How Does Big Data help For Business Growth?

How Does Big Data help For Business Growth?

Data sets that are too large to handle are known as big data. For enterprises and large scale businesses, it becomes a complex task to segment the huge volume of raw data.  So to rectify these issues Big Data have been introduced.

What is Big Data?

Big Data is a systematic data management process that treats a comprehensive approach with a high volume of both structured and unstructured data in order to bring better data insights.

Business intelligence helps business to visualize the limited amount of structured data that worked and not worked in the past. Big Data, let the entrepreneurs experience the exact business insights that will bring fruitful results in the future. Big Data with predictive analytics tells what steps should be made in the future to drive the business on the right path.

Big Data Analytics

It is a systematic approach. It examines the large variety of data sets, to unbox the hidden patterns, market trends, customer experience, data complexities, and business flow to make better-informed business decisions.

How big data helps in business growth?

None of the business launched to the market with a big amount of data in the initial stage, when the business grows as a brand, the data behind the business flow gets dumped in huge volume. So, Big Data is strictly suitable for large scale businesses that are in big need to manipulate or audit their business information.

Why business has to manage the data?

There are two reasons,

1. To segmentize in an easily readable, identifiable, trackable data, which we call as data modeling

2. To make better decisions, by analyzing the data and visualizing it with proper data visualization process.

If a business fails to do any one of the above, sure it will be tough for them to lead the business next level and will be tired of trapped into the data collections.

Data Analytics Processes

Big Data Analytics involves the following basic processes.

1. Data aggregation   -  Data collection.

2.  Data Cleansing      -  Filter out unwanted data.

3.  Data Modelling       -  Making the Data into easily identifiable data sets.

4.  Data warehousing  -  Storing the data into the warehouse.

5.  Data Visualization  -  Visualizing the data in a graphical manner.

6.  Visual Report Development - Developing visual reports.

In Future, we will discuss each process in detail.


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