Every day three quintillion bytes of data is generated. This data comes from different sources like digital pictures, videos, posts on social media, e-businesses, intelligent sensors, and log storage in the IT industry.

According to McKinsey, “big data” refers to datasets whose size is far beyond the ability of typical database software tools to capture, store, manage and analyse. 

Real-world challenges of Big Data enterprises

One of the major sources of data is the log storage, which is present in the IT industries because the IT industry stores a lot of information in the form of logs of data.

This data is so vast that the traditional system becomes unable to handle such kinds of logs as this data is semi-structured in nature and is growing with great velocity.

 Sensor data refers to the data coming out of sensors. An enormous amount of sensor data is also a challenge for big data. One example of sensor data is the Large Hadron Collider (LHC).

LHC is the world’s largest and highest-energy particle accelerator. The data-flow in its experiments consists of twenty-five to two hundred petabytes of information that has to be processed and held on.

Also Read: How big data is impacting the legal world

So we can say that this sensor data is a very challenging one for management as the traditional system cannot handle such a variety of data.

Another place where we can find big data is in Risk Analysis.

So any financial institute needs to model data, and based on this data, they need to calculate what kind of risk can occur.

One of the problems with the data of risk analysis is that it is huge and is generally under-utilised. We need to perform big data analytics on such data to get more accurate information and predictions on risks involved in the management of risk analysis data.

The data generated from social media can be of any type—may be structured, semi-structured or unstructured.

The rate at which social media data is increasing is astounding. Therefore, it is also one of the important sources of big data in a real-life scenario.

Apart from the above scenario, there are some other significant data contributors as well; for example, customer analytics, experience analytics, threat analysis, fraud analysis, and brand sentiment analysis, where we use big data.

Big Data opportunities

From this real-world scenario, we can say that big data has a lot of applications and opportunity and lots of use cases. So it is essential to study this big data and perform analytics on it because it can be very advantageous for an organisation. 

According to a survey conducted by SoftServe in 2016. 86 per cent of organisations are now using big data analytics solutions for retaining customers, understanding customers, the trending interests of the customers, and more.

Out of this, 45 per cent of all respondents use it across an organisation, and 41 per cent use it in their organisations.

The areas where big data analytics provides the most value are:

  • Customer Intelligence
  • New Revenue Opportunities
  • Better Customer Services

There is a lot of application of big data analytics in fields like healthcare, telecommunication, financial firms, retail, law enforcement, marketing, new product development, banking, energy and utilities, insurance, and education. 

With increasing analytic skills among various organisations, the advantage of big data analytics can be realised in sectors like constructions and material sciences.  

Also Read: Busting the myths around AI, IoT, Big Data and Cloud at Echelon 2019

Big Data analytics uses

  • Big data analytics applications regularly include data from the internal system and external sources.
  • Weather and demographic data on customers collected by third party information services providers.
  • Also, streaming analytics applications have become common in a big data environment.

Big Data issues: security and privacy

The prime concern with big data apart from conceptual significance entails both technical as well as legal relevance.

An individual’s private information, when combined with large external datasets, leads to the inference of new and specific facts about the said person.

When law enforcers take assistance from big data, the chances that certain tagged people might have to go through uncalled sufferings decrease drastically.

Big Data analytics solutions

Getting out the secrets hidden in big data is a critical factor for developing future strategies which can help in the growth of a business. So, for this, there are many big data solution companies, which are big data solution providers.

These big data solution providers have different big data consulting strategy for solving complex big-data challenges for enterprises.

Big Data Consulting Services Companies

As all the organisations, business companies and industries are using big data analytics. The big data consulting services are largely used for big data services and solutions. There are many big data consulting services companies which are operating mainly in India like Spec India, Pythian, ScienceSoft, etc. 


As data is increasing day by day at a very high pace, there are a lot of challenges involved in big data and enterprises can have a lot of opportunities from this.

According to the McKinsey Global Institute, big data is in every industry and business function and is an important factor for production as well as business growth and development.

Also Read: Implications and solutions for Big Data in insurance

Many companies for big data solutions are coming with a different strategy for big data solutions and are providing big data solutions to various organisations and business companies. With the help of big data, we can make more educated decisions, and our focus can remain on business operations moving forward.

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Image Credit: Stephen Dawson