Big data is revolutionizing the way businesses manage, analyze, and leverage data. Using big data technologies, organizations can perform manipulations on large volumes of data, uncover hidden patterns, gain insights, and make informed decisions that lead to greater ROI. Today, every company, whether big or small, is investing in big data applications to improve the quality of its services. Here are a few top big data applications in industries where big data is changing the face of businesses these days.
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Applications & Uses of Big Data
Evolving technology has extended the potential of using big data in every type of industry. Organizations of all sizes in various industries are using big data insights to make good strategic and operational decisions. Here are the top 5 domains where big data is used:
Banking, Financial Services, and Insurance (BFSI)
BFSI is one of the most data-intensive domains in the world economy. Financial institutions have huge amounts of customer data, such as information on customer profile data collected for KYC, deposits and withdrawals at ATMs, online payments, and more. Big data technologies enable financial institutions to easily access data and eliminate redundancy and overlapping.
The BFSI industry uses big data to efficiently use these rich data sets and become more customer-centric and profitable. Banking and finance institutions leverage big data technologies data sets to maximize customer understanding and gain a competitive advantage. Traders also use this technology for sentiment measurement and high-frequency trading.
Big Data use cases in the BFSI industry
- Improved levels of customer insight
- Customer engagement
- Fraud detection and prevention
- Market trading analysis
- Risk management
- New data-driven products and services
The retail industry collects a huge amount of data through RFID, customer loyalty programs, and more. Big data analytics enable businesses to uncover patterns and trends in a large volume of data to improve pricing, supply chain movement, personalized shopping experiences, and enhance customer loyalty. Businesses also use retail analytics data to forecast trends and make strategic decisions. This helps increase their competitiveness in the market to a great extent.
Big Data use cases in the Retail industry
- Personalized customer experience
- Predicting demands
- Dynamic pricing
- Customer journey analytics
- Fraud detection and prevention
Healthcare institutions gather a large amount of data in the form of patient details, physician’s prescriptions, medical imaging, lab reports, insurance, and other administrative data. Using big data, the vast amount of data can be stored systematically and easily accessed when needed.
Many healthcare institutions are using electronic health records (EHR) to gain a deeper understanding of patient disease patterns. Using big data, healthcare practitioners can access a wide range of data and make informed decisions related to the patient’s health, hospital performance, and more.
Big Data use cases in the Healthcare industry
- Improved patient predictions
- Real-time alerts
- Electronic Health Records (EHRs)
- Better patient engagement
- Fraud prevention and detection
- Smoother hospital administration
In the education sector, a lot of data is collected in the form of names of students enrolled in a program/course, enrollment year, course details, student ID, marks obtained in each subject, and more. Using big data, educators can store this information efficiently and identify patterns and trends to spot opportunities for positive change in the performance of both the students and the educational institutions.
Big data analytics help educators reveal trends in students’ behavior and their preferences to create customized programs. It also gives a base to evaluate the state of the entire education system.
Big Data use cases in the Education industry
- Create customized programs
- Improve student’s results
- Reduce dropouts
- Identify leaner’s strengths
- Data-driven decision making
Regardless of what type of data a business has, it plays an important role when it comes to outperforming the competition. In manufacturing, data is gathered from machines, devices, and operators at every stage of production. Big data help manufacturers store this data efficiently. The use of big data also allows firms to identify new ways to save costs and improve product quality. Using big data analytics, companies can find patterns to solve existing problems and improve the overall process.
Big Data use cases in the Manufacturing industry
- Customize product design
- Predictive quality
- Anomaly detection
- Better management of supply chain
- Production forecasting
- Yield improvement
- Risk evaluation
Must Read: Best Online Resources to Learn Big Data
In this blog, we explored the top big data applications across different domains. Big data is transforming the way enterprises operate and compete. The big data field is evolving fast and organizations cannot ignore its benefits. Big data technology is likely to bring about further disruption as its revolution works its way through big and small organizations across all industries.
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What are the benefits of big data for businesses?
Big data is a technology that helps businesses drive smart and sustainable change. Organizations of all sizes in different industries are adopting big data because of its numerous benefits. Some of the advantages of big data for businesses are Cost reduction, Improved efficiency, Improved products and services, Competitive pricing, and Better decision-making.
What are the disadvantages of big data?
Nowadays, many companies are leveraging the power of big data. However, some organizations face significant challenges while implementing big data. Some of the disadvantages associated with big data include Data quality, Need for expert talent, Need for cultural change, Compliance, Cybersecurity concerns, and Hardware needs.
What are some popular big data tools?
Big data tools enable users to manage large data sets and identify patterns and trends in them. Some of the popular big data tools include Hadoop, Cassandra, Storm, Cloudera, OpenRefine, Rapidminer, Hive, and Atlas.ti.
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