APPLICATIONS OF BIG DATA ANALYTICS

BIG DATA ANALYTICS

What is Big Data Analytics?

Big data analytics helps businesses to get insights from today's huge data resources. People, organizations, and machines now produce massive amounts of data. Social media, cloud applications, and machine sensor data are just some examples.Big data analytics  use different sources, and in different sizes from terabytes to zettabytes.

Applications of Big Data

Healthcare 

Big data analytics have improved healthcare by providing personalized medicine and prescriptive analytics. Researchers are mining the data to see what treatments are more effective for particular conditions, identify patterns related to drug side effects, and gains other important information that can help patients and reduce costs. It’s possible to predict disease that will escalate in specific areas. Based on predictions, it’s easier to strategize diagnostics and plan for stocking serums and vaccines.

Media & Entertainment

Various companies in the media and entertainment industry are facing new business models, for the way they – create, market and distribute their content.

Media and entertainment service providing company like Netflix, Amazon Prime, Spotify do analysis on data collected from their users. Data like what type of video, music users are watching, listening most, how long users are spending on site, etc are collected and analyzed to set the next business strategy.

Big Data applications benefits media and entertainment industry by:

Predicting what the audience wants

Scheduling optimization

Increasing acquisition and retention

Ad targeting

Content monetization and new product development

Spotify an on-demand music service, uses Hadoop Big Data analytics, to collect data from its millions of users worldwide and then uses the analyzed data to give informed music recommendations to individual users. Amazon Prime, which is driven to provide a great customer experience by offering video, music, and Kindle books in a one-stop-shop, also heavily utilizes Big Data.

Traffic Optimization

Big Data helps in aggregating real-time traffic data gathered from road sensors, GPS devices and video cameras. The potential traffic problems in dense areas can be prevented by adjusting public transportation routes in real time.

Real-time Analytics to Optimize Flight Route 

With each unsold seat of the aircraft, there is a loss of revenue. Route analysis is done to determine aircraft occupancy and route profitability. By analyzing customers’ travel behavior, airlines can optimize flight routes to provide services to maximum customers.

Increasing the customer base is most important for maximizing capacity utilization. Through big data analytics, we can do route optimization very easily. We can increase the number of aircraft on the most profitable routes.

E-commerce Recommendation

By tracking customer spending habit, shopping behavior, Big retails store provide a recommendation to the customer. E-commerce site like Amazon, Walmart, Flipkart does product recommendation. They track what product a customer is searching, based on that data they recommend that type of product to that customer. As an example, suppose any customer searched bed cover on Amazon. So, Amazon got data that customer may be interested to buy bed cover.

Next time when that customer will go to any google page, advertisement of various bed covers will be seen. Thus, advertisement of the right product to the right customer can be sent. YouTube also shows recommend video based on user’s previous liked, watched video type. Based on the content of a video, the user is watching, relevant advertisement is shown during video running. As an example suppose someone watching a tutorial video of Big data, then advertisement of some other big data course will be shown during that video.

Big data applications in agriculture

Traditional tools are being replaced by sensor-equipped machines that can collect data from their environments to control their behavior – such as thermostats for temperature regulation or algorithms for implementing crop protection strategies. Technology, combined with external big data sources like weather data, market data, or standards with other farms, is contributing to the rapid development of smart farming.