Name: Youssef Mohamed El-Sayed
The Definition of Big Data
What exactly is big data?
To really understand big data, it’s helpful to have some historical background. Here is Gartner’s
definition, circa 2001 (which is still the go-to definition): Big data is data that contains greater
variety arriving in increasing volumes and with ever-higher velocity. This is known as the three
Vs.
Put simply, big data is larger, more complex data sets, especially from new data sources. These
data sets are so voluminous that traditional data processing software just can’t manage them. But
these massive volumes of data can be used to address business problems you wouldn’t have been
able to tackle before.
Characteristics
Volume
The quantity of generated and stored data. The size of the data determines the value and
potential insight, and whether it can be considered big data or not.
Variety
The type and nature of the data. This helps people who analyze it to effectively use the
resulting insight. Big data draws from text, images, audio, video; plus it completes missing
pieces through data fusion.
Velocity
The speed at which the data is generated and processed to meet the demands and
challenges that lie in the path of growth and development. Big data is often available in realtime. Compared to small data, big data are produced more continually. Two kinds of velocity
related to big data are the frequency of generation and the frequency of handling, recording,
and publishing.
Veracity
It is the extended definition for big data, which refers to the data quality and the data value.
The data quality of captured data can vary greatly, affecting the accurate analysis.
Extensional
If new fields in each element of the data collected can be added or changed easily.
Scalability
If the size of the data can expand rapidly.
Value
The utility that can be extracted from the data.
Variability
It refers to data whose value or other characteristics are shifting in relation to the context
they are being generated.
Fine-grained and uniquely lexical
Respectively, the proportion of specific data of each element per element collected and if the
element and its characteristics are properly indexed or identified.
Relational
If the data collected contains commons fields that would enable a conjoining, or metaanalysis, of different data sets.
Why Is Big Data Important?
The importance of big data doesn’t revolve around how much data you have, but what you do
with it. You can take data from any source and analyze it to find answers that enable 1) cost
reductions, 2) time reductions, 3) new product development and optimized offerings, and 4)
smart decision making. When you combine big data with high-powered analytics, you can
accomplish business-related tasks such as:

Determining root causes of failures, issues and defects in near-real time.

Generating coupons at the point of sale based on the customer’s buying habits.

Recalculating entire risk portfolios in minutes.

Detecting fraudulent behavior before it affects your organization.
How Big Data works

Before businesses can put big data to work for them, they should consider how it flows among a
multitude of locations, sources, systems, owners and users. There are five key steps to taking charge of
this big “data fabric” that includes traditional, structured data along with unstructured and semistructured
data:
Set a big data strategy.
Analyze the data.
Identify big data sources.
Make data-driven decisions.
Access, manage and store the data.