Key Big Data Applications which are in Limelight Forever
In this article, we will see the Big Data Applications in Different Sectors. The article provides you the complete guide about the uses of Big Data in different cases.
In this article, you will explore challenges faced by different sectors like finance, healthcare, government, education, manufacturing, retail, and more. The article describes how Big Data helps different industries to handle challenges.
Let us first see a short introduction to Big Data and Big Data Analytics.
Big Data refers to the rising data from variable sources like social media, internet, IoT devices. This rising Big data is of different formats and can not be handled with traditional processing systems.
Big Data Analytics
Big Data Analytics is the process of storing, transforming, analyzing, and processing Big data to generate valuable insights.
Companies are moving ahead towards Big Data technologies for Big Data analytics to defeat their competitors and achieve growth. There are many Big Data analytics tools available in the market such as Apache Hadoop, Apache Spark, Kafka, etc.
Let us now explore the Applications of Big Data in different sectors.
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Big Data Applications in Different Domains
No industry is left behind the use of Big Data. Big Data has expanded its roots to all the major domains like finance, retail, education, government, manufacturing, energy and utilities, and many more. Below we have enlisted some major sectors using Big Data.
1. Big Data Applications in Banking and Finance Sector
Challenges in industry: Some of the challenges that Finance sectors faced are:
- Securities fraud early warning.
- Card fraud detection.
- Enterprise credit risk reporting
- Tick analytics
- Social analytics for trading.
- Customer data transformation.
- Archival of audit trails.
- IT operations analytics.
1. Real-time stock market insights:
Machine learning with Big Data enables finance sectors to analyze stock prices by considering social and political trends that might affect the stock market.
It allows monitoring of trends in real-time thus providing the capability to analysts to evaluate appropriate data and make smart decisions.
2. Fraud detection and prevention:
ML fueled by big data is significantly responsible for fraud detection and prevention. The security risk possessed by the credit card gets mitigated with analytics.
When information about any secure and valuable credit card gets stolen, banks instantly freeze the card and transactions. It then informs the customer about the security threats.
3. Trade analytics:
Banks, Hedge funds, and others in the financial markets are using Big Data for trade analytics. Trade analytics helps finance and banking sectors in pre-trade decision support, high-frequency trading, Predictive Analytics, and sentiment measurement.
4. Risk Management:
Financial organizations utilize Big Data for gaining a comprehensive view of the total cost of risk. It allows companies to optimize the return on their investments.
5. Increased revenue and customer satisfaction:
Companies use big data analytics to understand client payment behaviors. This helps them to know their customers which results in increased revenue and customer satisfaction.
2. Big Data Uses in Communications, Media and Entertainment:
Challenges in industry: Some of the challenges that Communications, Media and Entertainment industry faced are:
- Leveraging social media and mobile content.
- Collecting, analyzing, and using consumer insights.
- Understanding real-time media content usage patterns.
Big Data Use in Communications, Media and Entertainment Industry
1. Predicting audience interest:
Big data helps companies to understand the patterns between customer’s TV watching decisions. It provides information about customer search history, how long customers watch videos, use social media, how customers respond to trailers, ratings, etc.
Analyzing big data helps media and entertainment industries to develop and market the new programs as per their audience interest.
2. Improved ad targeting
For any media brand, Advertisements are the fundamental form of revenue. Advertisers are investing lots of money for ads between show segments. Big Data enables companies to create highly-targeted ads.
3. Measure content performance:
Big Data helps designers, writers, and media executives to create content based on the mathematical model of becoming popular.
Companies with big data platforms easily predict content success instead of operating on gut feelings. Big Data helps in predicting storylines, apps, actors.
3. Big Data Uses in Healthcare sectors:
Challenges in industry: The medical sector can access huge amounts of data but they are plagued by failures in using data to reduce the rising healthcare costs and provide better healthcare benefits. It is due to electronic data which is unavailable, and inadequate thus unusable.
1. Predictive Analytics in Healthcare
Big Data plays a vital role in Predictive Analysis in healthcare. Predictive analysis of Patients’ medical histories allows doctors to provide excellent treatments to their patients. It ensures the patient’s safety.
The analytics tools predict which patient is having the risk of what disease thus enabling doctors to make decisions accordingly leading to improved patient’s health.
2. Electronic Health Records (EHRs)
Electronic Health Records are the main use of Big Data in the medical sector. Earlier, healthcare sectors were facing challenges while dealing with the rapidly growing patient record. Now, every patient is having his/her own medical records such as lists of medicines, medical reports, laboratory tests results, etc.
Electronic Health Records make it easier for the healthcare sectors to maintain patients’ data and have access to that data. A separate file is maintained for each patient. The file can be easily modified from time to time by the doctor and can be shared safely.
3. Real-Time Monitoring
Healthcare Systems are now offering excellent treatments to their patients by monitoring their health in real-time. There are many tools available that analyze patient data and suggest doctors take respective actions.
There are many wearable sensors that track patients’ health like blood pressure, pulse rate, heartbeat, etc. which can be monitored by the doctors. It reduces patients’ unnecessary visits to hospitals.
4. Big data can help cure cancer
Medical researchers use Big data analytics to see the recovery rates of a patient suffering from cancer and plan the treatment accordingly which have the highest rates of success.
They do so by linking patient’s databases from various health institutions keeping patient data confidential. This approach is not limited to cancer. They are using it for other diseases as well.
4. Big Data Applications in Education
Challenges in industry: The significant challenge in the education sector is to incorporate big data originated from various sources and vendors. It is difficult to utilize big data on the platforms not designed for the data of different formats.
Maintaining confidentiality of big data used for educational purposes.
Big Data in Education:
1. Enhance the Grading system:
With the rise of Big Data, educational institutions can monitor their student’s performance around various disciplines at an individual as well as collective level. They can develop relevant solutions to provide assistance for the student progress.
The statistical analysis of student grades in different subjects helps institutions in guiding them study plans according to student studying capability. They can find out the factors affecting student’s performance and provide effective solutions through data analytics.
2. Career guidance to students:
Big Data helps students to choose the right career options. Based on the student area of interest and performance in respective subjects, teachers can advise students about the career path they should opt for.
Big Data expands the student’s knowledge of career options. It enables institutions to provide career guidance to their students.
3. Proposing new learning plans:
Students have different learning capabilities. Some can learn just by reading, some learn by writing things. Some students learn things by watching videos, or by some other methods.
It often happens that the student forcibly has to adapt a defined learning structure which hinders his growth. This has an adverse effect on the student’s academic performance.
Big Data Analytics helps educational institutions to build different learning plans based on students’ capabilities. Big data Analytics understands the student’s strengths and weaknesses and provides relevant study plans and learning material.
5. Big data Applications in Manufacturing and Natural Resources:
Challenges in industry:
- Large volumes of untapped data from manufacturing industries.
- Increase in the volume, variety, complexity, and velocity of data due to rising demands of Natural resources.
- Underutilization of data prevents improved quality, reliability, energy efficiency, and better profit margins.
Big Data in Manufacturing and Natural Resources
In natural resources industries, Big Data analytics helps in predictive modeling to improve decision making. It is used for ingesting and integrating large data from geospatial data, text, and temporal data.
The use of Big Data solutions in manufacturing improves product quality, tracks defects in the manufacturing process, and supply scheduling.
Big Data solutions help manufacturing industries in expanding energy efficiency, predicting output, testing new manufacturing processes, and in developing new manufacturing processes.
6. Big Data Applications in Government sectors:
Challenges in industry: The biggest challenge for the government departments is the integration and interoperability of vast amounts of data from different government-affiliated organizations and government departments.
Big Data in Government Sector:
Big data also plays an important role in the government sectors. The government needs to manage various procedures and solve complex tasks. Thus they require a platform that is capable of handling vast amounts of data.
The rising big data technologies provide a smart platform for collecting, cleaning, filtering, and analyzing big data. Local governments, federal agencies use big data tools to optimize their work and improve security, law, public sector, defense, etc.
Big data technologies are playing a major role in fields like defense, national security, crime prediction, cybersecurity, etc.
Big Data plays an enormous role in the field of Defense. Governments all over the world use real-time analytics to track and monitor the borders.
They use analytics for creating multiple video streams from various objects and watch over the navy and air spaces. The introduction of big data and machine learning algorithms improve the performance of several military departments.
In the era of the digital world, cybersecurity becomes a major issue at the government level. The government uses big data analytics and machine learning tools for monitoring and examining all the operations which involve critical data.
The system monitors the devices and users on the network and detects any suspicious individual or actions and generates alerts. It enables states to take more operative, secure, and reliable measures to prevent cyber attacks.
Challenges in industry: In retail industries, from traditional retailers and wholesalers to e-commerce traders, the amount of data gathered increases day by day. This large amount of data is generated from customer loyalty cards, RFID, POS scanners, etc.
But before big data analytics, these huge data are not used efficiently to improve customer experiences. Any improvements and modifications made are quite slow.
Big Data in Retail industry:
1. Predicting Spending
The retailers collect customers’ data through credit card transactions, user log-ins, IP address and more. Retailers then analyze the historical data of customer spending and the flow of shopping. This helps them to predict customers’ future spending and provide personalized recommendations to customers.
Amazon also uses big data to provide recommendations to its customers based on customers’ past searches and purchases.
2. Personalizing Customer Experience
Big data provides opportunities for retailers to provide better customer experiences.
For example, When the California fruit company warned Costco about the possibility of contamination in peaches and plums, Costco uses its data collection to keep its customers healthy. Costco sent an email to specific customers who had purchased peaches and plums affected by the contamination.
3. Forecasting Demand in Retail
Retailers use big data and Machine learning algorithms to analyze social media trends to predict the next big thing in the industry.
Some brands like Pantene and Walgreens analyze weather patterns to customize product recommendations for their consumers. Due to this, Pantene and Walgreens product purchases have increased by 10 percent over two months.
8. Energy and utility industry:
Challenges in industry: Energy and utility industries are constantly applying smart technologies to their landscape, including cloud computing technologies, sensors, power planning, etc.
All these technologies produce vast amounts of data over a time period. Businesses across the energy and utility industry are facing challenges in drawing insights out of this valuable data.
Big Data in the energy and utility industry:
1. Dynamic energy management
Dynamic management systems are an innovative approach for managing the load. It covers all of the energy management principles concerning distributed energy sources, demand, and demand-side management, energy saving, demand reduction, and temporary load.
Big data analytics empowers dynamic energy management systems in Smart Grids. This highly contributes towards the optimization of energy flows between the consumers and providers.
Big Data analytics helps energy and utility industries to analyze the vast quantities of data collected by DEM systems. It provides excellent recommendations for energy management.
2. Smart utilities use smart data
In the energy and utility industry, smart grid, smart meters, and access to weather data provide tremendous opportunities. Energy and Utility industries can use Big Data and machine learning to optimize their business and improve customer experiences.
They improve their customer experiences by storing and processing the real-time streams of unstructured data.
9. Big Data in Transportation industry
Challenge in industry:
- Massive amounts of data from location-based social network sites and applications affects travel behaviour.
- High speed data from telecoms.
- Transport demand models are based on poorly new social media structures.
Big Data use in the Transportation industry:
1. Government services:
Big Data in transportation is used by the Government for traffic control, congestion management, and intelligent transport systems.
2. Private services:
Private sectors use big data in transportation for revenue management, technology enhancement, etc.
3. Individual use of Big Data in transportation:
Individuals use big data for route planning to save fuel and time. Big Data is also used for travel arrangements in tourism.
10. Big Data Applications in Insurance sector
Challenges in the industry:
- Lack of personalized pricing, services.
- Improper targeted services to new segments.
- Lack of targeted services to specific market segments.
- Underutilization of data collected by hungers of better insight and loss adjusters.
Big Data use in insurance sector:
1. Insurance industries use Big Data for providing customer insights for simpler products. They do so by analyzing data from GPS-enabled devices, social media, CCTV footage and predicting customer behavior.
2. Insure industries use Big Data for claim management, fraud detection, risk management, and real-time monitoring of claims.
After reading this article, we can easily see how Big Data is ruling the world through big data applications in various sectors. From retailers to government departments all are using Big Data to improve their decision making and customer experiences.
Big Data Analytics enables companies to gain valuable insights from the data collected. Companies are adopting Big Data technologies to mark their growth.