Game Changing Big Data Use Cases

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May 13, 2014
Game Changing Big Data Use Cases
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Big Data can address the various difficulties faced by large organizations .The following are high value Big Data use cases that can be used to address the concerns faced by them.

Big Data Exploration

Big Data exploration deals with the challenges like information stored in different systems and access to this data to complete day-to-day tasks, faced by large organization. Big Data exploration allows you to analyse data and gain valuable insights from them.

Enhanced 360º Customer Views

Enhancing existing customer views helps to gain complete understanding of customers, addressing questions like why they buy, how they prefer to shop, why they change, what they’ll buy next, and what features make them to recommend a company to others.

Security/Intelligence Extension

Enhancing cyber security and intelligence analysis platforms with Big Data technologies to process and analyze new types from social media, emails, sensors and Telco, reduce risks, detect fraud and monitor cyber security in real-time to significantly improve intelligence, security and law enforcement insights.

Operations Analysis

Operations analysis is about using Big Data technologies to enable a new generation of applications that analyze large volumes of multi-structured, like machine and operational data to improve business. These data can include anything from IT machines to sensors and meters and GPS devices requires complex analysis and correlation across different types of data sets.

Data Warehouse Modernization

Big Data needs to be integrated with data warehouse capabilities to increase operational efficiency. Getting rid of rarely accessed or old data from warehouse and application databases can be done using information integration software and tools.

Companies and their Big Data Applications:

Guangdong Mobiles:

A popular mobile group in China, Guangdong uses Hadoop to remove data access bottlenecks and uncover customer usage pattern for precise and targeted market promotions and Hadoop HBase for automatically splitting data tables across nodes to expand data storage.

Red Sox:

The World Series champs come across huge volumes of structured and unstructured data related to the game like on the weather, opponent team and pre-game promotions. Big Data allows them to provide forecasts about the game and how to allocate resources based on expected variations in the oncoming game.

Nokia:

Big Data has helped Nokia make effective use of their data to understand and improve users’ experience with their products. The company leverages data processing and complex analyses to build maps with predictive traffic and layered elevation models.  Nokia uses Cloudera’s Hadoop platform and Hadoop components like HBase, HDFS, Sqoop and Scribe for the above application.

Huawei:

Huawei OceanStor N8000-Hadoop Big Data solution is developed based on advanced clustered architecture and enterprise-level storage capability and integrating it with Hadoop computing framework. This innovative combination helps enterprises get real-time analysis and processing results from exhaustive data computation and analysis, improves decision-making and efficiency, make management easier and reduce the cost of networking.

SAS:

SAS has combined with Hadoop to help data scientists transform Big Data in to bigger insights. As a result, SAS has come up with an environment that provides visual and interactive experience, making it easier to gain insights and explore new trends. The potent analytical algorithms extract valuable insights from the data while the in-memory technology allows faster access to data.

CERN:

Big Data plays a vital part in CERN, home of the large Hadron Supercollider, as it collects unbelievable amount of data from its 40 million pictures per second from its 100 megapixel cameras, which gives out 1 petabyte of data per second. The data from these cameras needs to be analysed. The lab is experimenting with ways to place more data from its experiments in both relational databases and data stores based on NoSQL technologies, such as Hadoop and Dynamo in Amazon’s S3’s cloud storage service

Buzzdata:

Buzzdata is working on a Big Data project where it needs to combine all the sources and integrate them in a safe location. This creates a great place for journalists to connect and normalize public data.

Department of Defence:

The Department of Defense (DoD) has invested approximately $250 million for harnessing and utilizing colossal amount of data to come up with a system that can make control and make autonomous decisions and assist analysts to provide support to operations. The department has plans to increase their analytical abilities by 100 folds, to extract information from texts in any language and an equivalent increase in the number of objects, activities, and events that analysts can analyze.

Defence Advanced Research Projects Agency (DARPA):

DARPA intends to invest approximately $25 million to improve computational techniques and software tools for analyzing large amounts of semi-structured and unstructured data.

National Institutes of Health:

At 200 terabytes of data contained in the 1000 Genomes Project, it is all set to be a prime example of Big Data.  The datasets are so massive that very few researchers have the computational power to analyse the data.

Big Data Application Examples in different Industries:

Retail/Consumer:

  • Market Basket Analysis and Pricing Optimization
  • Merchandizing and market analysis
  • Supply-chain management and analytics
  • Behaviour-based targeting
  • Market and consumer segmentations

Finances & Frauds Services:

  • Customer Segmentation
  • Compliance and regulatory reporting
  • Risk analysis and management.
  • Fraud detection and security analytics
  • Medical insurance fraud
  • CRM
  • Credit risk, scoring and analysis
  • Trade surveillance and abnormal trading pattern analysis

Health & Life Sciences:

  • Clinical trials data analysis
  • Disease pattern analysis
  • Patient care quality analysis
  • Drug development analysis

Telecommunications:

  • Price optimization
  • Customer churn prevention
  • Call detail record (CDR) analysis
  • Network performance and optimization
  • Mobile user location analysis

Enterprise Data Warehouse:

  • Enhance EDW by offloading processing and storage
  • Pre-processing hub before getting to EDW

Gaming:

  • Behavioural Analytics

High Tech:

  • Optimize Funnel Conversion
  • Predictive Support
  • Predict Security Threats
  • Device Analytics

Related posts:

Introduction to Hadoop 2.0 and advantages of Hadoop 2.0 over Hadoop 1.0 

Career advantaged through Hadoop certification.

Rising popularity of Hadoop and MongoDB.

How essential is Hadoop training?

Hadoop 2.0 FAQs. 

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Comments
2 Comments
  • Rajiv

    thanks sir
    very useful resource..thanks edureka

    • EdurekaSupport

      Thanks, Rajiv! Do check out our complete training details here: https://goo.gl/ZLkR8t. Our learners get to work with many use-cases from the industry in the live components of the course. Cheers!