Big Data

Big Data

Big data! Big data need, tools, and techniques nowadays booming in every industry sector. Traditional data analytics tools and methodology do exist in the industry however these traditional data analysis tools/technology are not suitable for handling big data.

This blog post will help you to understand,

What big data is?

Why big data is important

Big data technologies

Application of Big Data

What big data is?

          Data exist in terabytes or petabytes, are huge in volume, wide in variety (text, picture, videos), and are generated and grow at high velocity (in milliseconds), which traditional data analytics software/tools cannot process and analyze.

         Eg. Millions of people use Google for information, data, and help by using keywords. Google handles huge data of keyword search, a number of the site most visited by users for provided keywords with time, demographics, age group, and type of gadget (cellphone/desktop).

          A data set can exist or generate in a different format. Eg. Structured Data, Semi-Structured data, and unstructured data.

what big data is

Why big data is important

If you can’t measure, you can’t improve! Big data analytics is an important and useful tool/methodology to improve business processes, understand customer needs and sales trends, improve efficiency, and many more.

Big data is used for varieties of reasons, let’s review each application.

  1. To analyze consumer behavior
  2. For better market insight
  3. To improve the supply chain
  4. For better risk management
  5. To reduce cost
  6. To increase operation efficiency
  7. For better compliance management
  8. Better product development
  9. For data-driven smart decision making
  10. Big data analytics helpful in Industry 4.0 initiatives
  11. Better industry segmentation and targeted audience marketing
  12. For better quality management system
Big Data Analytics 2

Big data technologies 

Following big data tools widely used in the industry! 

  1. Apache Hadoop
  2. Cassandra
  3. Knime
  4. Tableau
  5. Spark
  6. QlikView

Application of Big Data:

Big data analytics is popular in various business sector. Big data is used in following sectors for various application and business needs,

big data applications

a. Manufacturing sector:

In manufacturing sector big data analytics plays a big role. A big data analysis helps to reduce operation cost, improve the factory efficiency, monitor factory machineries & equipment and their usage and maintenance cycle.

Big data analytics helps to reduce production cycle time and it can helps to monitor supply chain lead time and provide data which helps an organization to focus on bottleneck in the supply chain.

b. Banking and Financial Sector:

A credit card spend details of particular region or country can be measured and analyzed with the help of big data analytics tools. Big data helpful to detect potential fraud in the financial and banking sector. Big data analytics helps banks and financial institute to keep monitoring of on-time loan or credit card bill payment and defaulters or late payment trend.

c. Insurance Sector:

Regulation in insurance industry mandates insurance provider to keep delivering services as per rules and regulations. Big data analytics helps to keep track on number of insurance claims, claim types, false and forgery in claim submission, delay in claim submission and processing, delay in reimbursement, to monitor lead time in completion of new insurance applications.

d. Health and Wellness:

Health and Wealth! Big data analytics not only helps hospitals to consolidate data but it helps to provide a more personalized treatment and better diagnostics. There are many examples in the healthcare which is termed as a big data.
Eg. Patient history, operation records, medicine and treatment record, health check-up records, demographic specific decease trend etc.

Big Data in Healthcare

e. Education Sector:

Monitoring of student mark trend, student drop out trend, demographic specific school facility and availability, teacher and student ratio at each school, subject specific results and student performance. There are so many opportunities in the education sector where big data analytics techniques can be utilized.

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