A Conceptual Framework for Big Data Analysis

Introduction

The term “big data” has recently grown in prominence as a way of describing the phenomenon of growth in data volume, complexity and disparity. The definition of big data is not totally consensual in literature and there may be some confusion around what it really means. Big data is not just an environment in which accumulated data has reached very large proportions. The word “big” does not just refer to size. If it was just a capacity issue the solution would be relatively simple. Instead, big data refers to environment in which data sets have grown too large to be handled, managed, stored and retrieved in an acceptable timeframe (Slack, 2012).

Big Data can be often characterized by three fundamental factors: volume, velocity, and variety. According to Wilson and Kerber (2011) only fifteen percent of the information today is structured information, or information that is easily stored in relational databases of spreadsheets, with their ordinary columns and rows. However, unstructured information, such as email, video, blogs, call center conversations, and social media, makes up about 85% of data generated today and presents challenges in deriving meaning with conventional business intelligence tools. Information-producing devices, such as sensors, tablets, and mobile phones continue to multiply. Social networking is also growing at an accelerated pace as the world becomes more connected. Such information sharing options represents a fundamental shift in the way people, government and businesses interact with each other.

The characteristics of Big Data will shape the way government organizations ingest, analyze, manage, store, and distribute data across the enterprise and across the ecosystem. Table 1 illustrates the characteristics of Big Data and highlights the difference of “Big Data” from the historical perspective of “normal” data.