Implement Real-Time Big Data Analytics Cautiously

A large data set can be analyzed computationally. If it reveals patterns, trends, and associations, then it comes under big data. Organizations are finding it difficult to collect and store data at an ever-increasing flow. Business needs real-time analysis.  This requires applications that can display real-time changes and more illustrative graphics. This is a combination of tools, techniques, methods and frameworks. These statistics have both positive and negative values.
Big data can come from almost anything that generates data. It includes search engines and social media. This data can be categorized into three types:
Structured data – This data resides in a database or spreadsheet, so that its elements are available for effective processing and analysis.
Semi-structured data – It is neither raw nor structured data of a database. It contains tags to enforce hierarchies of records and fields within the data.
Unstructured data – It is very valuable and very much available in business interactions. It is also available in web logs, multimedia content, email, customer service interactions, sales automation, and social media data.
Big data is normally collected and analyzed at predefined intervals, however, the collection and analysis with the real-time big data analysis is continuous, giving the business latest information.
The handling of real-time big data analytics is done with Twitter-owned Storm system. It works with any programming language and can be changed in size or scale. Whereas GridGain also handles the big data and it is compatible with Hadoop DFS – a substitute for Hadoop’ MapReduce.

Advantages:

• Quick Error corrections – This can help prevent many severe failures. This also helps a business’ reputation in the long run, providing more customers.
• Savings – Real-time big data analytics is an expensive proposition but immediate data analysis make up for this expenditure.
• Monitoring products and services provides higher profits. Customer needs are easily predicted with analytics.
• Early real-time fraud detection leads to immediate action.
• Strategic competition with competitors – Big data analytics help in providing a detailed picture of competitors like launching of a new product, price variations for a particular duration or focusing on users from a specific location.
• Vital sales insights can provide additional revenue. The insights include not losing a customer in the long term, checking the bounce rate and finding optimal ways of increasing sales through analyzing real-time big data analytics.
• Long-term decisions by analyzing customer trends.

Disadvantages:

• Hadoop is not currently compatible to handle real-time data.
• New approach required to receive insights asap. This could lead to remodeling of some decisions and plans.
• Roper handling real-time big data analytics is required.

Conclusion:

Real-time big data analytics is an important tool for business, but the organization must check all the situations first before implementing a new technology.