Data analysis is the process of developing answers to questions or issues through the examination and interpretation of data.
It involves presenting data findings in a way that supports policy and decision making and is easy to interpret,
In short, data analysis makes data more meaningful. It is the principal tool for obtaining information from data.
The basic steps in the analysis process include:
- Identifying objectives of the analysis or issues (what issues am I addressing?)
- Determining the availability of suitable data (what data am I using?)
- Deciding which analytical methods are appropriate for addressing the issues of interest (what statistical techniques are appropriate?)
- Applying the methods and evaluation, summarizing and communicating the results.
- And knowing your target audience (who is interested in this issue and why?)
Analytical results underscore the usefulness of data sources by shedding light on relevant issues. For example, some National Statistics Office programs depend on analytical output as a major data product as due to confidentiality concerns it is not possible to release the microdata to the public.
Data Analysis also plays a key role in data quality assessment by pointing to data quality problems for instance in a given census or Household Income and Expenditure (HIES) survey. Thus, analysis can influence future improvements to the Census or HIES survey.