Advantages and disadvantages of big data for the individual

            

         1.What are the advantages and disadvantages of big data for the individual?


The term “big data” refers to the recording, storage, and processing of information related technologies. Big data does not necessarily define data sets and databases: it refers to knowledge processing systems, methods, and tools. The revolutionary nature of a real-time data processing system enables users to be given immediate feedback without having to wait for further study.

Pros of Big Data for individuals:

 

·       Detects Fraud 

Another important advantage companies find with big data is that it can help identify fraud. That benefit is so profound from a financial services perspective that the identification always takes place before the consumer really realizes something is wrong.

·       Customer Service

One of it’s most-cited goals of a big data implementation effort is to improve the interactions between customers. AI, machine learning, and similar systems can analyze CRM systems, social media, and email interactions information to provide a wealth of information about how people really feel and think. Accessing the information from data collection processes enables serving consumers when anything unexpected occurs.

·       Security

Analysis of the data in real-time allows you to spot anomalies in expected patterns almost instantly. This allows you to identify and, in fact, fix any problems that may have occurred, resulting in better customer experience.

·       Increases Productivity

This advantage also gives individuals more information about themselves so that they can recognize areas where they could be more productive in their activities. That’s why investing in this technology often leads to a slow rise in results starting from the bottom-up.

 

Cons of Big Data for individuals:

 

·       Questionable Data Quality

A significant drawback to consider when using big data as an asset is the quality of the information the organization collects. Analysts and data scientists must ensure the accuracy of what they receive before any of the info becomes usable for analytics. They will then need to determine the relevance of each data lake and correctly format it for review. These required tasks can significantly slow the reporting steps.

·       Security Risks

Almost all of the information businesses gather in a data lake includes sensitive information that requires a specific level of protection. Accessing such analytics can make an organization an attractive target for a potential cyber-attack. A data breach is often the single biggest threat a company faces when it attempts to create that culture.

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https://honestproscons.com/pros-and-cons-of-big-data/

 

 



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