling and resource management within the cluster.
Spark
Spark is an open-source cluster computing framework that provides an interface for programming entire clusters using implicit data parallelism and fault tolerance. Spark supports both batch processing and stream processing for rapid computation.
Tableau
Tableau is an end-to-end data analytics platform that enables the preparation, analysis, collaboration, and dissemination of big data insights. Tableau excels at self-service visual analysis, enabling users to ask novel questions of governed big data and easily share these insights throughout the organization.
How leading brands made Big Data a part of their core business
1. Amazon
Amazon is currently the leading online retailer, and they have their database to thank for that. They are constantly utilizing big data to enhance the customer experience, so here are 2 examples that demonstrate how effective this strategy is.
- Dynamic pricing
Everyone is aware that airlines use this strategy when selling plane tickets. If you repeatedly check out the same tickets, it likely means you want them and are willing to pay more. Amazon’s website utilizes the same logic. However, you undoubtedly didn’t know that their prices change up to 2.5 million times per day.
Factors such as purchasing patterns, the prices of competitors, and the product’s popularity influence these price changes.
- Product recommendations
Amazon will use this information regardless of whether a customer purchases a product, adds it to their shopping cart, or simply views it. In this way, they can learn what each customer desires and likes and recommend the same or a similar product when the customer returns.
This accounts for 35% of the company’s annual reviews.
2. Netflix
Netflix has more than 231 million subscribers and accumulates information on each one. They monitor what people view, when they watch it, the device being used, whether or not a show is paused, and how quickly a user completes a series.
They even capture screenshots of repeated scenes. Why? By integrating all of this data into their algorithms, Netflix can generate personalized user profiles. These enable them to customize the experience by recommending movies and television programs with remarkable precision. While you may have read articles about how Netflix spends a lot of money on new shows, this is not done blindly; the data they collect helps them determine what to commission next.
3. McDonald’s
Big data analytics is not only employed to personalize online experiences. McDonald’s is an excellent example of this, as they use big data analytics to shape offline aspects of their offering as well. This includes their mobile app, drive-thru, and digital menus.
McDonald’s obtains vital information about user habits through its application. This allows them to offer customized loyalty rewards to encourage customer retention. In addition, they capture data from each restaurant’s drive-thru, which enables them to ensure there is sufficient staff on shift to meet demand.
Lastly, their digital menus offer various options based on variables such as the time of day, the presence of nearby events, and even the weather. Expect to be offered a McFlurry or a cold drink on a hot day, but not a spicy burger.
4. The Marriott hotels
The hospitality industry has been expanding in recent years and will continue to expand. Let’s take a look at how Marriott Hotels, one of the largest hotel chains in the world, uses big data analytics to generate more revenue and acquire more loyal customers. Marriott is one of the industry leaders.
Similar to Amazon, Starwood Hotels (one of the Marriott hotel brands) also use dynamic pricing. This tactic resulted in a 5% increase in revenue per room based on variables such as the local and global economic situation, weather, availability and reservation behavior, and cancellations.
They utilize data so thoroughly that they track when famous musicians perform at Madison Square Garden to adjust the prices of adjacent hotels.
To improve the customer experience, they have also begun testing facial recognition check-ins, which appears to be a win-win situation: their visitors no longer have to wait at the front desk, and the hotel collects even more valuable information. Amazon Echos were installed in the guestrooms as a second data-gathering measure; this enables guests to have Alexa perform tasks that were previously performed by the reception staff.
Now, guests can access all the information they desire, while Marriott gains insight into their customers’ preferences, requirements, and possible concerns.
5. UPS
Logistics companies use big data to expedite operations by monitoring warehouse stock levels, traffic reports, product orders, and more. UPS is a good example. UPS learned the quickest routes for their employees by monitoring weather and truck sensor data.
After analyzing the data in greater depth, they made an intriguing discovery: by turning left across traffic, drivers were wasting a significant amount of fuel. As a result, UPS implemented a “no left turn” policy. The company claims it now consumes 10 million fewer gallons of petroleum annually and emits 20,000 fewer tonnes of carbon dioxide. Impressive work indeed!
6. Uber
In urban mobility, big data analytics is a huge business, from car rental agencies to the boom of e-bike and e-scooter rental. Uber is a prime example of a business that has fully realized the potential of big data analytics. First, because they have a large database of drivers, they can quickly match users with the nearest driver.
However, it does not end there. Uber stores information for each journey taken. This allows them to predict when the service will be the busiest, allowing them to adjust their prices accordingly. What’s more, by pooling data from across the locations they operate in, Uber can analyze how to avoid traffic jams and bottlenecks. Cool, right?
7. Accuweather
All the companies mentioned above use their data internally, but can you transform your big data into a service? Accuweather did exactly that.
In the past, their only partners were global brands, but they realized that many other companies could also benefit from their weather data. Therefore, they created a website where developers could purchase API keys and implement them in their projects/businesses.
Sum Up
Certainly, not every trend is worth pursuing, but big data analytics is no longer just a trend. If you want to genuinely understand your clients and take your business to the next level, this is a tried-and-true software development solution that works.
Today, to increase sales, you must be able to truly attend to your customers and value the information they provide. And with the development of machine learning, even smaller businesses can utilize Big Data analytics to enhance their operations.
However, with all the previously mentioned elements growing easier and more available, the influx of data sources and ways to analyze and present data has made the process of defining the right business strategy around big data more difficult — but that is the topic for the whole new article.
