Examples as to where companies use Big Data technology

Use Cases reported by Hadoop customers

Some Hadoop customers provide information on why they’re using Hadoop as well as their infrastructure setup. This information can be found on the Apache Hadoop wiki Looking at the sample use cases, common reasons for adopting “Big Data” technology include:-

1. DotCom startups who don’t know how big their data will become commonly use “Big Data” solutions such as the Hadoop ecosystem because:-

i) It’s open source and free

ii) It provides a high level of availability – important if you don’t want your website to go down due to your data store no longer functioning

iii) It’s highly scalable – Hadoop is built to automatically detect extra servers added to a Hadoop cluster, which allows data to be stored on a large amount of data.

2. Large organisations use it because:-

i) It offers a cost effective mechanism for storing large amounts of data

ii) During a development project, you can retain a large amount of historical data on your Hadoop-based Enterprise Data Hub whilst you complete build of your enterprise data warehouse.

iii) It’s highly available

iv) It’s highly scalable

Common use cases include:-

1. Capturing data from web logs for analysis of drop off during a consumer’s journey through a web site.

2. Capturing and indexing web sites

3. Analysing social media feeds

4. Profiling consumer’s data (product choices, interaction with a website, clickthroughs, dwell times etc.), producing models and creating algorithms which target products towards consumers

5. Storage archive for documents and data

Common Use Cases by Industry

icon-financial-servicesFinancial Services

1. Gauging sentiment of customers from monitoring social media

2. Track trends in order to launch new products

3. Background credit checking / know your customer

4. Fight fraud

5. Improve pricing of insurance premiums based on better insight in to customer

6. Using customer insight to better segment customers and improve targeting of products

7. Trading Analysis and Simulation

8. Risk modeling

retailRetail

1. Recommendation Engines

2. Gauging sentiment of customers from monitoring social media

3. Identifying cross-selling opportunities

4. Using holiday, weather etc. data to determine what should be in stores

5. Location-based & personalised marketing

6. Improving supply of goods/streamlining logistics.

7. Identifying potential clients via dwell times

8. Establishing relative success of advertising campaigns

Telecom-ICON-RGBTelcos

1. Profiling customers based on their usage of Telco services e.g. number of calls, call times, data rates

2. Network optimisation e.g. installation of extra infrastructure to avoid dropped calls, constrained bandwidth issues.

3. Gauging sentiment of customers from monitoring social media

gamingGaming

1. Fraud detection and prevention

2. Sentiment analysis

3. Customer acquisition

4. Customer retention

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