In this era of rich data, how to understand and analyze the data obtained by enterprises has become an important driving force to promote the transformation and development of enterprises and economic development. Today, data analysis has become a must-have skill in the Internet age.

Despite the huge amount of data we create every day, only 0.5% of it is actually analyzed and used for data discovery, improvement, and intelligence. While that may not seem like much, 0.5% is still a huge amount of information given the digital information base we have.

With so much data and so little time available, how to collect, manage, organize and make sense of all this potentially business-boosting information is a source of frustration for most people.

To aid in analytics and how data can be used to improve business practices, we will explore data analysis methods and techniques, examine different types of data analysis, and show how data analysis can be performed in the real world.

What are data analysis methods?

First of all, data analysis methods focus on obtaining original data, mining information related to the main objectives of the enterprise, and in-depth study of these information, indicators, facts and figures into conducive to the development of the enterprise data for analysis.

There are a variety of data analysis methods based on two core areas: quantitative data analysis methods and qualitative data analysis methods.

In quantitative and qualitative research, better understanding of different data analysis techniques and methods will provide a clearer direction for information analysis work. Therefore, it is very valuable to take the time to integrate this specific knowledge.

Now we’ve answered the question, ‘What is data analytics? Taking into account the different types of data analysis methods, we will show you how to do data analysis quickly in 10 steps.

First, discuss the needs

Before you start analyzing data or delving into analytics techniques, it’s critical to sit down with all of your team members and identify key activities or strategic goals, to fundamentally understand which types are best for growth, or which data are most helpful for growth prospects.

Step by step wrong, only to consolidate the foundation, in order to achieve the purpose of data analysis.

Two, determine the problem

Once you have identified your core goals, you should consider what questions need to be answered to help you achieve your goals. To help ask the right questions and ensure that data is useful, asking questions and seeking solutions is essential.

Third, collect data

Once you have provided real guidance on data analysis methods and know which questions need to be answered to get the best value out of the available information, you should decide on the most valuable data sources and start collecting, which is the most fundamental step in all data analysis techniques.

4. Set KPI

Set up a set of key performance indicators (KPIs) that track, measure, and shape your progress across many key areas. KPI is crucial for data analysis methods in qualitative research and quantitative research, and it plays an important role in urging oneself to complete data analysis goals in time.

Ignore useless data

Reducing the amount of information is one of the most critical steps in data analysis because it allows you to focus on analytics and extract every last drop of value from the remaining “lean” information.

Any statistics, facts, figures or indicators that are inconsistent with business objectives or KPI management strategies should be removed from the equation.

6. Statistical analysis

This analysis method focuses on various aspects including clustering, homogeneity, regression, factor and neural network, which will eventually provide a more reasonable direction for data analysis methods.

Here 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 particular sense) than to elements in other groups (hence the term “cluster”).
  • Congener group: A subset of behavior analysis that draws insights from a given set of data, such as a Web application or CMS, rather than looking at everything as a broader unit, each element is divided into related groups.
  • Regression: A set of defined statistical processes centered on estimating relationships between specific variables in order to deepen understanding of a particular trend or pattern.
  • Factor: A statistical practice used to describe the observed variability between related variables, i.e. the number of unobserved variables that may be called “factors” may be smaller. The goal here is to find independent potential variables.
  • Neural networks: Neural networks are a form of machine learning that is too comprehensive to generalize, but this explanation will help paint 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 technologies.

Powerful analytics platforms can not only extract key data from the most valuable resources, but can also be used in conjunction with dynamic KPIs to provide actionable insights and display information in a visual, interactive format from a central real-time dashboard.

By integrating appropriate analytical techniques in statistical and core data analysis methods, insights will be avoided, time and effort saved, and the enterprise will get the most value out of the most valuable insights.

Visualize your data

Arguably, the best way to make data analysis concepts visible throughout the organization is through data visualization.

Online data visualization is a powerful tool for visualizing trends and changes, enabling users across the enterprise to extract digital information that will help them grow their business. It also covers all the different data analysis methods.

By 2020, every person on earth will generate about 7 megabytes of new information every second. A 10% increase in data accessibility will generate more than $65 million in additional net revenue for your average Fortune 1000 company.

Ninety percent of the world’s big data has been created in the last three years, and according to Accenture, 79 percent of prominent business executives believe companies that don’t embrace big data will lose their competitive edge and may face bankruptcy.

In addition, 83% of business executives have implemented big data projects to gain competitive advantage.

Data analytics concepts may take many forms, but ultimately, any proven data analytics approach will make businesses leaner, more cohesive, more insightful, and more successful than ever before.