Generally, quantitative data analysis deals with the interpretation and distribution of data. It explores the numerical or mathematical analysis of data, objective measurements, and statistics collated through questionnaires, polls, and surveys.
This analysis is key to communicating to stakeholders and business owner's important data and figures about their businesses.
Below are thirteen best techniques for quantitative data analysis every manager should know.
1. Correlation analysis
This is a statistical data analysis technique. It is used to determine if there is a relationship between two different quantitative variables and how strong that relationship is between the variables. It is usually used when it is suspected that two variables follow or have a similar evolution. Ex: the IBEX 35 index and the DAXX. You can learn more about correlation analysis here.
2. Regression analysis
It is another of the statistical data analysis techniques to investigate the relationship between different variables. It is used when one suspects that one of the variables may be affecting (independent variable) the behavior of the other (dependent variable) or others. You can go deeper into regression analysis here and here.
3. Data visualization
The data display is by far one of the quantitative data analysis techniques most demanded and appreciated today by how easy it through a graphic or image to detect patterns in the data. It is especially useful when we seek to understand large volumes of data in a fast and simplified way. Infographics and graphics are the most popular form of this type of technology that uses software such as Tableau Bourdieu or Quick View.
4. Analysis of scenarios
Scenario analysis consists of analyzing a certain variety of future events with alternative results. It is good to use it when we are not sure about what decision to make or what course of action to pursue. This is a good example of scenario analysis.
5. Data mining
Data mining or data mining is a data analysis process designed to work with large volumes of data. It is now better known as Big data and is used to detect patterns, relationships, or relevant information that can improve the performance of operations related to the customer and the Internet of Things.
6. Sentiment analysis
This is another data analysis technique that has become very popular thanks to social networks. Try to determine the attitude of an individual or group towards a particular topic. It is used when trying to understand the opinion of the different agents that interact in an industry. The great difficulty of this type of analysis is that it is based on subjective aspects that are very difficult to measure and have to do with human emotions.
7. Semantic analysis of texts
This type of analysis, also known as text mining, is a process that tries to extract value through the semantic analysis of large volumes of texts. Related to the prior art, it seeks that computers can understand what they index and obtain data from unstructured texts. The tool I know best for this type of analysis is BItext.
8. Analysis of patents and scientific literature
This data analysis technique uses metadata from scientific publications and patents to extract information on trends and relationships between studies, authors, or intellectual property. It is one of the most used techniques in the surveillance of technological trends.
9. Monte Carlo simulation
This mathematical probability technique is used to measure the approximate risk that a certain event will take place. It is very useful to understand the implications that a certain course of action derived from a decision may have.
10. Programming and mathematical optimization
Also known as linear optimization, it is a method to identify which is the best possible result given specific restrictions to our situation. It is widely used to solve given problems in production processes and determine how to minimize costs or maximize profits.
11. Mathematical prediction
It is a set of statistical techniques that uses time-series data to predict what is the most likely result that can be given shortly. The basis of these data analysis techniques is to look at what has happened in the past to know what will happen in the future. It is widely used in macro-economic projections.
12. Neural networks
This is perhaps one of the most complex data analysis techniques out there. The neural networks try to simulate the decision process and information from the brain or groups of neurons. The objective of these networks is to simulate the learning process of a human brain in a computer to facilitate decision-making in artificial intelligence.
13. AB Experiments
Also known as AB tests or split testing, they are one of the most used techniques in digital marketing to check the reaction of users to a message and see which one works best. It is mainly used to test hypotheses in the launch of a new product, an advertising campaign, or a message in an advertisement.
There you have it. Thirteen best data analysis techniques every manager should know. Yes, there are more, but the techniques listed here are without a doubt the most important you should know.
Modificado por última vez enViernes, 18 diciembre 2020 00:23