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

TL;DR

This paper explores the transition from small data to big data, emphasizing that organizations can leverage big data effectively with appropriate technology and vision, despite past challenges with small data solutions.

Contribution

It provides insights into the attributes of small and big data and discusses how lessons from small data can inform big data strategies.

Findings

Organizations can leverage big data with the right technology.

Understanding data attributes is crucial for value addition.

Past small data challenges inform current big data solutions.

Abstract

Data is a critical aspect of the world we live in. With systems producing and consuming vast amounts of data, it is essential for businesses to digitally transform and be equipped to derive the most value out of data. Data analytics techniques can be used to augment strategic decision-making. While this overall objective of data analytics remains fairly constant, the data itself can be available in numerous forms and can be categorized under various contexts. In this paper, we aim to research terms such as 'small' and 'big' data, understand their attributes, and look at ways in which they can add value. Specifically, the paper probes into the question "If we didn't solve small data in the past, how can we solve Big Data today?". Based on the research, it can be inferred that, regardless of how small data might have been used, organizations can still leverage big data with the right technology and business vision.