With the development of the times, people generate a large amount of data on the Internet every day, which is a very precious resource for enterprises. Companies can make strategic adjustments and marketing deployments through data analysis. Especially for Internet companies, the data generated by user behavior is the company's most valuable resource.
In this case, BI vendors came into being. Baidu Baike defines BI as follows: BI is the English abbreviation of Business Intelligence, and the Chinese explanation is business intelligence. It is a collection of technologies used to help companies make better use of data to improve the quality of decision-making. It is a process of drilling information and knowledge from massive data. . Simply put, it is the process of business, data, and data value application.
For an enterprise, the realization of business intelligence is not only simply to install a system and collect data, but to transform these data into actionable business solutions. Companies can see which processes are working under the business surface from the data collected from various sources, and help the team prepare for future business development trends. However, without proper analysis and understanding of the data you collect, all you have are numbers and numbers without context.
More importantly, there is no suitable method to analyze the data. Without collecting data types according to your needs, even using the so-called "correct" data analysis methods and models will not help. This also makes it necessary to understand each type of data and which method of analysis can provide the best results. In this way, the data analysis software also contains some common technologies, which are effective. Next, I will introduce you to five data analysis methods, which can help you create more valuable and actionable data analysis programs.
Quantitative and qualitative data-what is the difference?
The first step in choosing the right data analysis technique for a data set is to understand the quantity or quality of data types. As the name implies, quantitative data involves quantities and hard numbers. These data include sales figures, marketing data (such as click-through rates), payroll data, income, and other data that can be objectively counted and measured.
Qualitative data is slightly difficult to determine because it involves all aspects of an organization and is more explanatory and subjective. This includes information obtained from customer surveys, interviews with employees, and usually refers to quality rather than quantity. Therefore, the structure of the analytical method used is not as certain as quantitative techniques.
Quantitative data analysis
Quantitative analysis methods rely on the ability to accurately count and interpret data based on hard facts. Our first three methods to improve data analysis capabilities will focus on quantitative data:
1. Regression analysis
When you need to make predictions and predict future trends, regression analysis is a good tool. Regression measures the relationship between the dependent variable (variable to be measured) and the independent variable (data used to predict the dependent variable). Although you can only have one dependent variable, you can have an almost unlimited number of independent variables. Regression can also help you discover business points in your operations that can be optimized by highlighting the relationships between trends and factors.
2. Hypothesis testing
This analysis method is also called "T test", which compares the data you have with the hypothesis. It can also predict how possible decisions will affect your business. T test can compare two variables to find correlation and make decisions based on the results. For example, actual business may assume that more working hours are equivalent to higher productivity. Before implementing the extension of working hours, it is important to ensure that there is a real effect in order to avoid adverse reactions.
3. Monte Carlo simulation
As one of the most commonly used methods to calculate the impact of unpredictable variables on specific factors, Monte Carlo simulation uses probabilistic modeling to help predict risk and uncertainty. In order to test hypotheses or scenarios, Monte Carlo simulation will use random numbers and data to analyze various possible results for any situation based on any result. This is a very useful data analysis method that can be applied across multiple fields, including project management, finance, engineering, logistics, and so on. By testing various possibilities, you can understand how random variables affect your plans and projects.
Measure qualitative data
Unlike quantitative data, qualitative information needs to shift from purely statistical data to a more subjective approach. However, it is still possible to extract useful data by using different data analysis techniques according to needs. Our last two techniques focus on qualitative data.
content analysis
This method helps to understand the overall theme that appears in the qualitative data. Techniques such as color-coding specific topics and ideas using word clouds can help analyze text data to find the most common threads. When dealing with user feedback, interview data, open surveys and other data, content analysis can work well. This helps identify the most important areas for improvement.
Narrative analysis
Narrative analysis mainly includes five elements, namely, act, scene, agent, agency and purpose. This analysis focuses on the way stories and ideas are communicated throughout the company and can help you better understand the organizational culture. This may include explaining how employees feel about their work, what customers think of the organization, and how to view operational processes. It is very useful when considering changing corporate culture or planning new marketing strategies.
There is no gold standard for statistical analysis, and there is no absolutely correct method. The method chosen should always reflect the data collected and the type of solution to be extracted. Matching the correct data and analysis helps to find better solutions to optimize the business of the enterprise and carry out digital transformation of the business.