Data

2. It is the process of examining, at a
great speed, large volumes of data of
a wide variety of types and of great
value (the 4v model) to discover
hidden patterns, unknown
correlations and other useful
information, so that the results of the
Analysis can provide competitive
advantages to organizations in
relation to competition and produce
benefits for the business, such as
more effective and effective
marketing (marketing), and higher
revenues.

3. • Acquisition: The data will come from traditional
data sources (Company data warehouses, EDW,
related databases and files with transactional data),
and from a large number of unstructured data sources
that can be stored in NoSQL databases and "in
memory.“
• Organization of information: Prepare and
process the information to obtain the best
possible results from it, and on which the
advanced analytical techniques can be
applied as efficiently as possible.

4. • Analysis: Analyze all the information with
access to all data with advanced statistical
tools such as social and opinion mining, or
apply techniques developed with the R
programming language, specific for the
design of advanced statistics.
• Decision: Make decisions in real time or
as quickly as possible so that it can
positively affect the business of the
company.

5. Business analysis or business analysis is
the discovery and communication of
significant data patterns. Analytics allows to
achieve a competitive advantage for
organizations, especially those that are
more agile and innovative; it is a subset of
business intelligence and is generally
considered; However, the analytical is
reaching a lot of force and the terms begin
to separate, both in conceptual issues and
in tools.

6. •Analytics, has emerged according to Gartner as a
term that encompasses different initiatives and
related applications of business intelligence.
However, many other schools consider analytics as
the process of analyzing information from a given
domain such as web analytics or social analytics.
•Descriptive Analytics, consists of
preparing and analyzing historical data to
identify patterns and trends. Achieves a
deep knowledge from such data as reports,
dashboards, groupings, etc. Use the data
to explain what happened in the past.

7. •Predictive analytics, allows to discover
hidden patterns in data that the human expert
can not appreciate. It is the result of applying
statistical mathematics to the data. It consists
of using the data to determine what happens or
may happen in the future.
•Prescriptive analytics, uses the data
to prescribe those actions that
increase our chances of obtaining the
best results; determines new ways of
operating that can be achieved.

8. • Structured data, continue to maintain
hegemony over the remaining types, despite
the rapid growth of the unstructured and semi-
structured. Most of the data manipulated,
currently, through analytical platforms fall today
under the category of structured data.
• Semi-structured data are all those with
XML type formats and similar standards.
We will also group in this category, those
more complex types, coming from
hierarchical or old sources.
• Non-structural data of human activities,
such as text data.