How to analyze the data?

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. Grouping data:
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 collection:
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. Organize:
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. Clear the information from dark data:
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.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.
Resource Box:
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