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Running head: DATA ANALYTICS

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Data Analytics
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BLOCKCHAIN DEVELOPMENT

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Discussion

How do you describe the importance of data in analytics? Can we think of analytics
without data? Explain.
Data analytic involves an examination of datasets to come up with a summary that
contains necessary information. Data is known to be of use in analytics to provide information
from raw data. Data Analysts and scientists apply the technique in the decision-making for
business and field research. There are various importance of data in analytics. Some of this
importance include data in analytics that assist the businesses in optimizing their performances
and improving the processing power to multiply the profits. Data analytic helps a business
understand their respectful customers, develop products, and develop a content strategy. In other
words, companies can reduce costs by identifying comprehensive ways of storing large amounts
of data and practices of performing business activities (Wamba,2017).

An organization or a company can use data analytics to make correct business decisions
and analyze customers' satisfaction and trends. In healthcare systems, data analytics helps track
individual practitioner performance and helps in deriving systematic wastes of resources. Also in
health care systems, data analytics will help track populations' health, identifying people at risk
for chronic diseases. It does not make sense to say that analytics is there where there is no data
involved since data refers to raw material for analysis, and without data, there would be no
analytics. In other words, to perform analysis, we require data. These two practices go hand in
hand.

Considering the new and broad definition of business analytics, what are the main inputs
and outputs to the analytics continuum?

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The analytics continuum involves the ability to understanding the meaning of data and
the context in the business. Many tools help us understand data and put it into a context where it
will be actionable (Wamba, 2017). The analytic continuum consists of three disciplines, data
visualization, analytics, and Business intelligence. Data visualization allows the user to see
things in the data that might have impossibilities in discovering them. Analytics gives the user
the ability to view and predict data relationships. Business intelligence is the ability to collect
information and react to it. In Consideration of analytics definitions, every data from every
source can be viewed as an input.

Similarly, after performing the analytics, the output takes different forms depending on
the business's particular purpose. The primary input for business analytics is the human input. It
involves the data from human beings that is descriptive, diagnostic, predictive, and prescriptive
(Sharma et al., 2017). Once the user...