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The Benefits of Big Data Analytics

November 21, 2022

Big data analytics is the process of finding patterns, trends, and relationships between large amounts of raw data in order to make data-driven business decisions. With a constant influx of data coming from various touchpoints, such as smart devices, Internet usage, customer interactions, etc., prominent data analytics engineers can harness this information and transform it into essential business insights. 

Understanding Customers 

Big data analytics applications help companies gain a comprehensive understanding of their customers. What makes them act, what products they buy and when, how they interact with businesses, and why they choose a particular company/product over others. 

Companies may gather data from social media, public data, and browser logs using the correct mix of big data analytics tools, providing an inside perspective of customer expectations.

Defining Trends 

Identifying and tracking patterns and behaviors tells companies where their growth is headed, the demand for their products, and how it changes over time. Technology removes “instinct” from predicting trends. 

Big data tools are designed to gather data from social media and traditional media and customer behavior patterns. This, in turn, informs companies about where they should focus their energies proactively, both in terms of targeted advertising and customer retention. 

Monitoring the Competition 

Traditionally, understanding the steps of competitors has been limited to activities such as reading business news, pretending to be a customer to get process information, etc. Today, however, you can get all the information you need about your competitors without even leaving your desk. 

Google Trends can show a brand’s popularity, social media can tell you what people think about a product, and ratings and review sites can show you where a brand is falling behind. All of this is made available through simple big-data analytics. 

Improving Operational Performance 

Big data technology is in demand to optimize business processes and operations. Embedded in every business operation with big data, such as production lines, customer ordering systems, etc., technology is used to determine efficiency, find anomalies and identify when a process needs improvement. 

These benefits of big data analytics are actively used in industries such as retail, supply chain, telecommunications, healthcare, and other similar industries. 

Improving Business Models 

Companies use big data analytics applications to update and create new products and business lines. With a massive set of market data, companies can determine what their customers are looking for and which companies are meeting their needs. This information, in turn, is used to identify new products and business models. 

The benefits of big data analytics we have discussed are only a superficial explanation of how companies use big data. This technology has many other uses in the global ecosystem of startups and established businesses. However, startups need to understand that these benefits can only be realized if you correctly integrate big data analytics into your business. 

Getting Started with Using Gig Data Analytics in Your Business

An essential part of getting all the many benefits that big data technology promises is knowing how to start incorporating it into your business model. 

At Bragona Technologies, we observe this process every time a client asks us to help create a big data project. 

Big data projects emerge when a company executive becomes convinced that they are missing out on the benefits of big data. That conviction leads to the CMO and CIO teams starting to work together, where they identify and define the scope of the problems that need to be solved and create an analytics architecture around them. The business then launches a small IT project where the CIO team develops a sophisticated system to collect raw data and convert it into structured information. 

We suggest collecting data in an ad hoc model instead of creating a data collection and analysis process. If you start by collecting the specific data needed for the test, you’ll find that the amount of data is easy to manage. At this point, we plug in an analytical engineer. They can highlight which data is needed and which data should be ignored for a particular big data model. 

Once you’ve mastered these two steps, you can move on to the much broader big data implementation phase of the business. Implementing big data through these small, focused steps can be the cheapest and fastest way. This will give the business clarity on whether or not a significant investment in big data will be profitable or how it will pay off in the long run. Big data is undeniably essential, but what benefits others may not help you. By running these small-scale prototypes, you can decide if there is even a need for big data in your business. 

Third, it’s important to know the challenges of implementing big data. Knowing these in advance will help you make informed decisions about the project’s scope. Here are some of these challenges 

Managing data: As the business grows, so does the amount of data that needs to be collected. 

Maintaining data quality – companies spend a lot of time cleaning up data and preparing it for analysis. 

Ensuring data security: Privacy issues are an inevitable part of big data, especially given its volume of business information. 

While this is the most secure approach to bringing big data into the business, knowing how big data analytics works is equally important. This will give you an idea of how to get started. Let’s take a look at that further to conclude this article. 

How Does Big Data Analytics Work?

Big data analytics is a four-step process that businesses use to produce informed data. Here’s what those stages are:

Data Collection – Here, data (structured, unstructured, and semi-structured) is collected from a variety of sources and stored in data warehouses where BI tools can analyze it. Typically, a wide variety of unstructured data is endowed with individual metadata and stored in data lakes. 

Data Processing – Once data is collected and stored, it is processed to produce query results. There are two processing options for this – batch processing, where large sets of data are examined over a period of time, and streaming processing, where small batches of data are processed. 

Data Cleansing – Data, regardless of size, requires cleaning to improve data quality and produce better results. In this step, the data is formatted in a single context, and any irrelevant, duplicate information is removed from the system. 

Data Analysis – The collected data is converted into conclusions in this step. Some of the most popular data analysis techniques include – data mining, predictive analytics, and deep learning. 


That’s all you need to know about big data analytics. While the approach to integration may vary, the result should be the same – big data should solve business problems, not complicate them. 

If you’re looking to integrate big data technology into your business and are looking for support to get you started, contact our data analytics team. We’ll help you identify the areas where big data can do the most good and work to implement it.

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