DATA ANALYSIS DESCRIPTION
Data analysis is defined as a process of cleaning, transforming and modeling data with the objective of discovering useful information to assist in decision making.
A simple example is when we make a decision in our daily life thinking about what happened last time or what will happen when choosing that particular decision. It is nothing more than analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future.
The first type of data analysis is descriptive analysis, which responds to “what happened”. The major use of descriptive analysis in business is the tracking of KPIs. KPIs are measures of effectiveness and productivity of the different actions taken by companies.
After asking the main question of “what happened”, the next step is to go deeper and ask “why it happened”. This is where diagnostic analysis comes into play.
The diagnostic analysis takes the knowledge found from the descriptive analysis and goes deeper to find the causes of these results. Organizations make use of this type of analysis because it creates more connections between data and identifies patterns of behavior.
Predictive analytics shows “what is likely to happen” using previous data. An example of predictive analytics would be: if last year I bought two dresses based on my savings and this year my salary doubles, then I can buy four dresses. But, of course, it is not so easy because you have to think about other circumstances, such as the chances of clothing prices increasing, additional expenses….
This analysis makes predictions about future results based on current or past data. Forecasting is only an estimate. Its accuracy depends on how much information you have and how detailed it is.
Prescriptive analysis is based on the combination of information from all previous analyses to determine the course of action to be taken on a problem or decision. This analysis uses different simulation and optimization techniques to define the path to be followed. This is why it uses a higher computational level than the previous analyses.
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