5 Ways to Understand the Importance of Big Data

June 19, 2017
5 Ways to Understand the Importance of Big Data

Modern times handle Big-data and the amount of data just keeps growing by the moment. Today enterprises not only use the data generated by them but also cull the data from internet services, audio clips, video s, social posts, blogs and other sources.

Understanding Importance Of Big Data

Big data analytics deals with data primarily and the predictions or forecasts from analyzing databases that help with informed decision making in all processes related to business. All of us generate data and the volume of data has now become incredibly large. Keeping pace with the generation of data has been the need for cutting edge tools to clean, format, group, store and draw inferences from databases not only our own but across verticals and fields. Some of the interesting fields spawned and co-existing with the use of big data analytics are in machine learning, artificial intelligence, virtual reality, and robotics.

In modern times the value of Big Data, its forecasts and insights are invaluable to companies. However, it is not easy to clean the data, match and format the various types of data, prepare the data to be available in an easily understandable form and then use the data for analytics. It requires discipline, patience, lots of practice and asking the right question to the right database to be able to produce those predictive insights. Importance of Big Data is so encompassing in a world ruled and constantly generating large amounts of data every moment that analysts, engineers, scientists and others making a career in the Big Data field is sure to have an unending scope. The more the data, the better the evolving technologies get and so also follows the demand for personnel who can understand and handle it.

Yet, the 4 V parameters can be used to understand Big data. They are

• Variety – This defines the type of data source and whether it is generated by a machine or people.
• Volume – This parameter has moved from Gigabytes to terra bytes and beyond and denotes the amount of data generated. The sources have increased as also the speeds of data generation. The definition of volume should be very large Big Big Data many times over by now.
• Velocity – This parameter defines the generational speed of data. This grows by the moment and entails huge volumes.
• Veracity – This parameter defines the data quality and at times is out of the analyst’s control.

Technology has also evolved and has taught us that it is not sufficient to just gather data but use it effectively to improve organizational performance. Big-Data has immense applications across all industrial verticals, in personal and industrial scenarios and has successfully advanced not just organizational productivity but the economy as a whole. This development in data and its technology-enabled predictive analytics to make use of forecasts and gainful insights to improve the various processes and applications.

The Three Stages of Data

All data may not be in the same format and may be in different formats and made available from various sources. Labelled data is very different from real-time unlabeled data. Thus all data passes through three stages which are performed as loops and repeated many times in a fraction of a second.

• Managing the data: Here the data is extracted from various sources and the relevant data is extracted from it.

• Analyze and perform data analytics on it: In this stage, ML algorithms are applied and data processed to gain foresight, insights and make predictions.

• Make the correct decision with data: The all-important stage of applying the data to a relevant decision-making process is executed to provide the desired outcome. When the results are not the desired outcome the process is automatically repeated to narrow the differences between output and the desired result.

With traditional tools, one can work with relatively smaller databases that are less than a terabyte size-wise. However, modern data tends to be unstructured and comes in the form of videos, audio clips, blog posts, reviews, and more which are challenging to clean, organize and include huge volumes of data. The tools and techniques involved in the capture, storage and cleaning of data need necessarily to be updated. One also would need faster software that can compare databases across platforms, operating systems, programming languages and such complexities of technology.

The Five Organizational Benefits of Big Data

Big Data brings in great process benefits to the enterprise. The top five are

  •  Understand market trends: Using big data, enterprises are enabled to forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behaviour. The insights can help understand purchasing patterns, when to and which product to launch and suggest to clients product preferences based on buying patterns. Such prior information helps bring in effective planning, management and leverages the Big Data analytics to fend off competition.


  •  Understand customer needs better: Through effective analysis of big-data the company can plan better for customer satisfaction and thus make alterations needed to ensure loyalty and customer trust. Better customer experience definitely impacts growth. Complaint resolution, 24×7 customer service, interactive websites and consistent gathering of feedback from the customer are some of the new measures that have made big-data analytics very popular and helpful to companies.


  • Work on bettering company reputation: Sentiments and their analysis can help correct false rumours, better service customer needs and maintain company image through online presence which eventually helps the company reputation using Big Data tools that can analyze emotions both negative and positive.\


  • Promotes cost-saving measures: Though the initial costs of deploying Big Data analytics are high, the returns and gainful insights more than pay for themselves. This also enables constant monitoring, better risk-management and the IT infrastructure personnel can be freed up. This translates into reduced personnel required. Besides this, the tools in Big Data can be used to store data more effectively. Thus the costs are outweighed by the savings.


  •  Makes data available: Modern tools in Big Data can in actual-time present required portions of data anytime in a structured and easily readable format.


If you are keen to take up data analytics as a career then doing Big data training with a reputed institute like Imarticus is certainly advantageous to you. The courses augment your knowledge, bring you up to speed with the latest tools and technologies and even include real-time, live projects that enable the transformation of theory into confidence-based practical applications of learning in the data analytics field. Why wait?

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