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Big data analytics

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Definitions Edit

Big data analytics is

[t]he process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations, and other useful information.[1]

Big data analytics refers to:

1. Data analysis being undertaken that uses high volume of data from a variety of sources including structured, semi structured, unstructured or even incomplete data; and
2. The phenomenon whereby the size (volume) of the data sets within the data analysis and velocity with which they need to be analysed has outpaced the current abilities of standard business intelligence tools and methods of analysis.
3. The complexity of the relationships with complex structures embedded in the data has reached a level that cannot be handled by the current tools and models of statistics and analysis.[2]

Overview Edit

"Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources, which might be left untapped by traditional business intelligence (BI) systems. That's because traditional systems often fail to analyze numerous data sources, which include Internet click-stream data and Web server logs, social networking activity reports, cellphone call detail data and sensor-captured data."[3]

"Big data analytics can provide numerous opportunities for improvements in society. In addition to more effectively matching products and services to consumers, big data can create opportunities for low-income and underserved communities."[4]

References Edit

  1. TechTarget, "Big Data Analytics" (full-text).
  2. Australian Public Service Better Practice Guide for Big Data, at 4.
  3. Techopedia, "Big Data Analytics" (full-text).
  4. Big Data: A Tool for Inclusion or Exclusion?: Understanding the Issues, Executive Summary.

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