This article mainly describes the four most commonly used data analysis methods in the field of data mining analysis: descriptive analysis, diagnostic analysis, predictive analysis and directive analysis.
When analysts new to the field of data mining analysis were asked what was the most important capability of a data mining analyst, they gave a variety of answers.
In fact, what I want to tell them is that the most important ability in the field of data mining analysis is the ability to transform data into meaningful insights that non-professionals can clearly understand.
It is necessary to use some tools to help you better understand the importance of data analysis in mining the value of data. One of these tools is called 4D analysis.
Briefly, analysis can be divided into 4 key methods.
These four methods are described in detail below.
1. Descriptive Analysis: What Happened?
This is the most common analysis method. In business, this approach provides data analysts with important metrics and measures of business.
For example, monthly revenue and loss bills. Data analysts can access a large amount of customer data through these bills. Knowing the customer's geographic information is one of the "descriptive analysis" methods. The use of visualization tools can effectively enhance the information provided by descriptive analysis.
2. Diagnostic analysis: why does it happen?
The next step in descriptive data analysis is diagnostic data analysis. By evaluating descriptive data, diagnostic analysis tools allow data analysts to analyze data deeply, drilling down to the heart of the data.
A well-designed BI dashboard can integrate: data reading in time series, feature filtering, and drilling data to better analyze data.
3. Predictive analytics: what might happen?
Predictive analytics is primarily used to make predictions. The likelihood of an event happening in the future, predicting a quantifiable value, or estimating the point in time when an event will happen can all be done with predictive models.
Predictive models often use a variety of variable data to make predictions. The diversity of data members is closely related to the prediction results.
In an environment of uncertainty, forecasting can help make better decisions. Predictive models are also an important approach being used in many fields.
4. Imperative analysis: what needs to be done?
The next step in data value and complexity analysis is imperative analysis. The command model is based on an analysis of "what happened", "why it happened" and "what could happen" to help users decide what action to take. Usually, imperative analysis is not a method used alone, but an analysis method that needs to be completed at the end after all the previous methods are completed.
For example, traffic planning analysis takes into account factors such as the distance of each route, the speed at which each route is driven, and current traffic restrictions to help choose the best route home.
in conclusion
Finally, it should be noted that each analysis method is of great help to business analysis and is also applied in all aspects of data analysis.
Original link: http://www.kdnuggets.com/2017/07/4-types-data-analytics.html
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