What do you mean by Data Analytics?
It is the process of exploring data sets so as to draw
conclusions regarding the information they have with the use of software and
specialized systems. The techniques and technologies of data analytics are used
widely in commercial industries to allow organizations to make more informed business
decisions in order to disprove and substantiate scientific model hypotheses and
theories. Data Analytics mainly refers to the mixture of applications like BI
(Business Intelligence), OLAP (Online analytical processing) into multiple
advanced analytics forms. It has a similarity with Business analytics, but
business analytics uses business, whereas data analytics’ focus is broad. Data
Analytics’ capability can help improve operational efficiency; businesses’
revenue increase, customer service efforts and optimize marketing campaign's
response to emerging market trends quickly and obtain, over rivals, a
competitive edge - ultimately with the goal of improving business performance. Depending
on the specific application, the analyzed data can consist of either new
information or historical records which, for real-time analytics, have been
processed.
Data Analytics and its Types
There are at least four types of Data Analytics. Given below
are the detailed descriptions of various types of Data Analytics.
1.
Descriptive Analytics: The question of what
happened is answered by Descriptive Analytics. For example, a provider of health
care will know the number of patients that were hospitalized the last month;
retailer knows the volume of average weekly sales; manufacturer knows the
products rate which is returned for the past month. Descriptive Analytics organizes
raw data from various data sources in order to give precious insights into the
past.
2.
Diagnostic
Analytics: The question of why something happened is answered by Diagnostic
Analytics. Thanks to the diagnostic analytics, there is a chance to drill down,
identify patterns and find out the dependencies. Companies choose Diagnostic
Analytics because, for a specific problem, it offers in-depth insights. At
their disposal, a company should contain detailed information or else the collection
of data may be time-consuming for every issue.
3.
Predictive Analytics: The question of what will likely
happen is answered by Predictive Analytics. Predictive Analytics uses the
findings of diagnostic analytics and descriptive analytics in order to detect
clusters, expectations, predict future trends and tendencies which, for
forecasting, makes it a valuable tool. Notwithstanding many advantages brought
by predictive analytics, it is necessary to understand that forecast is an
estimate whose accuracy depends highly on stability and data quality of a situation;
therefore, continuous optimization and careful treatment is required. Thanks to
the predictive analytics for enabling a proactive approach, for instance, a
telecom company can recognize the subscribers who would most likely reduce
their spending.
4.
Prescriptive Analytics: The question of what
action can be taken in order to take advantage fully or eliminate the problem
of promising trend is answered by Prescriptive Analytics. For example, a
multinational company could identify opportunities for purchases that were
repeated based on sales history and customer analytics. Prescriptive Analytics
uses standard technologies and tools like algorithms, machine learning and
business rules which make it standard to manage and implement.
Resource Box
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