Database Reference
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the more intelligent it becomes. If you're familiar with control systems or
control theory, which adapt based on observations, it's a similar kind of loop-
ing process. As a simple analogy, think about how much easier it is to finish
a puzzle when it's almost done, or even when you have the outside framed.
In an RTAP system, the more pieces you identify and bring to the frontier of
the business, the more complete a picture you have of the topic of interest,
and you have it earlier in the cycle.
Data Here, Data There,
Data, Data Everywhere: The Veracity of Data
Veracity
is a term that's being used more and more to describe Big Data; it
refers to the quality or trustworthiness of the data. Tools that help handle Big
Data's veracity transform the data into trustworthy insights and discard
noise.
Collectively, a Big Data platform gives businesses the opportunity to ana-
lyze all of the data (whole population analytics), and to gain a better under-
standing of your business, your customers, the marketplace, and so on. This
opportunity leads to the Big Data conundrum: although the economics of
deletion have caused a massive spike in the data that's available to an orga-
nization, the percentage of the data that an enterprise can understand is on
the decline. A further complication is that the data that the enterprise is try-
ing to understand is saturated with both useful signals and lots of noise (data
that can't be trusted, or isn't useful to the business problem at hand), as
shown in Figure 1-2.
We all have firsthand experience with this; Twitter is full of examples of
spambots and directed tweets, which is untrustworthy data. The
Figure 1-2
As the amount of data that is available to an organization increases, the
relative amount of data that it can process decreases.
