15+ Innovative Big Data Applications Transforming Industries

Are you tired of hearing the phrase "big data" thrown around like a buzzword at every tech conference and in every business meeting? Well, the truth is, big data in 2020 was just a trendy term but today it's a game-changer. Think of it like a superhero, swooping in to save businesses from the clutches of ignorance and uncertainty. From healthcare to finance, this data revolution is transforming industries and changing the way we live our lives. And while working with massive datasets can certainly be a challenge, the rewards are well worth the effort. So, if you're ready to join the big data bandwagon (or maybe just curious about what all the fuss is about), come along with us on this exciting journey to explore big data applications across industries. Who knows, you might just find yourself feeling like a data superhero too!

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Characteristics of Big Data Applications

The following features characterize the applications of big data:

• Volume: Big data applications involve processing and analyzing vast amounts of data.

• Variety: The data comes in various forms and formats, such as structured data, semistructured data, and unstructured data.

• Velocity: The data is generated and collected at high speeds and needs to be processed in real-time or near real-time.

• Veracity: Big data may have questionable accuracy or reliability and require thorough validation and cleansing.

• Value: Big data aims to extract insights and value from the data to drive informed decision-making.

Benefits of Big Data Applications 

Big Data applications offer numerous benefits across various industries and sectors. Here are some of the main advantages of Big Data applications:

• Improved Decision-Making: Big Data applications provide businesses with valuable insights and analytics to help them make more informed and accurate decisions. 

• Increased Operational Efficiency: Big Data applications can help businesses optimize their operations and processes by identifying inefficiencies and areas for improvement. 

• Better Customer Experience: Big Data applications can help businesses gain insights into customer's behavior and preferences. 

• Cost-Savings: Big Data applications assist businesses in saving money by enhancing their operations, minimizing errors, and increasing efficiency. 

List of Big Data Application Examples Across Various Industries 

Companies across different industries now leverage Big Data to gain valuable insights into their business operations and improve decision-making. Big data applications have proven to be a game-changer from healthcare to finance, education to retail. So, let’s check out the big data applications examples in different industries.  

• Application of Big Data in Healthcare

• Big Data Applications in Finance

• Big Data Applications in Retail 

• Big Data Applications in Education Industry 

• Big Data Applications in Agriculture 

• Other Unusual Big Data Applications in Real-World 

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Applications of Big Data in Business

Big data is crucial for businesses, providing valuable insights and enhancing their decision-making processes. Here are some of the common big data applications in business: 

• Predictive Analytics: Predictive analytics involves using big data to predict future events or behavior. Business users can make informed decisions about potential future outcomes by analyzing past data and identifying patterns. This information can be used to improve customer experience, optimize processes, and create new products or services. 

• Risk Management: Big data can be used for risk management to detect and prevent fraud, assess and manage supply chain risks, and assess and manage operational risks. Companies can identify and proactively mitigate potential risks, significantly improving risk-handling processes.

• In-Depth Customer Insights: Big data can help businesses generate insights to identify patterns and trends in customer behavior and preferences. These insights can personalize marketing campaigns, improve customer experience, and develop new products or services. 

• Better Decision Making: Big data can also improve decision-making processes. It enables businesses with real-time insights and analytics to make better decisions based on data rather than intuition. 

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Applications of Big Data in Healthcare 

The applications of big data analytics in Healthcare are numerous, from improving diagnostic accuracy to optimizing treatment plans. So, let’s explore the typical big data applications in Healthcare below: 

Real-Time Monitoring and Diagnostic Support 

Big data is transforming healthcare in many positive ways. It enables doctors and other medical professionals to constantly monitor their patient's health using cutting-edge technology and equipment. This allows them to detect any changes in their patient's health and provide prompt medical care, ultimately reducing the cost of healthcare.

The data gathered from IoT devices and mobile health apps support remote patient monitoring and telemedicine, providing medical personnel with necessary health information for risk assessment and treatment prescription.

For example, the ECG app on the Apple Watch uses a convolutional neural network to detect atrial fibrillation by analyzing the patient's heart signals. Similarly, the Automated Device for Asthma Monitoring and Management (ADAMM) can predict asthma conditions by monitoring respiratory signals through wearable sensors. IBM's stream computing platform can monitor multiple sensor data in neonatal intensive care units, alerting doctors and nurses to potential health issues before they become critical.

Facilitating Medical Research 

Big data has become a crucial tool for facilitating medical research, enabling researchers to gain valuable insights and develop more effective treatments for various diseases. It is transforming how medical records are collected and analyzed, leading to exciting advancements in healthcare.

Apple's ResearchKit allows researchers to develop apps and conduct medical studies using data from wearable devices, such as the Apple Heart Study, the ASSIST Study, and the DETECT Study, which use data to learn about Alzheimer's disease and detect early COVID-19 symptoms. 

Genomics, the basis for precision medicine and drug discovery, also generates large amounts of data that require advanced analysis methods. Companies like BGI and Genomics England conduct large-scale studies to discover the reasons behind diseases and identify individual responses to various agents. Genetic testing has become more common, with newborns tested for phenylketonuria and cystic fibrosis.

In addition, data from wearable devices are being used to predict and diagnose medical conditions, such as diabetes and cardiovascular diseases, through projects like DeepHeart. Cancer research also benefits from big data, with initiatives like Project GENIE, CancerLinQ, and The Cancer Genome Atlas Program using genomics data to study cancer types and improve diagnosis and treatment.

Managing Mental Health Conditions 

Big data can also help manage mental health conditions by analyzing relevant data from social media, wearable devices, and other sources to identify psychological markers associated with particular mental conditions. Speech elements, such as the frequency of using possessive nouns, can also provide hints about mental disorders. 

One excellent application of big data in mental health is suicide prevention. Crisis Text Line uses big data and machine learning models to prioritize people who need help, as the demand often exceeds staff capacity. This algorithm analyzes the unstructured text messages from individuals to identify patterns and markers that indicate a higher risk of suicide. Surprisingly, certain words like "Ibuprofen" and "Advil" were found to be 14 times more predictive of suicide than commonly associated words like "die" or "cut". Similarly, the crying-face emoji was 11 times more predictive of suicide than the word "suicide" itself. This helps therapists to enhance the quality of care and potentially prevent tragic outcomes in individuals' lives by utilizing big data and AI to comprehend psychological indicators and forecast the risk of suicide.

Big Data Applications in Finance

The finance industry has realized the potential of big data in transforming the big data industry. From banking to investment management, big data applications have proven to be highly successful in providing valuable business insights and enhancing decision-making processes. So, let’s check out the successful applications and examples of big data in the finance industry. 

Risk Management and Fraud Detection 

Financial institutions, including banks and insurance companies, harness big data's power to mitigate operational risk and combat fraud while achieving regulatory and compliance objectives. The use of big data in risk management and fraud detection has significantly reduced information asymmetry problems, which were once a challenge for these institutions.

One of the key benefits of using big data in risk handling and fraud detection is the ability to access real-time data. Banks can use real-time data to identify potentially fraudulent activities. For example, if two transactions are made through the same credit card within a short time gap in different cities, the bank can immediately notify the cardholder of security threats and even block such transactions. This helps to prevent losses due to fraud and enhances the customer experience by providing proactive security measures. 

Similarly, insurance companies can access a wide range of data, including social media, past claims, criminal records, and telephonic conversations, beyond the claim details while processing a claim. This helps them identify any suspicious activities and flag them for further investigation. This approach allows insurance companies to prevent fraudulent claims, which can help them reduce costs and improve customer satisfaction.

Alibaba is one of the organizations that have built a fraud risk monitoring and management system based on real-time big data processing. It uses machine learning to analyze massive amounts of user behavior data in real time to identify bad transactions and capture fraud signals. This approach has helped the company to tackle fraud effectively and reduce losses due to fraudulent activities.

Customer Data Analytics 

Banking and financial companies use big data to gain insights into customers' behavior and preferences. They analyze customer data to learn what customers like and use that information to develop new products and services that meet customer needs. These insights can improve customer satisfaction, generate sales leads, and use new channels and technologies.

For example, Oversea-Chinese Banking Corporation (OCBC) analyzes vast amounts of historical customer data to gain a deep understanding of individual customer feedback and preferences. This enables the company to design an event-based marketing strategy focused on personalized, coordinated marketing communications across multiple channels, including text messages, email, ATMs, and call centers.

Investment Analysis 

Investment analysis is one of the big data applications in the financial domain. Several financial industries use advanced algorithms and machine learning to analyze large amounts of historical and real-time data to automate and produce more reliable results. This helps investors make informed decisions based on scientific analysis.

One excellent example of how big data can be utilized in investment analysis is through algorithmic trading, where mathematical formulas help bankers make more accurate and profitable data-driven investment decisions.

The financial services industry has been able to provide innovative solutions for predictive analytics, risk modeling, and customer engagement through the use of big data applications. Predictive analytics can help identify potential risks and opportunities, risk modeling can provide insights on the probability of risks occurring and their potential impact on investments, and customer engagement can be improved by analyzing customer data to offer personalized investment recommendations and improve overall customer satisfaction.

As the financial markets embrace big data technologies, we expect to see further advancements in investment analysis and more profitable investment decisions.

Big Data Applications in Retail

Retailers use big data analytics to collect, analyze, and interpret large volumes of data to gain insights into customer behavior and preferences, optimize supply chains, and make informed decisions. So, let’s explore the applications and examples of big data in the retail industry, highlighting how retailers leverage data to gain a competitive edge and enhance their operations. 

Measuring Emotions 

Emotional measurement technology is innovative using big biometric data such as facial recognition, fingerprint scans, and retina scans for big data analysis. This technology can be used to judge the reaction of audiences to different advertisements by measuring people's emotions on how they interact with a company’s brand or service- to leverage it for big data analytics and facilitate profitable business decision-making.

Affectiva uses emotional measurement technology i.e. facial recognition technology to analyze the huge amount of videos and photos based on the feelings and mood of the people featured in them. Wild Blue Technologies uses Affectiva’s big data analytics solution to develop a video for Hershey that could be displayed in retail stores. When customers in the retail store watch the video and smile at the screen, the display dispenses free Hershey’s chocolate samples to them by analyzing the customer’s mood. 

Customer Journey Analytics 

Customer journey analytics is an excellent area where big data has proved particularly valuable. It involves analyzing every touchpoint a customer has with a company, which helps businesses identify pain points, optimize interactions, and improve the overall customer experience.

Let's consider a popular e-commerce company specializing in clothes and accessories. They have a vast customer base and must understand how customers interact with their platform. They have a website, a mobile app, and social media platforms, and customers can buy products online or in-store.

The company uses big data analytics to track the entire customer journey. They collect big data from every interaction point, including website visits, clicks, searches, purchases, returns, and customer service calls. The data collected is then processed and analyzed to identify patterns and trends. After analyzing the data, they found that customers abandoned their carts due to high shipping costs. They introduced a free shipping threshold, increasing sales and fewer abandoned carts. They also optimized their inventory management based on customer search data and introduced a notification feature for out-of-stock products.

Another excellent example is when Netflix uses big data analytics to recommend TV shows and movies to users based on their past viewing history.

Predicting Demands 

Big Data has significantly changed the retail industry by helping retailers gain valuable business insights into consumer behavior. With the massive amount of data generated from consumers' interactions with products and services, retailers can analyze patterns and trends to make informed decisions about what products to offer and how to market them.

One example of how Big Data has been applied to predict demand is the collaboration between Pantene, Walgreens, and The Weather Channel. The Weather Channel collected data on the humidity level in the air and when it would be at its highest. This information was used to predict the impact of humidity on consumers' emotions and behavior.

Pantene and Walgreens used this information to advertise their hair products, highlighting how they could help women prevent hair problems caused by humidity. This targeted marketing campaign resulted in a 10% increase in Pantene sales at Walgreens during July and August and a 4% increase in sales across the entire hair care category.

Big Data Applications in Education Industry 

The use of big data techniques in education enables educators to gain valuable insights into student performance and make data-driven decisions to improve the learning experience. From personalized learning to predictive analytics, big data applications in education have the potential to transform the way we teach and learn. Listed below are the most essential applications of big data in education: 

Better Grading System 

Using big data in education enables educators to track and analyze students' performance. The statistical analysis of individual grades helps educators understand the areas of interest and proficiency among students. 

For instance, Institutions can use big data tools, such as Socrative, Nearpod, and Classroom Monitor, to observe students' real-time performance and behavior. For example, Ashford School in the UK has implemented such tools and has kept an improvement in their students' performance.

Gaining Attention 

The use of Big Data applications in education has brought about a new way of analyzing students' behavior and their level of engagement during lectures. Big Data experts have developed innovative ways to capture biometric data that can help teachers understand the level of attention among their students. 

Using biometric data such as heart rate, facial expressions, and objects touched during a lecture is an excellent idea. This data can be captured using devices such as smartwatches or cameras on the ceiling. The data gathered can then be analyzed to provide insights into students' engagement levels.

Blended Learning 

One of the most significant applications of big data in education is the ability to offer blended learning. Blended learning combines online and offline learning, allowing students to access study materials and lectures online while also providing offline guidance from professors. This approach enables students to study at their own pace and follow classes they are interested in..

MOOCs (massive open online courses) are a great example of blended learning offered worldwide. With MOOCs, students can access a wide range of courses from top universities across the globe. For instance, the Machine Learning class at Stanford taught by Andrew Ng was attended by approximately 400 students, but when offered online on Coursera, more than 100,000 students participated.

Big Data Applications in Agriculture 

Let us now explore the key applications of big data in agriculture and their benefits to the industry. 

Big Data Applications for Supply Chain Optimization 

Big data applications are critical to supply chain optimization in agriculture. They enable farmers and suppliers to manage resources better, minimize waste, and improve efficiency. 

One of the significant benefits of big data in the agricultural supply chain is its ability to track the movement of goods and products from farm to table. Additionally, big data can optimize fleet management software, ensure delivery reliability, reduce transportation costs, and offer advanced mapping of animal and vehicle locations. For instance, GPS-oriented analytics can help identify the most efficient routes for transporting goods, minimizing travel time and fuel consumption.

Precision Farming 

The use of big data is becoming increasingly important in precision farming, allowing farmers to make informed decisions based on real-time data and historical trends. Precision farming involves utilizing technology and data to optimize crop yields and reduce waste.

SMAG InVivo, a French agronomy company, has developed a Data Crop algorithm based on 30 years of weather data history, satellite and drone images, and soil types. This algorithm helps farmers to optimize their decision-making in planting, fertilizing, and harvesting. 80% of French agricultural land under wheat cultivation is managed through Data Crop, and SMAG plans to expand this to other crops and countries. 

Weather Prediction 

Weather prediction is crucial for the success of agricultural production, as natural conditions such as climate, pests, and weather significantly impact crop yields. However, accurately predicting weather patterns can be challenging, as it involves analyzing vast amounts of data from multiple sources. Thus, utilizing big data for weather prediction helps farmers gain valuable insights into expected weather patterns and adjust their farming practices accordingly. With the help of intelligent farming software, farmers can monitor changes in weather conditions in real time and respond to any deviations from expected patterns.

For example, sensor data from the sensors in soil and images taken by drones can provide farmers with valuable information that can help them establish expected growth rates. When an intelligent system knows what to expect, it can automatically detect anomalies or deviations and warn farmers of them. This can help farmers take preventive measures to protect their crops and ensure that unexpected weather patterns do not negatively impact them.

Unusual Big Data Applications in the Real-World 

Big data has applications in almost every aspect of modern business. However, there are also a number of unusual and innovative applications of big data that go beyond the traditional use cases. So, let’s explore them below: 

Parking Lots 

Modernizing intelligent transport systems in any smart city is only possible with smart parking solutions. Smart parking is a big data analytics solution that uses big real-time data and reliable information from mobile automated payment systems to help people find an available parking lot and pay for it.

“Smarking Drives Big Data to Parking Industry” -TechCrunch

Smarking ‘s smart parking big data solution designed for parking lots uses big data analysis and cloud technology to help parking staff make the most of the available space and maximize pricing by analyzing the usage pattern of the parking lots and various other influencing factors such as flight schedules, special events and changing weather conditions.

Every time the entry gate goes up to permit entry of a car inside the parking lot, a payment is made, and then again, the gate goes up for the car's exit; all this involves a data trail. The date and time the car enters the parking lot, the amount paid for parking, the method of payment, and the time the car exited- all this data is collected and recorded to leverage big data analytics.

The smart parking solution by Smarking is helping parking management organizations and airports in real-time tracking and predictive analytics, leading to effective revenue optimization and staffing. According to the CEO of Smarking- Wen Sang, close to 20% of the airport’s revenue is generated from parking. Smarking has partnered with Boston’s Logan Airport in the past few months to help Logan increase its revenue by $10 million next year. 

Finding Your Next Music Icon 

Big data analytics will predict who will be your next big rock star. When an individual watches Eminem videos on YouTube or tweets about their famous album, it is very likely that some company will be using this big data to process the information and try to find out what probably can be the next big hit in the pop music industry.

The big data solutions by The Next Big Sound can predict the probability of a pop star entering the Billboard 200 list a year in advance. This helps people in the music and entertainment industry decide whether investing money in a certain artist is worth it.

The Next Big Sound analytics solution can help music artists analyze their popularity on social network platforms. For instance, an artist with a Facebook, Twitter, and Instagram account can leverage big data analytics to determine where they must focus their efforts. If the Twitter following for an artist is the largest, Facebook following is growing fastest, and Instagram following is highly engaged- a pop artist can choose to launch a marketing campaign on Instagram as the artist will get maximum engagement from there.

“Typically, how our biggest clients have used it is as a kind of monitoring system – they can look at the impact of the various social promotions they are doing, see how an album is selling or how it is streaming, and what all this activity online around the artist is.”- says Liv Buli, data journalist at The Next Big Sound.

With The Next Big Sound's big data analytics solution, artists can look at how they are growing and determine the best ways to keep moving with the popularity. Artists can become superstars by leveraging the right big-data solutions to predict success. 

Saving Rare Animal Species

Poachers are always on the hunt for endangered species of animals like Indian Tigers to manufacture medicines from their bones. Poachers know all the characteristics of a tiger’s habitat, and tracing them without big data in action would be challenging. Tons of images captured by motion-sensitive cameras are being analyzed through various image recognition software to help find the numbers and environment of the most endangered species.

Animals in the wild are difficult to track, and the only way this can be done is by leveraging a rudimental tracking system. HP recently announced its partnership with Conservation International (CI) to create Earth Insights through big data analytics. 

The big data analytics system is designed to create an early warning system for the animals belonging to the group of endangered species. Using sensors and cameras, the analytics system will collect data from close to 1000 devices and then collate it to find out the population of each species. The collected big data is then fed into the HP Vertica platform, which analyzes the readings precisely to find regions or locations of endangered species. This helps the conservation department invest time and money in specific areas to save endangered species. 

Big Data- The Master Chef of the Kitchen

A professional master chef spends several years mastering their specialized cuisine by experimenting with various ingredients - big data does this in just a few minutes.

“Meet Chef Watson, IBM’s futuristic foodie robot”-May 12, 2015 Washington Post

IBM’s Watson computer in collaboration with the Institute of Culinary Education, New York has paved for an innovative cookbook “Recipes for Innovation” which is the talk of the food industry. This cookbook contains mouth-watering delicious food pairings that would not be possible for the chefs to discover independently.

Some of the unusual food pairings that IBM Watson has identified are Brussels sprout with Cardamom, Creole shrimp lamb momos, Mushrooms with Strawberries, Viet Thai Green Curry Poutine, and Plum Pancetta.

IBM Watson consumes tons of unstructured data from – recipes, chemical compounds, food pairings, academic studies, tweets, and books. This unstructured data is then analyzed to detect patterns humans cannot find.

“Watson amplifies human creativity. It’s a collaboration that allows Watson and the chef to discover more than either could independently.”- says Steve Abrams, an IBM engineer from the Watson team.

Future of Big Data Applications 

The future demands of big data applications are bright and promising. With the ever-increasing amount of data generated every second, big data has become critical for businesses and organizations to gain insights, make data-driven decisions, and stay competitive. One of the most significant trends in the future of big data applications is the integration of machine learning (ML) and artificial intelligence (AI) technologies. With the help of AI and ML, big data applications can analyze large volumes of data in real-time and make predictions or recommendations based on the data patterns. This can lead to improved decision-making, enhanced customer experiences, and increased efficiency in various business processes.

Moreover, big data applications are also becoming more accessible and user-friendly. With the advancements in cloud computing, big data platforms are now available on-demand, making it easier for businesses of all sizes to adopt and implement big data applications. 

Working on big data projects is crucial for aspiring data scientists and analysts. It provides an opportunity to gain hands-on experience handling large data sets, developing models, and analyzing data using various tools and techniques. So, if you're looking for resources to strengthen your big data skillset, ProjectPro is an excellent place to start. They provide over 270+ solved end-to-end data science and big data project solutions with source codes. In addition, they also offer guided video explanations and unlimited one-on-one expert sessions with top industry experts. So, check the ProjectPro repository to explore these projects and take your big data skills to the next level.

FAQs on Big Data Applications 

1. What are the applications of big data?

Big data has a wide range of applications in varied industries, including Healthcare, Finance, Retail, Education, and more. The major applications include improving customer experiences, fraud detection, increasing operational efficiency, streamlining resource management, optimizing supply chain management, and enhancing medical research. 

2. What are the three major types of big data applications?

The three major types of big data applications are batch processing, stream processing, and interactive processing. 

3. What is big data mostly used on?

Big data is primarily used in healthcare, finance, banking, retail, agriculture, transportation industry and other government agencies. 

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