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Big Data refers to a collection of data that is so huge and complex that none of the traditional data management tools can store or process it efficiently. Big data involves lots of data. It can be a combination of structured, semi-structured, and unstructured data collected from various resources.

[Figure_1: Big Data (Edited by me on PowerPoint)]

Why Big Data a Hot Edge of IT Development?

The world is becoming an increasingly digital space. Today we manage to share and store our lives, online data is gathered from our devices, computers, and smartphones that collect and transmit information on what we do. But that is just the beginning, soon most things including our TVs, watches, and even diapers will collect and transmit data.

[Figure: Data from Various sources (Edited by me on PowerPoint)]

If we gather all the data from the beginning of time until the year 2000 it would be less than we now create in a minute. This phenomenon is transforming our understanding of the world and our place in it. It's become known as big data. It could be valuable for businesses and could provide a window into the lives of customers that we have never previously imagined.

Businesses are already using big data to understand better and predict customer behavior, optimize and improve business processes. But the possible applications of big data are endless. We're only just beginning to see the emergence of the big data economy. Our business needs to consider big data or risk being left behind here. At the Advanced Performance Institute, experts specialize in helping companies understand and leverage big data.

[Figure: Importance of Big Data (Edited by me on PowerPoint)]

In the future, we can even use the big data of DNA to determine the perfect treatment, this way curing genetic diseases like cancer would become much easier and that's just the start.

Barriers to Big Data:

Here is a problem how do we unravel the strands of big data and pick out the relevant parts as data come from multiple sources. Some other critical issues are:

1. Proper investigation of big data
2. Lack of timely Access
3. Lack of accurate data Conversion
4. Lack of sufficient comprehension
5. Data growth issues
6. Confusion for tool selection.
7. Scarcity of Data Professionals
8. Data Security
9. Data Integration.