Big Data

Today world steadily becomes more connected with an ever-increasing number of electronic devices, which are only set to grow over the coming years.

Ninety percent of the data in the world has been created only in the last two years. World’s output of data is approx.   2. 5 quintillion bytes a day.

In every minute forth five million drivers gets trip in Über and fifty thousand photo posted by users. Last year’s report found that more than 3.5 million text messages were sent every minute. Now, it’s around 15.2 million texts, a 334 percent increase.

The shock value of the recent prediction by research group that the world will be creating 165 zetta bytes of data a year by 2025.

Everybody will create, access and share data on a completely device-neutral basis. The cloud will grow well beyond previous expectations and corporate leaders will have unparalleled opportunities to leverage new business opportunities, it might be predicted. But companies will also need to make strategic choices on data collection, utilization and location.

The term “Big Data” was first introduced to the computing world in 2005 in order to define a great amount of data that traditional data management techniques cannot manage and process due to the complexity and size of this data.

If we understand big data. Here is What  we should know four V’

Volume : Very large amounts of data, usually from terabytes to petabytes and beyond

Variety : Structured and unstructured data

Velocity : Data that is created at a rapid pace and analyzed in near real-time

Veracity : Data that may not be fully accurate or trustworthy

There are a lot of definitions on Big Data circulating around the world, but we consider that the most important one is the one that each leader gives to its one company’s data. The way that Big Data is defined has implication in the strategy of a business. Each leader has to define the concept in order to bring competitive advantage for the company

The importance of Big Data consists in the potential to improve efficiency in the context of usage a large volume of data, of different type. If Big Data is defined properly and used accordingly, organizations are able to get a better view on their business therefore leading to efficiency in different areas like improving the manufactured product, sales and so forth

The most impactful Big Data Applications will be industry or even organization specific, leveraging the data that the organization consumes and generates in the course of doing business. There is no single set formula for extracting value from this data; it will depend on the application.

Value comes only from what we infer from it. That is why we need Big Data Analytics.

Where Big Data are able to used

In information technology in order to improve troubleshooting and security by analysing the patterns in the existing data’s

In customer service by using information from call centers in order to get the customer pattern and thus enhance customer satisfaction by customizing their services;

In improving services and products through the use of social media content. By knowing the potential customers preferences the company can modify its product in order to address a larger area of people;

In the detection of fraud in the online transactions for any industry;

In risk assessment by analysing information from the transactions on the financial market.

Making use of any kind of data requires data collection, processing and analysis, followed by interpretation of results. Specialised skills are needed to address the challenges involved, which may be exacerbated by the scale, complexity or speed of big data

It can take significant exploration to find the right model for analysis, and the ability to iterate very quickly and ‘fail fast’ through many models at scale is critical

Process challenges with deriving insights include

  • Capturing data
  • Aligning data from different sources
  • Transforming the data into a form suitable for analysis
  • Modelling it, whether mathematically, or through some form of simulation

Understanding the output, visualizing and sharing the results, think for a second how to display complex analytics on any mobile device.

Analysts need a tool to work with the data, turn it into a form that can be usable and then perform the analysis. The means having pre built transformations to clean up the data so that is usable as well as prebuilt analytic functions. These functions should include ones to support unstructured data and sentiment analysis.

What constitutes big data today may not constitute big data tomorrow. As technology improves, current computational problems that require massive amounts of parallel storage and processor power often become problems that can be solved with simpler and less costly hardware/software combinations. Just as the supercomputers of 20 years ago are now matched by the personal computers of today, the big data problems of today will be matched by conventional computer systems in years hence.