In this data-rich era, how to understand and analyze the data obtained in an enterprise has become an important driving force for the transformation and economic development of the enterprise. Today, data analysis has become one of the must-have skills in the Internet age.
Although the amount of data we create every day is huge, only 0.5% of the data is actually analyzed and used for data discovery, improvement and intelligence. Although this may not seem like much, considering the base of digital information we have, 0.5% of the data still accounts for a huge amount of information.
With such a large amount of data and such a short period of time, how to collect, manage, organize, and understand all these potential information that can promote business has become the cause of trouble for most people.
In order to help analyze and how to use data to improve business practices, we will not only explore data analysis methods and techniques, but also study different types of data analysis, and show how to perform data analysis in the real world.
What are data analysis methods?
First of all, the data analysis method focuses on obtaining raw data, mining information related to the main goals of the enterprise, and in-depth study of this information, transforming indicators, facts and figures into data that is conducive to the development of the enterprise for analysis.
Data analysis methods are diverse, mainly based on two core areas: quantitative data analysis methods and qualitative data analysis methods.
In quantitative and qualitative research, a better understanding of different data analysis techniques and methods will provide a clearer direction for information analysis. Therefore, it is very valuable to take the time to incorporate these specific knowledge into it.
The question has now been answered,’What is data analysis? ‘Considering different types of data analysis methods, I will teach you 10 steps to quickly complete data analysis.
1. Explore the needs
Before starting to analyze data or research analysis techniques in depth, sit down with all the small partners in the team to determine the main activities or strategic goals. It is very important to fundamentally understand which types are most beneficial to development, or which data is beneficial to development. The prospects are most helpful.
One step is wrong, and only when the foundation is consolidated can the purpose of data analysis be achieved.
2. Determine the problem
Once you have determined your core goals, you should consider which questions need to be answered to help you accomplish your goals. In order to help ask the right questions and ensure that the data is useful, asking questions and finding answers is essential.
3. Collect data
After providing real guidance for data analysis methods and knowing which questions need to be answered to obtain the best value in the available information, you should decide on the most valuable data source and start collecting, which is the most basic of all data analysis techniques One step.
4. Set KPI
Set up a series of key performance indicators (KPI), these indicators can track, measure and shape your progress in many key areas. KPI is very important to data analysis methods in qualitative research and quantitative research. It plays an important role in urging oneself to complete data analysis goals in time.
5. Ignore useless data
Reducing the amount of information is one of the most critical steps in data analysis, because it allows you to concentrate on the analysis and extract every drop of value from the remaining “lean” information.
Any statistics, facts, data or indicators that are inconsistent with business goals or KPI management strategies should be deleted from the equation.
6. Statistical analysis
This analysis method focuses on all aspects including clustering, homogeneity, regression, factors and neural networks, and will ultimately provide a more reasonable direction for data analysis methods.
The following is a brief glossary of these important statistical analysis terms:
- Clustering: The operation of grouping a group of elements so that the elements are more similar to each other (in a specific sense) than elements in other groups (hence the name “cluster”).
- Cohort: A subset of behavioral analysis that derives insights from a given data set (such as a web application or CMS), instead of treating everything as a broader unit, it divides each element into Related groups.
- Regression: A set of defined statistical processes, centered on estimating the relationship between specific variables, to deepen the understanding of specific trends or patterns.
- Factor: A statistical practice used to describe the observed variability between related variables, that is, the number of unobserved variables that may be called “factors” may be smaller. The purpose here is to find independent latent variables.
- Neural network: Neural network is a form of machine learning. It is too comprehensive to generalize, but this explanation will help draw a fairly comprehensive picture.
7. Integration technology
There are many ways to analyze data, but one of the most important aspects of successful analysis in a business environment is to integrate the right decision support software and technology.
The powerful analysis platform can not only extract key data from the most valuable resources, but also can be used in conjunction with dynamic KPIs to provide actionable insights, and can be displayed in a visual and interactive format from a central real-time dashboard information.
By integrating appropriate analysis techniques with statistical data analysis and core data analysis methods, dispersal of insights will be avoided, time and energy will be saved, and the enterprise will be able to obtain the greatest value from the most valuable insights.
8. Visualize your data
It can be said that the best way to make data analysis concepts present throughout the organization is through data visualization.
Online data visualization is a powerful tool that allows data trends and changes to be presented intuitively, so that users throughout the enterprise can extract digital information that is helpful for business development, and it also covers all the different Data analysis methods.
By 2020, everyone on the planet will generate approximately 7 megabytes of new information every second. A 10% increase in data accessibility will bring you more than $65 million in additional net income for your average Fortune 1000 company.
Ninety percent of the world’s big data was created in the past three years. According to Accenture’s data, 79% of well-known corporate executives believe that companies that do not accept big data will lose their competitive advantage and may face bankruptcy.
In addition, 83% of business executives have implemented big data projects to gain a competitive advantage.
Data analysis concepts may come in many forms, but fundamentally speaking, any reliable data analysis method will make the business more streamlined, cohesive, insightful and successful than ever before.
