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Purposes of Big Data Technologies

Big Data Technologies

Big data comprises structured, semi-structured, and unstructured data that is collected by the organization. And mined for machine learning. The organization usually mine information for learning projects. Such as predictive modeling and advanced analytics applications.

Storing and processing big data has become an important component of data management in a variety of organizations. Big data technologies is characterized by 3Vs namely, volume, variety, and velocity. Doug Laney, an analyst at Meta Group Inc. helped identify these characteristics back in 2001. Today, more Vs have been identified such as veracity, value. And variability of data. Big data does not refer to any specific volume of data. But it refers to the deployments such as Terabytes (TB), Petabytes (PB), and Exabytes (EB) captured over time.

Why is Big Data important?

Many companies use big data to improve their day-to-day operations. And customers survive through personalized marketing campaigns. As well as Big data allows them to increase profitability by creating content. That aligns with customer preferences. This provides companies an edge because they can make more informed business decisions.

Big data is especially useful because it provides companies with insight into customer behavior. This can be monumental in refining marketing campaigns. And increasing customer engagement which then leads to higher conversion rates. Big data helps companies become more customer-centric and competitive.

Big data can come from different sources such as transactions, customer databases, medical records. As well as social networks, scientific research repositories, and machine-generated data. The raw form of data is then processed using a data mining tool also called data preparation software for different business purposes.

Here are some of the business purposes of Big Data.

Big data is most commonly used for comparative analysis. Because it includes an examination of customer behavior. As well as gives real-time engagement and comparison of different products and brands.

Purpose of Big Data:

Through social media listening. information about customers is gathered through polls and surveys. Marketing campaigns can be improved with more information to sell products. As well as services while focusing on customer satisfaction and sentiment.

The volume of big data is the most commonly cited characteristic of big data. Big data does not refer to a lot of data. But the volume of data is quite important. The data is collected through clickstreams, systems logs, and stream processing systems.

Velocity has to do with the speed at which big data is generated, processed, and analyzed. Various data types are stored in what is called a data lake. Which is a cloud storage service. Big data applications consist of multiple sources of data that are integrated. With an attempt to predict how future sales may work. By correlating sales made in the past. Information about consumer behavior is used to market products in current marketing campaigns. Velocity is important because it helps expand the machine learning and AI capacity of an organization by automatically processing collected data. And generating insights Purposes of Big Data.

Similarly bigger sets of data provide Variability. As well as use large sets of random numbers. And figures which could have multiple meanings. and are less consistent. There are many different ways that this intel can be used.

There are many more Vs associated with big data. And they all serve different purposes. There are up to 10 Vs associated with big data.

How can big data be stored and processed?

It is a handful when it comes to the underlying computer infrastructure. The data needs to be quickly processed. Computing systems that are not as sophisticated. Can be overwhelmed by the high volumes. and varieties of data. The organization requires adequate processing of big data. Which often results in hundreds or thousands of survivors. operating collaboratively in clustered architecture. Technologies such as Hadoop and Apache Spark come to mind. when discussing big data processing. This could mean a huge investment for companies. And often companies shy away from making it.

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