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Big data talks about complex data forms, which could be structured, semi-structured and completely unstructured data that a business could hold. The origin of such information is the electronic devices humans are interacting it. As the dependency on such sources increases day after day, the data generated is also enormous.

With data lying as the key factor for any business, the necessity lies to use the data properly to reduce risks and maintain any organizations reputation. Herein, we will mainly discuss about big data and what data analytics helps businesses thrive and flourish.

Let us initially get on to know about big data:

Big data talks about complex data forms, which could be structured, semi-structured and completely unstructured data that a business could hold. The origin of such information is the electronic devices humans are interacting it. As the dependency on such sources increases day after day, the data generated is also enormous.

The value of the data is generated by the way we try to store, analyze and process the data. Until and unless it is done, the data remains insignificant. Data is initially raw and needs to be refined with the proper procedures. This serves businesses as helpful information to make effective decisions and is valuable to share with the end-users. Most renowned platforms, mainly social media platforms, use big data to give a better user experience and generate revenue.

Now  let us study the key factors big data has to improve so that the business decisions prove more effective:


Data value is more than analysis. It refers to the analysts’ study of patterns and behaviour, questions by the business experts, prediction of the system behaviour, and its relevant solutions.

What if the original data is not genuine? The resulting actions such as analysis, questioning, predictions, and solutions prove unworthy. So, data analysts must perform a proper study and confirm that the data is genuine initially.


Velocity talks about the rate of receiving and using the data. Digital techniques have enabled simple and quick interactions. Thereby, the usage of credit cards, debit cards and phone built apps have increased. The root information is regularly updated with the data resulting from various digital transactions. This key factor for big data can help to know customer buying patterns.


Veracity talks about being reliable and robust. What is the use if you have an enormous amount of data but lacks reliability? Hence, veracity should have significant importance for the output to turn qualitative.


You have an enormous volume of data originating from social media platforms, websites, user polls etc. However, unless it is structured, the data lie useless. So, organizations must build the capacity to store, analyze and execute the outputs. Notably, the more qualitative data you have, the more effective are the decisions.


The available data type and sources talk about variety. We found that the traditional data types were more structured. However, the resulting big data is more unstructured, and the variety of forms it includes are audio, video, images and text. All such information may not be well organized. Hence, the need to process them to turn more structured and support the decision-making process lies.

Now let us deviate to analytic data techniques and know some of its significant information:

Data analysis can be executed in a variety of ways, each one fulfilling an objective. The three essential data analytic techniques include the following :

  • Descriptive Analysis:

Analyzing the past and the current data comprise descriptive analysis. For instance, the data analysis can be performed by the customer, product, etc. Such analysis would help pay attention to the past trends, classify the information, draw relevant conclusions and significantly minimize the risks.

  • Predictive Analysis.

Such analysis helps anticipate things. Scenario Manager serves as an excellent to perform this. Additionally, a Data analysis pack is another effective option for performing statistical techniques. This helps know various dependencies. For instance, predicting the sustainability of cold beverages according to weather changes.

  • Prescriptive Analysis.

This kind of analysis is considered a more strong analysis. The analysis techniques primarily deal with cost optimizations, profits, and minimizing costs.

Conclusion :

A person aspiring to be a planned business leader must incorporate the advantages of big data and data analytics. Such implementation helps know customer actions. Consequently, the relevant procedures for gaining customer satisfaction can be put into action.

AI intelligence is thriving. ONPASSIVE, an AI-powered organization, has an AI intelligent tool called O-Desk that is creative enough to study the current market and suggest relevant options.

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