How To Use Big Data In Customer Prospecting

With continued progress in data science and the rise of Big Data, there is an enormous opportunity for sales managers to gain new insights into driving sales and increasing customer loyalty.

However, one hurdle many organizations face is learning to convert ‘big data’ into usable data. This is why more than data availability is needed, and knowing how to use it is crucial.  

After all, managers need to learn how to extract actionable data and use it correctly to increase sales opportunities. According to an IBM survey, only 5% of companies in large countries use big data to their advantage.

Furthermore, nearly 70% of businesses only operate on the first two steps. That is, educating and exploring while needing more engagement and execution. 

That’s why you need to learn how to use big data like Big Data efficiently to increase the performance of your marketing and sales team. 

What Is Big Data?

Big Data combines structured, semi-structured, and unstructured data collected by organizations that can be mined for information.

Furthermore, this data is used in machine learning projects, predictive modeling, and other advanced analytical applications. Therefore, they are systems that process and store big data so that they become a standard component of data management architectures in organizations. 

They are often combined with tools that support big data analytics. 

While Big Data does not equate to any specific volume of data, large data deployments often involve terabytes, petabytes, and even exabytes of data generated and collected over time.

How Does Big Data Work?

The main idea behind Big Data is that the more you know about anything, the more you can gain insights, decide, or find a solution. 

In most cases, this process is entirely automated — we have advanced tools that run millions of simulations to give us the best possible result. 

However, to achieve this with the help of analytics tools, machine learning, or even artificial intelligence, you need to know how big data works and set everything up correctly.

Dealing with so much data requires a stable and well-structured infrastructure. After all, this tool will need to quickly process large volumes and different types of data, which can overwhelm a single server or cluster. 

That’s why you need to have a well-thought-out system behind Big Data. All processes must be considered according to the capability of the tool. And this could require hundreds or thousands of servers for larger companies. 

Therefore, you need to know how Big Data works and its three main actions to plan your budget and build the best possible system.

Below we separate the three main ways in which this feature works:


Firstly, Big Data is continuously collected from many sources, and as we talk about substantial information loads, new strategies and technologies to deal with them need to be discovered. 

In some cases, we’re talking petabytes of information flowing through your system, so integrating such a volume of data will be challenging. 

In this way, you will have to receive the data, process it, and format it in the correct form that your company needs and that your customers can understand.


What else could you need for such a large amount of information? You’ll need a place to store it, as your storage solution could be in the cloud, on-premises, or both. In addition, you can choose how your data will be kept to have it available in real-time, on demand.  

That’s why more and more people are opting for a cloud storage solution because it supports their current computing requirements.


Okay, you have the data received and stored, but you must parse it before using it. So, dig into your data and use it to make any critical decisions, such as knowing which characteristics are most searched for by your customers or using it to share surveys. 

Do what you want and need with them — put them to work because you’ve made considerable investments to get this infrastructure in place, so you need to use them.

Also Read: What Are The Big Data Trends For The Coming Years?

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