Data Analysis Guide

What is Data Analysis?
Data analysis is the use of data to guide business decision-making. This is done by sorting, structuring and converting data to obtain useful information relevant to business functions.
This is a habit we all adopt every day when making decisions. When making purchases, for example, we tend to research the best price, quality, or standard of a product before we make the purchase.
It becomes data analysis when the same premise is applied to the use of big data for business decisions.
What is Data Analysis Process?
Data analysis refers to the process of using data to inform business decisions. It breaks down the different processes data must go through before it can be used to guide business decisions.
Our blog Data Processing provides more information about the data processing process. Data analysis processing involves four basic stages. Each stage of the data analysis process is used to answer business questions.
This is the first step before we start data analysis. It is a good idea to identify business questions before you begin the data analysis process. The data process can be guided by knowing the questions that the business is trying answer.

1) Data collection –
It is necessary to collect raw data relevant to the business questions. These data can be from company logs or external sources such as government records.
2) Data Cleaning
Many of the data in big data sets are irrelevant or moot. Data must be cleaned to remove duplicates, repeated data and inconsistent data. The rest of the data must be properly structured.
3) Analysis of data
Data analysis is used to identify patterns, exceptions and variations within data sets. Data visualization can be assisted by data analysis software.
4) Data interpretation
The data analysis results are used to answer the original business questions.
What is Data Analysis in Research?
Analyzing data for business decisions is very similar to data analysis in research. The objective here is to examine the data and not answer pre-conceived questions.
Our brain is wired to spot patterns and observe them. Data analysis is no different. Sometimes, when we have a question before, we give into our biases and interpret the data in a way that benefits us story.
Research data must be analyzed without biases. Research can uncover and reveal surprising data. This information can only be accessed by being open-minded when analyzing data in research.
Data can be classified based on its nature into three Data Types to aid in data analysis in research.
Quantitative data –
Data that is represented by numerical values. These data represent values that can be calculated.
Qualitative data –
Data can be represented using descriptions or words. This data is easily observed and can be used to calculate value. It is however subjective and cannot be used for calculations.
Categorical data
Data that is readily available and presented in groups. It is important to remember that data not grouped in this way cannot be repeated in another group.
What are the different types of Data Analysis?
Despite the fact that the underlying data analysis process is identical, they are separated into different types of Data Analysis depending on the function served.

There are four main types of Data Analysis. These Data analyses are classified according to the type of business question they answer.

1) Descriptive analysis
These types of data analysis allow us to answer the question “What” happened regarding certain events. This is done using quantitative data to present statistics. These statistics can help identify any event.
Sales data, for example, can help us understand things such as the time per sale, money spent per sale, peak sales times, etc.
2) Diagnostic analysis
It helps to diagnose any occurrence, as the name implies. This allows us to understand “why” something happened.
If the descriptive analysis shows an increase in sales, then the diagnostic analysis can help us to understand why.
3) Predictive analysis
These types of data analysis, as the name implies, allow for informed predictions of future events. They answer the question “What might happen” in relation to any future process.
If the descriptive analysis shows an increase in sales, and the diagnostic analysis indicates an increased due to a lockdown in a specific month, the predictive analysis can help to predict what might happen in a future locking down.
4) Prescriptive analysis
These types of data analysis are intended to give a recommendation for action regarding any event. This answers the question “What should be done?” regarding a particular event.
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