Different terminologies are used in data analytics depending on the type of analytics. There is so much data that can be extracted from different sources today. Understanding raw data is quite a challenge given the unpredictable nature of some forms of data. This is where data analysis comes in. Analysis deals with refining raw data into an understandable and actionable form. Here are some of the types of data analysis you will encounter:
● Descriptive Analysis
Descriptive analysis is about summaries. From the data available, you should be able to find summary answers to pertinent issues in the organization, events, or activities. Some of the tools you will use in descriptive analysis include generated narratives, pie charts, bar charts, and line graphs. At a glance, someone should get a summary of the information you present before them.
● Diagnostic Analysis
Think about diagnostic analysis in the same way you see a doctor to provide a diagnosis about your health. More often, you are only aware of the symptoms you are feeling. It is up to the doctor to run tests and rule out possibilities, then narrow down a list of possibilities and tell you what you are suffering from. In a diagnostic analysis, the goal is to use data to explain the unknown. Assuming you are looking at your marketing campaigns on social media, for example, there are so many things you can look at, from mentions, to reviews, to the number of followers and likes. These are features that indicate some activity about your brand. However, it is only through a diagnostic analysis that you can go deeper and unearth what the numbers mean in as far as engagement goes.
● Predictive Analysis
Predictive analysis is one of the common types of analysis in use in organizations today. It uses a combination of statistical algorithms and machine learning to understand data and use this to extrapolate future possibilities from historical data. For accurate predictions, the historical data must be accurate, or the predictions might be flawed.
Predictive analysis is entirely about planning for the future. You use present and historical data to determine what might happen in the future, especially when you alter a few variables that you can control. These studies focus on creating predictive models for new data.
Here I am ending this post where we discussed some of the types of data analysis that we encounter. In the next post our focus would be on Tools Used in Data Analysis
● Exploratory Analysis
Exploratory analysis is about determining trends in your data, and from there
explaining some features that you might not have been able to determine through
other analytical methods. The emphasis is on identifying outliers to understand
why and where they occur, and the variables that are affected by the outliers in as far as decision-making is concerned.
● Prescriptive Analysis
Many of the forms of analysis you use will give you a general view of your data.
A general analysis cannot give you the kind of information you need.
Prescriptive analysis is about precision. The answers you get from this analysis
are specific. It is like getting prescription medicine – the doctor recommends
specific drugs, which should be taken under specific instructions.
Assuming you are looking at data about recent road accidents, through
prescriptive analysis, you can narrow it down to accidents as a result of drunk
driving, poor road signage, roadworthiness of the vehicles, or careless driving.
● Descriptive Analysis
Descriptive analysis is about summaries. From the data available, you should be able to find summary answers to pertinent issues in the organization, events, or activities. Some of the tools you will use in descriptive analysis include generated narratives, pie charts, bar charts, and line graphs. At a glance, someone should get a summary of the information you present before them.
● Diagnostic Analysis
Think about diagnostic analysis in the same way you see a doctor to provide a diagnosis about your health. More often, you are only aware of the symptoms you are feeling. It is up to the doctor to run tests and rule out possibilities, then narrow down a list of possibilities and tell you what you are suffering from. In a diagnostic analysis, the goal is to use data to explain the unknown. Assuming you are looking at your marketing campaigns on social media, for example, there are so many things you can look at, from mentions, to reviews, to the number of followers and likes. These are features that indicate some activity about your brand. However, it is only through a diagnostic analysis that you can go deeper and unearth what the numbers mean in as far as engagement goes.
● Predictive Analysis
Predictive analysis is one of the common types of analysis in use in organizations today. It uses a combination of statistical algorithms and machine learning to understand data and use this to extrapolate future possibilities from historical data. For accurate predictions, the historical data must be accurate, or the predictions might be flawed.
Predictive analysis is entirely about planning for the future. You use present and historical data to determine what might happen in the future, especially when you alter a few variables that you can control. These studies focus on creating predictive models for new data.
Here I am ending this post where we discussed some of the types of data analysis that we encounter. In the next post our focus would be on Tools Used in Data Analysis
● Exploratory Analysis
Exploratory analysis is about determining trends in your data, and from there
explaining some features that you might not have been able to determine through
other analytical methods. The emphasis is on identifying outliers to understand
why and where they occur, and the variables that are affected by the outliers in as far as decision-making is concerned.
● Prescriptive Analysis
Many of the forms of analysis you use will give you a general view of your data.
A general analysis cannot give you the kind of information you need.
Prescriptive analysis is about precision. The answers you get from this analysis
are specific. It is like getting prescription medicine – the doctor recommends
specific drugs, which should be taken under specific instructions.
Assuming you are looking at data about recent road accidents, through
prescriptive analysis, you can narrow it down to accidents as a result of drunk
driving, poor road signage, roadworthiness of the vehicles, or careless driving.
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