When an enterprise has a large amount of data, how to analyze and extract information that helps business success from the data is one of the main driving forces for the success of the enterprise.
Although we generate a large amount of data every day, we actually only analyze 0.5% of the data, and then use it for data discovery, improvement and intelligence processing. It seems very small, but only 5% of the data is a huge amount of data for the staff, because the amount of digital information generated by an enterprise is very large.
When the amount of data is large and the time is short, knowing how to collect, manage, organize, and understand all the information that may help business development is indeed a big challenge for data analysts.
Therefore, this article focuses on how to tap the potential of data analysis, understand its meaning, and how to use it to enhance your business practice, discuss the methods and techniques of data analysis, and show how to conduct data analysis in the real world.
First, briefly explain what data analysis is?
Data analysis is a process that relies on the following methods and techniques: obtaining raw data, mining data information insights related to the main business goals, and in-depth study of this information to transform indicators, facts, and data into improvement plans.
There are various methods of data analysis, which are mainly based on two core areas: quantitative data analysis methods and data analysis methods in qualitative research.
1. How to analyze data: master these 15 data analysis methods and techniques
1. Collaborate to determine requirements
Before starting to analyze data or delve into any data analysis technology, it is important to sit down with all the key stakeholders in the enterprise, determine the main data analysis activities or strategic goals, and be familiar with the basic situation of the data types to be used , To obtain a vision that is most conducive to providing you with the development organization you need.
2. Create a problem
After you have outlined your core goals, you should consider which questions need to be answered to help you complete the task. This is one of the most important data analysis techniques, and it will lay the foundation for your success.
To help you ask the right questions and ensure that the data is useful to you, you must ask the right data analysis questions.
3. Collect data
After providing the real direction for the data analysis method and knowing which questions need to be answered in order to get the best value from the available information of the data, you should decide on the most valuable data source and start collecting data information-this is all data The most basic of analytical techniques.
4. Set KPI
After setting up the data source, collecting the raw data that you think can provide potential value and determining the clear questions you want the data analysis to answer, you also need to set up a series of key performance indicators (KPI) to help in many key areas in the data analysis process Track, measure and shape progress.
KPIs are essential for both analytical methods in qualitative and quantitative research. This is one of the main methods of analyzing data, and of course it cannot be ignored.
5. Ignore useless data
After determining the data analysis techniques and methods and determining the tasks, you should browse the raw data collected from all sources and use KPIs as a reference to eliminate information that is deemed useless.
Reducing the amount of information is one of the most critical methods of data analysis, because it allows you to focus on effective data analysis and extract every drop of value from the remaining "simplified" information.
Any statistics or indicators that are inconsistent with business goals or KPI management strategies should be removed from the equation.
6. Perform statistical analysis
Statistical analysis is one of the most critical types.
This analysis method focuses on various aspects including clustering, homogeneity, regression, and factors, and will ultimately provide a more reasonable direction for data analysis methods.
The following is a brief glossary of these important statistical analysis terms for your reference:
- Clustering: The operation of grouping a group of elements so that the elements are more similar to each other (in a specific sense) than the elements in other groups (hence the name "cluster"). This method is used to provide interrelationships for trends or databases.
- 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 breaks down each element into Related groups. By using this analysis method, you can gain in-depth understanding of consumer needs or a deeper understanding of a broader target group.
- Regression: A set of determined statistical processes centered on estimating the relationship between specific variables to deepen the understanding of specific trends or patterns. This is a very powerful method of analysis when focusing on predictive analysis.
- Factor: A statistical practice used to describe the observed variability between related variables, that is, the number of potential unobserved variables called "factors" may be smaller. The purpose here is to find independent latent variables. An ideal analysis method for simplifying specific data segments.
- Data modeling: An analytical method that is a general term for engineering indicators that use strategies and technologies as well as insights into other values, directions, and environments. When considering how to analyze data, adopting a data modeling mentality is critical to success, so this is an area worth exploring in more detail.
- Predictive analysis: Again, although this is a broad term (and we will re-discuss it in due course), when you are looking for how to analyze in the right way, you should make sure to firmly root the term. Predictive analysis technology is essential, because predictive analysis technology will enable you to discover valuable trends that are constantly evolving and make plans. These plans can not only enhance your various operational processes, but also help you gain a vital advantage in the competition. .
7. Establish a data management flow chart
This step is optional (a lot of insight has been gained before this and a fairly reasonable strategy has been formed), but the creation of a data governance roadmap will help the data analysis methods and techniques to succeed in a more sustainable way. . These roadmaps (if developed properly) can also be constructed, so they can be adjusted and scaled over time.
Spend enough time to develop a flowchart that will help you store, manage, and process data internally, and make your analysis techniques more fluid and practical-this is one of the most powerful data analysis methods available today.
8. Technology to integrate data analysis information
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.
A powerful data analysis platform not only allows you to extract key data from the most valuable resources, but it can also be used in conjunction with dynamic KPIs to provide actionable insights. It will also display data analysis information in a digestible, visual, and interactive format through a large central real-time data visualization screen. It is a data analysis method worth relying on.
By integrating the right technology for statistical method data analysis and core data analysis methods, you will avoid distracting insights, save time and energy, and allow you to get the most value from the most valuable insights of the enterprise.
9. Answer your question
By considering each of the above-mentioned efforts, using the right technology, and developing a cohesive internal culture, everyone analyzes data in different ways and the power of digital intelligence, and will quickly begin to answer the most pressing business questions . It can be said that the best way to make data concepts accessible throughout the organization is through data visualization.
10. Visualize your data
The SaaS data visualization platform is a powerful tool that allows you to tell stories with indicators, so that users throughout the enterprise can extract meaningful insights that help business development-and it covers all the analysis data Different ways.
The purpose of data analysis is to make the entire organization wiser, smarter, and use the appropriate platform or large data screen, which is easier than you think.
Source: Kangaroo Cloud EasyV data visualization platform case
The dynamic and interactive library data visualization large screen shown in the figure above can display the behavior of real-time users and visitors of the library, and propose a large number of index rates based on the KPI of page bounce, thereby producing a comprehensive data analysis report , The user can interact with it and make adjustments.
This centralized data analysis information combination provides real insights into how people interact with your business, content, and products. It can help identify weaknesses, leverage strengths, and make data-based decisions, thereby multiplying business benefits.
11. Implement text analysis
Large amounts of data collected by companies are unstructured.
Text analysis is also called text mining in the industry. It is the process of obtaining large amounts of text data and organizing it in a way that makes it easy to manage. Through a rigorous clean-up process, it will be able to extract data that is truly relevant to the business and use it to develop actionable insights to drive the business forward.
Modern analysis tools and techniques can accelerate the process of text analysis to help gather and organize insights in an efficient and results-oriented manner.
In general, we create a huge amount of 250 million bytes of digital data every day, a large part of which is text-based. By investing in data analysis tools and technologies that will help extract insights from various text-based data sources, including product reviews, articles, social media communications, and survey responses, you will gain valuable to your audience and their needs Insights, preferences and pain points.
By mastering this knowledge, it is possible to create campaigns, services and communications that can meet the needs of potential customers, thereby increasing customer retention while expanding the audience. This is one of the most effective data analysis tools and technologies that require investment.
12. In-depth diagnostic analysis
Regarding the course on how to conduct analysis, in-depth study of diagnostic analysis is essential. Designed to provide direct and feasible answers to specific questions, this is one of the most important research methods in the world, which includes other key organizational functions such as retail analysis, such as
Diagnostic data analysis is a powerful form of data discovery that can support analysts and business executives by helping them gain a solid contextual understanding of the cause of an incident. If you know why something happened and why it happened, you will be able to find the exact solution to the problem or challenge. To gain a practical understanding, it is essential that you have knowledge of the following two aspects:
a) Predictive analysis
If you understand why trends, patterns, or events occur through data, you can make wise predictions about the development of your business in a particular area. In this way, it is possible to plan or initiate activities before the curve, thereby defeating competitors. In addition, if you can take advantage of the predictive advantages of diagnostic analysis, you can prevent potential problems or inefficiencies, thereby losing control and eradicating potential problems. There are some BI reporting tools that have implemented predictive analysis options in them, but they have also become easy to use, so there is no need to manually calculate any data or perform powerful and advanced analysis by yourself.
b) Normative analysis
Normative data technology is another most effective data analysis method in research. It crosses predictive analysis by developing responsive and practical business strategies around usage patterns or trends.
Through in-depth study of descriptive analysis, a reasonably arranged visual data set is obtained and used to solve many key business areas (including marketing, sales, customer experience, human resources, performance, finance, logistics analysis, etc.).
13. Consider autonomous technology
Autonomous technologies such as artificial intelligence (AI) and machine learning (ML) play an important role in understanding how to analyze data more effectively.
Smart alert is a technology of modern data analysis, which provides automatic signals based on specific commands or events in the data set. For example, if you are monitoring supply chain KPIs, you can set up a smart alarm to trigger when invalid or low-quality data appears. In this way, you can study the problem in depth and solve it quickly and effectively.
Neural network is a branch of machine learning, a form of data-driven analysis, which attempts to understand how the human brain processes insights and predicts value with minimal intervention. Neural networks learn from every data transaction, which means they will develop and progress over time.
14. Create a narrative
Now that we have discussed and explored the technical applications of data-driven analysis, we will study how to combine all these elements to benefit the business—starting with a small story called "data telling".
The human brain responds very well to strong stories or narratives. After using various data visualization large screens to clean up, organize and visualize the most valuable data, you should strive to tell a story-a clear beginning, middle and end.
In this way, analytical work is more accessible, more digestible and more versatile, so that more people in the organization can use the discovery to give play to their operational advantages.
15. Share the load
Last but not least, in our recommendations on how to make data analysis suitable for business use, we discussed load sharing.
By using technical methods to provide more shape and meaning to the data, expanding on our previous perspectives can provide a platform for wider access to data-driven insights.
If the right tools and data visualization platform are used, metrics can be displayed in a digestible, value-driven format, so that almost everyone in the organization can take advantage of them and make use of relevant data.
Modern data visualization platforms can integrate data from various sources. Whether you need to monitor recruitment indicators or generate reports that need to be sent across multiple departments, you can access a large number of insights in a centralized location. In addition, these cutting-edge tools can access the data visualization platform from multiple devices, which means that everyone in the enterprise can remotely connect to actual insights-and share the load.
Once everyone is able to work in a data-driven way of thinking, it will promote the success of the business in ways you have never thought of. When it comes to how to conduct data analysis, this collaborative approach is essential.
To sum up, this is the first 15 steps of data analysis techniques and methods:
- Collaborate to determine requirements
- submit questions
- Data collection
- Set KPI
- Ignore useless data
- conduct statistical analysis
- Develop a data management flowchart
- Integrated technology
- answer your question
- Visualize your data
- Implement text analysis
- In-depth diagnostic analysis
- Consider autonomous technology
- Create a narrative
- Load sharing