Big Data vs Traditional Data - Putting an end to the debate

It has never been an easy discussion when we talk about Traditional Data and Big Data. Some talk about the stable nature of Traditional Data, while others debate on the fact that, unlike Big Data, it cannot deal with large voluminous data. Well, the important point that needs to be focused on is not only about the size, but how the stored data can be utilized to the most of its efficiency and yield greater results.

But before jumping into the differences the two terms have, it is going to be essential to get a basic idea about them individually.

Meaning of Traditional Data

Traditional data refers to the organized data stored in a conventional database management system where a centralized architecture concept is used to do all the data processes from storing to maintaining and analyzing everything stored in a fixed field or format in a file.

Every business house, be it very small or big conglomerate organizations, maintains all its data from operations to sales. Here, the common method of accessing and analyzing data is through SQL or commonly known as Structured Query Language.

What is Big Data?

Big data is an upgraded form of traditional data. Big data is a set of a substantially large volume of complex data which the conventional database system software could not manage to process. Unstructured, semi-structured, and structured data are the three types of formats of data available. Big Data does lay a lot of importance on size but it is not the only layer to it.

Key Differences between the two terms

• Big data is different from traditional data not just because of the variation of size they deal in but also because of the manner this data can be used or processed, the tools and applications which can be used while working on big data, and the reliability and accuracy it offers in making strategies and predictions. The confidentiality and accuracy of data is an important aspect for any database system. In the case of traditional data, it is not easy to sustain confidentiality and accuracy since the data is of high quality, also storing such data in high amounts is also quite expensive. While with big data, tasks become less hectic and easier. Furthermore it yields high accuracy and ends with giving highly accurate and trustworthy results.

• In Traditional Data, storing huge amounts of data was impossible. The amount of data was increasing exponentially day by day and needed to be stored, processed, and analyzed as well. This only led to the need for Big Data and other tools for similar purposes. On the other hand, big data can easily store voluminous data. Big Data also saves a huge amount of money used for storing such large data.

• Apart from storing so much data, Big Data and Traditional data also vary based on the types of data. Traditional Data mainly includes financial data, ERP data, organizational data, CRM transactional data, etc. Social Media data, Sensor data, data from videos, audios, pictures, etc are some of the sources for Big Data. Since their data sources are so largely different, the way of representing and storing them also varies. This also affects the database management system and makes it difficult to understand in the case of Traditional Data. Understanding the data is easier for Big Data.

Unlike Big Data, as we store less complicated data in Traditional Data, it is easier to go through, and understanding the relationship between data and data items is also less complicated. Manipulating the data is easier in the case of Traditional Data. The data model of Big Data is dynamic and hence, data integration is tough in this case.

• Although Traditional data had its features to provide to the audience, one cannot ignore the substantially increasing numbers of the data being produced. To believe the estimates, it is around 5 quintillion bytes of data that is being produced each day. So when Big Data has a vast range of features to offer, it is highly recommended and in fact, preferable. It is more flexible, easy to estimate and handle. This makes it easier to analyze. Thus companies can use it as per their requirements and can read the market trends to spend costs for the future according to them.

Conclusion:

The greatest aspect of Big Data is that it does not compromise on the quality to yield faster results. Big Data Solutions led importance to machine learning as well which was rare in the case of Traditional Data. Apart from giving new ways to explore data and information, Big Data even came up with solutions to some issues of Traditional Data that were being neglected for the past few years. Undoubtedly, Big Data cannot just store large data but also analyze, manipulate and use it to its full potential in an easier and faster manner.