Types of data analysis

Descriptive Analysis

Descriptive analytics is the process of looking at both current and past data to find patterns and trends. It's sometimes called the simplest way to look at data because it shows about trends and relationships without going into more detail.

Descriptive analytics is easy to use and is probably something almost every company does every day. Simple statistical software like Microsoft Excel or data visualisation tools like Google Charts and Tableau can help separate data, find trends and relationships between variables, and show information visually.

Descriptive analytics is a good way to show how things have changed over time. It also uses trends as a starting point for more analysis to help make decisions.

This type of analysis answers the question, “What happened?”.

Some examples of descriptive analysis are financial statement analysis, survey reports.

Diagnostic Analysis

Diagnostic analytics is the process of using data to figure out why trends and correlation between variables happen. It is the next step following identifying trends using descriptive analytics. You can do diagnostic analysis manually, with an algorithm, or with statistical software (such as Microsoft Excel).

Before getting into diagnostic analytics, you should know how to test a hypothesis, what the difference is between correlation and causation, and what diagnostic regression analysis is.

This type of analysis answers the question, “Why did this happened?”.

Some examples of diagnostic analysis are examining market demand, explaining customer behavior.

Predictive Analysis

Predictive analytics is the process of using data to try to figure out what will happen in the future. It uses data from the past to make predictions about possible future situations that can help make strategic decisions.

The forecasts might be for the near term or future, such as anticipating the failure of a piece of equipment later that day, or for the far future, such as projecting your company's cash flows for the next year.

Predictive analysis can be done manually or with the help of algorithms for machine learning. In either case, data from the past is used to make guesses or predictions about what will happen in the future.

Regression analysis, which may detect the connection between two variables (linear regression) or three or more variables, is one predictive analytics method (multiple regression). The connections between variables are expressed in a mathematical equation that may be used to anticipate the result if one variable changes.

Regression allows us to gain insights into the structure of that relationship and provides measures of how well the data fit that relationship. Such insights can be extremely useful for assessing past patterns and formulating predictions. Forecasting can help us to build data-driven plans and make more informed decisions.

This type of analysis answers the question, “What might happen in the future?”.

Some examples of predictive analysis are Marketing-behavioral targeting, Healthcare-early detection of a disease or an allergic reaction.

Prescriptive Analysis

Prescriptive analytics is the process of using data to figure out the best thing to do next. This type of analysis looks at all the important factors and comes up with suggestions for what to do next. This makes prescriptive analytics a useful tool for making decisions based on data.

In prescriptive analytics, machine-learning algorithms are often used to sort through large amounts of data faster and often more efficiently than a person can. Using "if" and "else" statements, algorithms sort through data and make suggestions based on a certain set of requirements. For example, if at least 50% of customers in a dataset said they were "very unsatisfied" with your customer service team, the algorithm might suggest that your team needs more training.

It's important to remember that algorithms can make suggestions based on data, but they can't replace human judgement. Prescriptive analytics is a tool that should be used as such to help make decisions and come up with strategies. Your judgement is important and needed to give context and limits to what an algorithm comes up with.

This type of analysis answers the question, “What should we do next?”.

Some examples of prescriptive analysis are: Investment decisions, Sales: Lead scoring.