Solution
Shreyashi answered on
Mar 10 2021
Running Head: BIG DATA, SMALL DATA 1
BIG DATA, SMALL DATA 14
IF WE DID NOT "SOLVE" "SMALL" DATA IN THE PAST, HOW CAN WE SOLVE BIG DATA TODAY?
Table of Contents
Introduction 3
What is Small Data? 4
Why Small Data Failed? 5
What is Big Data? 7
How can Big Data be More Beneficial? 9
The Question 11
Conclusion: 12
References 14
Introduction
In these days, every inch of everyone's life is based on technology. We wake up and the first thing we use is a product of technology and when we sleep the last thing, we mostly use is also a product of technology. Technology is everywhere round us. Moreover, no matter what class we belong to, technology is a part and parcel of our life. We comprise the generation, which is tech-savvy.
From waking up with the beeps of alarm in mornings to scrolling through the day's news to sleeping with a soothing song played in nights, our lives are concentrated around devices. Nevertheless, with technology comes a great share of data generation. Technological devices create data, which indirectly is created by humans. There can be data generated in small scale as well as huge scale.
Along with it comes the responsibility of solving the data co
ectly. This paper shall mainly focus on the head if we did not solve small data in the past, we would not be able to solve big data today. After looking at resources from over ten different credible journals, textbooks and articles, it can be understood that small data and big data go hands in hands. Small data alone cannot be solved to make predictions, but big data can be solved in order to create small data.
This small data solved is the prediction to help businesses create insights to allow them to thrive. Simply, small data is unable to create insights, it is the insight gathered from big data. They are both useful in one way or the other. Creating solutions for small data is not enough to make predictions. However, solving big data can enable the creation of small data. Small data cannot produce perception rather we need big data to get a clear picture in businesses. Data when solved with utmost precision can prove to be very helpful.
What is Small Data?
Small data are sets of data that are small in size. Small data, as mentioned by Cubuk, Sendek and Reed (2019), are capable of making short-term decisions or decisions that involves only the present. Small data can store and make decisions but it cannot be used to evaluate large-scale sums such as the demands or necessities of a certain market group in a given amount of time. Small data is rather useful for a short span of time.
Since business nowadays are very serious regarding where they stand and the competition is raising higher every day, people avoid small data since if you look at the bigger picture short term investments such as this can hurt the company economically. Almost everything cu
ently in progress and the data of which, can be acquired in an Excel file.
Small data, as mentioned by Xu, Nash and Whitmarsh (2020), is also useful in decision-making but is not intended to have a large impact on business, rather for a short period of time. Simply put, Small data is data that is accessible, concise and workable on a smaller work group or for a minimum amount of time.
Below there is a table that shows the strong points and week points of small data and the criteria that it fulfils.
Volume
Limited-Large
Exhaustivity
Samples
Resolution and Identification
Coarse and weak-tight and strong
Relationality
Weak-strong
Velocity
Slow, freeze-framed
undled
Variety
Limited-wide
Flexible and Scalable
Low-middle
Here from the table, we can see that there are various criteria that have to be fulfilled by a data set. These are volume, exhaustivity, resolution, identification, relationality, velocity, variety, flexibility and scalability and we can here draw the conclusion that in each of the criteria above, the performance of small data ranges from below average to average.
Small data thus is useful for cottage businesses, small firms, one-time decisions regarding something or decision-making process that are barely important. The small data is also very traditional. Now with the development of technology new things and new alternatives have come up, which resulted to the invention of a far better substitute of small data. In addition, people generally tend to go with the modern and improved alternative.
Thus, we generally see big market agencies as well as the government using big data, because, in spite of being easier to work with from the outside, small data cannot take the load that the population of a country or even a big market agency demands. Although even today small data are used for various purposes all over the world, however, there are more reasons that are deep and important that resulted in the failure of big data on a large scale. Let us now discuss extensively the reasons that led to the failure of big data on a large-scale basis.
Why Small Data Failed?
Small data has its fair share of advantages. It is useful in analyzing day-to-day affairs. Although, now, small data is, for examples, like a 12th century thing that is of no proper use anymore. It goes in a specific direction and solves at individual level, which makes it very difficult to work with, compared to big data. However, it has its own set of limitations and these limitations are so many and so deep that Big Data turned out to be much more advantageous to be worked with.
Because of its limitations, it is replaced with big data at many instances. Small data cannot be used to scrutinize larger problems. It tends to solve problems that are only very small and mainly deals with one particular individual at a time. It is limited and not applicable to a larger mass of people. Small data is also very unpredictable and this particular criterion of small data being unpredictable may sometimes even result in...