Big Data and Small Data: What’s the Difference?

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Click here to learn more about author Sahil Miglani.

The technology domain has witnessed a dramatic growth in the recent past. Methodologies have changed, new ways have been adopted by companies to accomplish these desired tasks. No wonder this change will continue to rise as steadiness in this domain looks bleak.

One of the key areas, which has impacted the industry hard in the last three years is the data balloon. This data balloon has inflated immensely giving birth to new data analysis and mining tools, and techniques to manage it.

Data has always been an important aspect of business. Whether to make decisions or to analyze the past, data is required. Lately this requirement has bewildered the whole system. What does Big Data really mean and how does it stand apart from Small Data? Here we dig deep to understand the core of both the terms — Small Data and Big Data.


Small Data

Small Data can be defined as small datasets that are capable of impacting decisions in the present. Anything that is currently ongoing and whose data can be accumulated in an Excel file. Small Data is also helpful in making decisions, but does not aim to impact business to a great extent, rather for a short span of time.

It comprises of definite and specific attributes of datasets, which can be used to analyze current situations. The specific datasets derived after digging into the huge chunks of data can also be referred to Small Data. There are a lot of issues within an organization that demand quick and instant analysis. In cases like these, there is no need to use Big Data analytical tools.

Big Data

Big Data, on the other hand, can be described as huge chunks of structured and unstructured data. The volume of data stored is huge. Hence, it becomes important for analysts to carefully dig the whole thing up making it meaningful and useful for making better business decisions.

Big Data comes in handy when a business owners like you need to make crucial decisions for expansion. You rely on a bunch of professionals to extract useful data using Big Data Analytics, which can impact the business in a positive manner. The insights incurred by a Big Data professional are extremely beneficial for businesses to make important calls and make a move accordingly.

However, an important thing to note here is the term ‘Big’ used in Big Data. Do you have any clue what is big here? Does it refer to the quantity of data that a professional manages and analyzes during the mining process? Or does it refer to something else? Taking the covers off, ‘Big’ refers to the big decisions that it enables an organization to take time after time, which ultimately results in increased revenue, more customers and responsibility to nurture relationships.

Looking at some of the past stats, which actually hint to the future showing how big, ‘Big Data’ will get. According to, there will be approximately 50 billion devices connected to the internet by 2020. In 2015, traffic recorded by cloud computing in North America alone was approximately 106 exabytes (3.7 billion gigabytes), while for the same year, it was about 3.7 exabytes for mobile devices.

There is no denying the fact that technology is evolving, a total number of connected devices are growing and companies connecting the physical world to digital media are constantly working on techniques to get more people onboard. This clearly indicates how much more data and information will float in the digital space in the coming years. So much more data needs to be processed, analyzed and put to beneficial use by companies in the future.

Comparing the two, we can conclude that Big Data is nothing but large data groups either structured or unstructured, which are hard to comprehend, access, organize and analyze. While, Small data, on the other hand, is easy to understand, access and analyze. One common thing between the two is their ability to impact businesses via thoughtful decisions and conclusions made after in-depth analysis.

There are several Big Data analytical tools available in the market making lives easier for analysts and helping businesses to flourish. But, still, the dominance of either is questionable as no definite results can be incurred. Both of them hold their own pros and cons, which can’t be ignored. After, serious analysis and study about the two it’s pretty evident that they aren’t fading away anytime soon instead will continue to impact the market in future as well.

This can be regarded as just the beginning of a new revolution, which is about to happen when data flow becomes immense like never before.

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