Top 7 Big Data Applications with Examples in Real Life

Can you imagine the market value of big data analytics is expected to reach over $655 billion which is nearly double the value in 2023? Yes, Patterns and correlations hidden in massive data are no longer tedious to analyze and process. All thanks to Big Data! Big Data is one of the most powerful innovations in almost every industry. It plays a key role in planning future products, services, and whatnot. Approximately, 97% of businesses are investing in Big Data by 2022. Within just a decade it has grown to such a level that it has almost entered each aspect of our lifestyle like shopping, transportation, healthcare, and routine choices.

Big Data Applications with Examples in Real Life

The article will enhance knowledge on the practical use of Big Data applications in day-to-day real-life examples. This will give a better understanding of Big Data and its uses.

Big Data Application in Marketing

In the past, marketers emphasized TV, newspapers, and survey responses to ascertain customer’s responses to marketing campaigns. However, times have changed with evolving technology and companies now buy or gather a high volume of customer data to understand their behavior. Big data and marketing work in alignment with one another. Companies analyze past buying trends and consumer information to forecast buyer habits, market trends, and market moves. Here are some real-life examples of Big Data in marketing.

• 3Q Digital
  At 3Q, Big data supports strategies that blend social, video, mobile marketing, and search engines. The decision teams study data on transactions and consumer behavior by using multi-touch attribution. The technique backed by Big Data allows analysts to differentiate between effective and ineffective marketing channel strategies even at a small level.
• DISQO
  DISQO offers products to measure brand and customer experience by taking insights from big data. The company has a specialization in marketing or sales efforts and offers optimization software to track key performance metrics. Approximately 125 firms utilize DISQO research tools for making smart marketing decisions.
• Amazon
  Amazon implements big data analytics to accumulate massive data on customer purchases, payment preferences, and delivery methods of its customers. The company then sells ad placements that are used for targeting specific customer groups and subgroups. Amazon analyzes purchase history, customer feedback, and browsing history to identify common issues and address them.

Big Data Application in Transportation

Big data controls the powerful GPS application in the smartphone on which most of us rely to get from one place to another in minimum time. GPS data sources include government agencies and satellite images. Airplanes generate massive data of around 1000 gigabytes for transatlantic flights. Aviation analytics consume all the data to analyze passenger or cargo weights, fuel efficiency, and weather conditions that promise travelers’ safety.

• Uber: Uber monitors its data through big data analytics to predict demand for demand for drivers and driver availability. The company utilizes the information set pricing for rides and offers incentives to drivers. Data analysis helps the company to keep pace with the demand and basis of estimated arrival times and time to reach a destination. It forms the basis of customer satisfaction up to an extent.
• Vizion: Vizion utilizes multiple data sources to keep track of thousands of containers, ships, ports, and railways for freight companies around the world. It uses geocodes for facilities and locations that provide GPS coordinates to shippers, logistics service providers, and cargo owners across rail and ocean.

Big Data Application in Government

Government agencies collect huge volumes of data and analyze it using big data analytics applications. It helps the government to gain insights on financial procedures, tax theft, and legislation to help authorities in the best allocation of resources.

• FBI or Federal Bureau of Investigation: The FBI implements big data analytical strategies to monitor and identify large purchases of commercial fertilizers, dark websites related to human trafficking, and social media activities of terrorist groups. The FBI also actively monitors suspected images, videos, and audio files to determine hostile-state cyberattacks.
• RapidDeploy: A public safety company, RapidDeploy, creates reporting software for emergency call centers. It uses big data to get location accuracy and situation awareness. The company’s products offer analytical insights on how to find callers faster, reduce response time, and improve emergency service.
• FDA (Food and Drug Administration): FDA applies big data techniques across food testing labs to investigate patterns of any foodborne illness which is a major concern. Likewise, the Centers for Disease Control tracks the spread of infectious diseases using data analysis from a specific region.

Big Data Application in Healthcare

Big data is making a significant impact in the healthcare industry. Various sensors and wearable devices collect patient data which is fed into patients’ electronic health records.

Big data in healthcare is mainly used for predicting epidemic outbreaks, maintaining electronic health records, preventing serious medical conditions, and analyzing medical images appropriately.

• Tempus AI: Tempus AI has made medical records of patients portable and no longer requires pen or paper for that. It records huge clinical data, radiology scans, and genomic data that turn out an asset in the healthcare industry.
• Sophia Genetics: Sophia Genetics provides data solutions to medical practitioners based on big data metrics specializing in inherited diseases and oncology. It draws patterns from millions of data records of the patients that guide healthcare professionals to provide relevant remedies.

Big Data Application in Cybersecurity

Big Data analytics play a key role in cyber security by immediately identifying unusual web patterns of users suspecting cyber fraud. It is a great defense mechanism against cyber-attacks.

• Splunk: Splunk’s security system relies on big data to identify and respond to cybersecurity fraud. Data flows through its analytical tools that pinpoint anomalies with machine algorithms. Data analysis also helps to prioritize breaches, avoid multipart attacks, and identify causes of security problems.
• OwnBackup: OwnBackup is a cloud-based platform for data security, backup, and backup. The software provides automated security risk mitigation and backups using insights from big data metrics. It has partnered with AWS and Veeva to provide compliance services to businesses worldwide.

Big Data Application in Education

Students and teachers both benefit from big data analytics. Big data enables institutions to tailor academic programs according to the needs of individual students. Predictive data analysis gives an insight into a student’s real performance, their responses to programs, and the way they apply learning in real life. Big data has made voice-based learning that makes learning fast.

• Appic Software: Appic Software applies big data for creating e-learning apps and education software to make learning easy. It helps the teachers to understand students’ behavior based on their understanding of software. This helps the teachers to cater to specific problems of students. Online tests help in analyzing overall performance based on past data. Chatbots in the software allow students to resolve queries.

Big Data Application in Media and Entertainment

Using a huge volume of data and analysis, media and entertainment companies gain insights into what customers prefer to view or hear. Data helps in analyzing customer patterns and preferences. Big data enables companies to understand why users subscribe or unsubscribe. It helps in creating valuable promotional and product strategies to attract customers.

• Spotify: Spotify applies predictive data analytics to predict and forecast behaviors like the chances of a song becoming popular, optimize their marketing campaigns, and understand the likelihood of a user taking or canceling the subscription. Spotify’s AI models recommend playlists, podcasts, and music content to the users.
• Netflix: Netflix uses a robust machine learning algorithm alongside big data analytics to assess huge volumes of user data. The company utilizes data to understand users’ viewing habits, preferences, and ratings. It personalized content recommendations that increase customer satisfaction.

Big Data Application in Banking Sector

Big data has transformed various industries, and the banking sector is no exception. Here’s how big data applications are revolutionizing the banking industry:

• Fraud Detection: Banks are leveraging big data analytics to detect fraudulent activities in real-time. By analyzing large volumes of transactional data and identifying patterns, anomalies, and suspicious behaviors, banks can prevent fraud before it occurs, saving billions of dollars annually.
• Customer Segmentation: Big data enables banks to segment their customers based on various criteria such as demographics, spending behavior, and financial preferences. This segmentation helps banks tailor their products and services to specific customer segments, improving customer satisfaction and loyalty.

Challenges and Consideration

Big data offers extensive solutions in almost every sector. However, it comes with challenges in implementing in real-time. The challenges demand immediate attention as failure to do so will fail data management. Here is a list of some significant big data challenges and considerations.

1. Sharing Data: One of the biggest challenges in big data is the inaccessibility of various data sets from multiple sources. Accessing data from public authorities is quite challenging as it needs legal documents at the inter-intra-institutional level. Until and unless accurate and complete information is available, companies cannot apply big data analytics and gain meaningful insights from them.
2. Security: Another challenge related to big data is privacy and security. Organizations spend more than a third of their big data budget on compliance owing to the risks related to big data security breaches.
3. High Infrastructure Cost: A limited IT budget is another big challenge in applying big data. Implementing big data is expensive as it requires careful planning and involves high data projects and infrastructure costs. As the volume of data increases, management costs increase which might not pay off quickly.
4. Scarce Talent: Lack of IT expertise in data management is another big challenge. The demand for data science specialists and analysts is accelerating day by day and exceeds the supply. There will be around 11.5 million data science jobs by 2026. This is because a greater number of companies are looking forward to investing in big data projects and competing for the best talent.
5. Slow Insight Time: Time to insight is how quickly you can gain insight from data before it becomes obsolete. The problem is that data becomes old quickly but ineffective data management and bulky data pipelines result in getting useful information. In many cases, even a small delay in data analysis will make it of no use.

Big Data Applications with Examples in Real Life – FAQ’s

What are the 3 Vs of big data ?

• Volume – It is the amount of data being collected.
• Velocity – It is the speed at which data is put into the system and the speed at which data changes or updates over time.
• Variety – It is the various formats in which data is available.

What are the different big data types?

• Structured data – It includes alphanumeric characters translated into that are translated into a format fed into a data model that is predefined.
• Semi-structured data – It has structure and organization to an extent but does not include rigid data schema.
• Unstructured data – It is found in a variety of formats like text, images, binary data, and audio files. It does not have a consistent schema or structure.

Name the architecture layers in big data.

The major architecture layers in big data include data sources, data collection, data storage, data processing, data Analytics, data visualization and data security and monitoring.

What is the difference between data and big data?

Traditional data sets were measured in giga or terabytes. However, big data is called so not only because of its size but also volume. Big data is measured in peta, zetta, or exabytes and is huge in volume.