For many Big Data is a term that immediately evokes images of huge servers. But it is in fact a much broader concept that spans across 12 major areas in which it is currently being used. Within these areas it can be put use for any purpose. These include:

-Understanding and targeting customers

-Understanding business processes

-Performance optimization

-Healthcare improvement

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-Improving sports performance

-Science and Research improvement

-Optimizing machines

-Improving device performance

-Improving security and law enforcement

-Improving cities and countries

-Improving financial trading

-Personal qualification


These 12 categories represent most of the areas where Big Data is currently being used. Of course there are many other applications of big data.

Today, only size may not be the defining characteristic of Big Data. Other things like variety and velocity also contribute to Big Data. It is just as important to sort the data and be up-to-date the more relevant it will be.

Data is also available in diverse forms. A greater variety of data means you have more ways to analyzing a particular challenge and more likely to come up with a solution. In fact, if data is unidentified, old or not linked to any source, it may be dangerous simple because solutions will be complication leading to increase in expenditure.

When it comes to predicting the future, data doesn’t actually tell us anything that is certain- anything it gives you will be based on a probability with a minor margin for error.

The more data you have, and the more relevant that data is, the more accurate your probability forecasts will be. The various sources in which you can gather data are:


Archives include documents such as scanned documents, forms, medical records, customer receipts, customer care forms or any document that contains a record between the organization and the customer


Docs include XLS, PDF, CSV, PPT, HTML, Word, JSON, XML


Media includes Images, video, audio, live streams, podcasts etc

Data Storage

SQL, Hadoop, Repository, File systems etc

Machine Log Data

Event logs, server data, application logs, process logs, audit logs, call records, mobile location, mobile app usage etc

Sensor Data

Medical devices, smart electric meters, car sensors, smart bulbs, road cameras, satellites, recording devices, processors found in vehicles, video games, cable boxes, household appliances, office buildings, cell towers. Air conditioning, refrigerators, trucks etc

Social Media

Facebook, Twitter, LinkedIn, Google Plus, YouTube, Pinterest, Blog, Slideshare, Tumblr etc

Business Apps

Project management, marketing tool, CRM, ERP, HR systems, storage, talent management, expense management, Google docs and portals.


Essentially, there exists a lot of internal archived data in an organization that is typically unstructured and is not being used. Similarly, docs, media exist both inside or outside the organization and are not being used. Business apps are structured and you can pull that data from inside as well as outside your organization.

Social Media is a high volume data platform that can be used to detect trends about your brand, customer service and competitors. Similarly, sensors are also high volume data collecting things. They can collect reams of data that is useful for better purchase and ownership experiences in a variety of industries.