Real-time Big Data Analytics to Take Over Big Data for Data Analysis

A quick revision of data analysis with big data

Data analysis remains to be a crucial part in any IT sector or business organisation. Abundance of articles and contents dedicated to the praise of big data, keep highlighting its importance in the lives of business organisations.

Big data, ever since the 2000s, has gained momentum and has demonstrated its potential in dealing with deluge of data produced on a daily basis. IT sectors and business organisations always strive to speed up data generation and convert the raw data into insightful ones.

Besides the production of huge amount of data generation, they also run the risk of being landing up in wrong hands or being subjected to breach of security. This is where real-time big data analytics comes into play.

Understanding real time big data analytics

Real time big data analytics is an advancement made in big data. While big data served to convert the data base right after the raw files were generated, real-time big data analysis serves to convert the raw files while they are generated. Precisely, the vague looking raw data gets converted into insightful data in some milliseconds after its production. The responses are made without any delay.

What real-time actually means?

Real-time, in literal sense and in the context of computing, means conversion of data during it's production.

Any real-time program yields response within time constraints and hence are looked upon as effective ways to manage time and data in a short period of time.

Why Big Data only was not enough?

Technology keeps evolving and business organisations keep growing. Growth leads to an increment in the production of data. This is turn, enhances the risk of breach of security of data.

Reports say that generation of data in huge amount has caused cyber security threats in business organisations. This happened because of the time taken to convert raw data into meaningful ones. Criminals too are speeding up with their activities to hack data within the time they are converted.

Diagnosing this problem, IT experts have deemed real-time big data as a promising solution.

How can organisations benefit from real-time big data analytics?

Real-time big data analytics are used for a number of purposes

1. Customer data: Real-time big data analytics can be used in tracking of consumer data in no time. Customer data can also be inclusive of sensitive content that it deals with in minimum time.

2. Aides real time testing: Companies and business organisations need to be swift with answers and actions. Real-time big data analytics aides in real-time testing of data that helps the organisations to optimise their data properly and accurate decisions could be made on that.

3. Cost- effectiveness: Real-time big data analytics are known to handle employee engagement, hiring and retention. This lowers the pressure on the IT department, thereby, increasing the cost effectiveness.

The Challenges that Real Time Big Data Analytics pose

Although, diligent in its dealings with large amounts of data, real-time big data analytics too comes with certain cons. For real-time data analytics to be truly functioning, it has to be readily available not only to handle large amounts of data but also to make quick responses to queries. This means that real-time big data analytics should also be adept in handling market and business factors to make effective and efficient real-time decisions.