Any kind of data, collected over a period of time, can be
organized, analyzed and interpreted. For example: a librarian keeps count of
the types of books that are of most interest to their patrons and, based upon
the popularity of each book, minimal amounts can be charged for borrowing the books over a
period of time. This helps aid revenue optimization for the librarian. Sitting
charges can also be added based on the traffic that the library attracts on a
daily basis. Data analysis is a smart science that allows for day to day
functions to become augmented.
Let us now look into the various steps that are taken in
order to analyze data
1.
It is essential to know the target group
for which information needs to be extracted. This information can be distilled
based on demographics (age group, income, education level, lifestyle),
geographical region (this distinction can be on the basis of national
boundaries or within a country), gender (some brands or products only appeal to
male or female and not to both), income group (this affects the way a product is
marketed and promoted), preferences (people are very interested in and follow
the latest trends and fads, it is necessary to keep an eye on this grouping as
well) etc.
2.
Data can be collected either from primary
or secondary sources. Primary sources are more reliable and provide dependable conclusions.
Secondary data should be collected from places that are also reliable because
only then can the data suggestions and conclusions that are reached during
analysis be helpful. For example: collecting information about how the
economies of the world are performing from The World Bank.
3.
There is a vast amount of data at hand that
needs to be segregated, understood and accepted. This can lead to problems and,
in order to ease the process, data needs to be organized in different ways
depending on its recurrence. This would help in streamlining the work.
4.
Dark data is large chunks of data that have
no use for the analyst who is working with the data. This data is redundant and
can only confuse the person that is working with the data. Dark data should be deleted
as it is considered to be garbage. The better sorted data is, the easier it
becomes for the analyst to draw useful conclusions.
Conclusion:
Big
data analysis is a term that is very in vogue now days. It can be rewarding to
a career when an individual has the proficiency to deal with analytics. data
science course Singapore institutions that offer online and offline courses
to individuals. Such courses allow the individual the capability to receive
placement in data companies and to earn a living.
