Big data
The five Vs of big data ..
data volume :
More than ever, systems and people are interconnected. More data sources are
created as a result of this interconnectedness, resulting in a higher volume of data
than ever before (and constantly growing). Older data is being converted to digital
form, which raises the volume. To extract value (meaning) from the growing
volume of data, computing power must continuously rise. The amount of data that
is currently collecting cannot be handled by traditional computing technologies.
A data velocity :
Interconnection and advancements in network technology have increased the speed
and directions from which data enters the business. It is approaching quicker than
we can process it. It is more difficult to extract value (meaning) from the data the
faster it is produced and the more diverse the sources. Data entering at the speeds it
does now cannot be processed using traditional computing techniques.
Data variety :
There are more sources of data, which means there are more types of data in more
formats, including semi-structured and unstructured data from click streams, GPS
location data, social networking apps, and IoT as well as traditional documents and
databases (to name a few). Because different data formats require varying amounts
of data to be retrieved for processing in different ways, it is harder to extract value
(meaning) from the data. All of these diverse types of data cannot be processed
using conventional computing techniques.
data veracity :
Data typically contains noise, biases, and anomalies. It's probable that there is
some ambiguity involved with such a large volume of data. Once a lot of data has
been collected, it needs to be vetted, sanitised, and cleaned.
The five Vs of big data ..
data volume :
More than ever, systems and people are interconnected. More data sources are
created as a result of this interconnectedness, resulting in a higher volume of data
than ever before (and constantly growing). Older data is being converted to digital
form, which raises the volume. To extract value (meaning) from the growing
volume of data, computing power must continuously rise. The amount of data that
is currently collecting cannot be handled by traditional computing technologies.
A data velocity :
Interconnection and advancements in network technology have increased the speed
and directions from which data enters the business. It is approaching quicker than
we can process it. It is more difficult to extract value (meaning) from the data the
faster it is produced and the more diverse the sources. Data entering at the speeds it
does now cannot be processed using traditional computing techniques.
Data variety :
There are more sources of data, which means there are more types of data in more
formats, including semi-structured and unstructured data from click streams, GPS
location data, social networking apps, and IoT as well as traditional documents and
databases (to name a few). Because different data formats require varying amounts
of data to be retrieved for processing in different ways, it is harder to extract value
(meaning) from the data. All of these diverse types of data cannot be processed
using conventional computing techniques.
data veracity :
Data typically contains noise, biases, and anomalies. It's probable that there is
some ambiguity involved with such a large volume of data. Once a lot of data has
been collected, it needs to be vetted, sanitised, and cleaned.
