"If we didn't solve small data in the past, how can we solve Big Data today?"

Ravi, Akash

doi:10.48550/arxiv.2111.04442

Cited by 2 publications

(2 citation statements)

References 13 publications

(13 reference statements)

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“…Among the various related works, Akash Ravi [6] provides a comprehensive and systemic survey on the exact issue discussed in this paper. The author has identified the potential that can be unlocked by Big Data and the ways in which organizations can leverage them regardless of how they may or may not have utilized small data.…”

Section: Literature Surveymentioning

confidence: 99%

Solving Big Data Based on Takeaways from Experiences with Small Data

Kotha¹

2023

Preprint

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With the rapid growth of digital technologies, the amount of data being generated and collected has increased dramatically, leading to the development of the concept of Big Data. Businesses must undergo a digital transformation and equip themselves to extract the maximum value from this data. Techniques for data analytics can be applied to improve strategic decision-making. Although the general goal of data analytics is largely invariant, the data itself can be found in many different forms and be categorized in a variety of circumstances. In this article, we investigate concepts like ”small” and ”big” data, analyze their characteristics, and consider how they can be valuable. The study specifically looks into the issue of ”How can we solve Big Data now if we couldn’t solve small data in the past?”. The paper highlights the advantages of taking a systematic approach to data analysis and the importance of considering the context and structure of the data when developing big data solutions. Based on the findings, this paper posits that firms may still benefit from big data with the correct technology and corporate strategy, regardless of how small data may have been used.

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Section: Literature Surveymentioning

confidence: 99%

Solving Big Data Based on Takeaways from Experiences with Small Data

Kotha¹

2023

Preprint

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“…Furthermore, small data focuses on answering specific questions or queries. Hence, it is difficult to apply its findings to large groups of events and activities (Ravi 2021).…”

Section: Small Data In Organisationsmentioning

confidence: 99%

The logical differentiation between small data and big data

Nyikana,

Iyamu

2023

SA Journal of Information Management

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Big data is defined by its characteristics known as the 4Vs: volume, velocity, veracity and variety (Sun, Strang & Li 2018;Osman 2019). According to Barham (2017), volume refers to size, which entails the scale of data. Velocity is the speed at which data travels, including how the data or set of data is streamed and flows in exchanges (Iyamu 2018). Veracity is the complexity and uncertainty of data (Lam et al. 2017). Variety refers to the different forms of data (Barham 2017). Background:The distinction between small data and big data is increasingly muted and has caused challenges and confusion in many quarters. Objective:The objective of the study is to gain a deeper understanding of the confounded confusion that exists between small data and big data. Firstly, to develop a taxonomy that distinguishes between small data and big data. Secondly, it seeks to extract the value from the concepts, which can be of fundamental importance to an organisation.Methods: This study follows the interpretive approach and employs qualitative methods, based on which 57 related materials were gathered, covering big data and small data, and analysed. Results:The study reveals the factors that differentiate the concepts, which are of a technical front, business logic and data processing. Conclusion:This study addresses the challenges which are increasingly of prohibitive ramifications for both academic and business domains. By removing the confusion, the classifications of small data and big data including associated attributes will be better understood. This increases their business use towards enhancement and competitive advantage. Contribution:The article distinguishes between small data and big data, which has been missing, from both academic and business perspectives, since the emergence of the latter. The differentiation between small data and big data provides a guide to organisations in developing strategic frameworks and operational plans.

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"If we didn't solve small data in the past, how can we solve Big Data today?"

Cited by 2 publications

References 13 publications

Solving Big Data Based on Takeaways from Experiences with Small Data

Solving Big Data Based on Takeaways from Experiences with Small Data

The logical differentiation between small data and big data

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