The six Vs of big data
n Volume (data at rest):
terabytes to exabytes, petabytes
to zettabytes of lots of data
n Velocity (data in motion):
streaming data, milliseconds to
seconds, how fast data is being
produced and how fast the data
must be processed to meet the
need or demand
n Variety (data in many forms):
structured, unstructured, text,
multimedia, video, audio, sensor data,
meter data, html, text, e-mails, etc.
n Veracity (data in doubt):
uncertainty due to data
more than half of all analytics projects
fail because they aren’t completed
within budget or on schedule, or because they fail to deliver the features
and benefits that are optimistically
agreed on at their outset.
Today, an abundance of knowledge
and experience exists to have successful data and analytics-enabled decision
support systems. So why do so many
of these projects fail, and why are so
many executives and users still so unhappy? While there are many reasons
A NA L Y T I C S
inconsistency and incompleteness, ambiguities, latency, deception, model approximations,
accuracy, quality, truthfulness or
trustworthiness
n Variability (data in change):
the differing ways in which the data
may be interpreted; different questions require different interpretations
n Value (data for co-creation and
deep learning): The relative importance of different complex data from
distributed locations. Big data with
deep analytics means greater insight
and better decisions, something that
every organization needs.
for the high failure rate, the biggest reason is that companies still treat these
projects as just another IT project. Big
data analytics is neither a product nor a
computer system. Instead, it should be
considered a constantly evolving strategy, vision and architecture that continuously seeks to align an organization’s
operations and direction with its strategic business goals and tactical and operational decisions. Table 1 includes a
list of common mistakes that can doom
analytics projects.
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