Welcome to our comprehensive guide on becoming a data analyst! In this article, we’ll delve into the world of data analysis, exploring what it means to be a data analyst, the core responsibilities of data analyst jobs, the qualifications and skills required for success, and practical steps on how to embark on a career in this exciting field.

Whether you’re a novice or someone looking to transition into data analytics, we’ve got you covered with all the essential information you need to know. So, let’s dive in and uncover the secrets of the data analyst profession!

1. What is a Data Analyst?

Before we begin, let’s clarify: What exactly is a data analyst?

Well, a data analyst is someone who works with data, analyzing it to extract valuable insights. Understanding the essence of data analysis is crucial for anyone aspiring to become proficient in this field.

Recently, a friend working in data analysis shared a frustrating experience. Despite compiling a detailed report filled with charts and data, he was criticized by his supervisor.

The problem? He hadn’t focused on the right content for analysis.

This issue is common among data analysts. Many of them come from technical backgrounds but lack practical experience in business operations. Consequently, their analyses may be skewed, as they prioritize data analysis methods over a deep understanding of the business context.

So, what can aspiring data analysts do differently? Let’s explore effective data analysis techniques and strategies for beginners in this field.

2. What does a Data Analyst do?

A data analyst serves three primary purposes in everyday business operations:

2.1 Current Status Analysis

This involves examining past events to understand the present situation. It helps evaluate the overall operational status of the enterprise by assessing the performance of various operational indicators. Additionally, it sheds light on the composition of different business areas, providing insights into their development and changes over time. Typically, current status analysis is conducted through regular reporting methods like the daily report, the weekly report, and the monthly report.

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Business dashboard (by FineReport)
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2.2 Cause Analysis

Put simply, cause analysis uncovers the reasons behind a given situation.

Following the initial assessment of the current status, we gain a broad understanding of the enterprise’s profits. However, we still lack insight into why certain aspects are performing well or poorly, and what factors are driving these outcomes. This is where cause analysis becomes crucial—it helps us delve deeper to identify the specific reasons behind changes in business operations.

For instance, if there’s a 26% decrease in gross profit in May, we need to understand why. Was it a general decline across all business sectors, or were specific areas affected? Did the decline occur uniformly across all regions, or was it localized? By conducting a cause analysis, we can pinpoint the precise reasons for the revenue decline, enabling us to fine-tune our operational strategies.

Gross Profit Analysis for the data analyst
Profit analysis dashboard (by FineReport)

Cause analysis typically involves focused projects tailored to investigate specific situations based on the enterprise’s operational context.

2.3 Predictive Analysis

Put simply, predictive analysis predicts future occurrences. Once we grasp the current operational landscape of a business, it’s crucial to sometimes anticipate future trends.

This helps in devising effective strategies and goals for the company’s sustained growth. Predictive analysis is often conducted through specific projects, typically occurring during the development of the quarterly report or annual business plans. However, it’s not as frequent as current status or cause analyses.

2.4 How Does a Data Analyst Conduct Data Analysis?

Data analysis, a core responsibility of a data analyst, utilizes a range of statistical methods to examine extensive datasets, to summarize, comprehend, and maximize the utility of data. Within the statistical realm, data analysis is typically classified into descriptive, exploratory, and confirmatory approaches, each tailored to distinct objectives of revealing insights and validating hypotheses. Now, let’s explore the methodologies and tools employed by data analysts in their analytical pursuits.

Data analysts employ a variety of statistical techniques to process large datasets, aiming to extract insights and leverage data effectively. This involves summarizing, understanding, and digesting information to harness its full potential.

Within the field of statistics, data analysis is often segmented into descriptive, exploratory, and confirmatory categories. Exploratory analysis focuses on uncovering new patterns within data, while confirmatory analysis verifies existing hypotheses.

Descriptive analysis falls under the basic level of data analysis, encompassing methods such as comparative and mean analysis. Conversely, exploratory and confirmatory analyses are considered advanced techniques, including correlation, factor, and regression analysis.

As data analysis is predominantly software-driven, data analysts must not only master diverse analytical methods but also familiarize themselves with mainstream data analysis tools and reporting tools software. While basic analysis can be executed using Excel, advanced analysis demands proficiency in specialized software like FineReport, Tableau, and FineBI.

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3. How to Become a Data Analyst?

The data analysis process primarily consists of six interrelated yet relatively independent stages. These stages include: defining analysis objectives and strategies, data collection, data processing, data analysis, data presentation, and report writing.

3.1 Defining Analysis Objectives and Strategies

Every endeavor, including data analysis, should have a goal in mind. Before embarking on data analysis, it’s essential to consider the purpose behind it. What problems are you trying to solve through this analysis? Once the analysis objectives are clear, it’s necessary to outline the analysis approach and build a framework. This involves breaking down the analysis objectives into various points, and determining how to analyze from different perspectives.

3.2 Data Collection

Data collection involves gathering relevant data according to the established data analysis framework, providing material and a basis for data analysis. Data includes both primary and secondary sources. Primary data refers to directly accessible information, while secondary data refers to processed and refined data. Common data sources include databases, public publications such as statistical yearbooks or reports, the internet, and market research.

3.3 Data Processing

Data processing involves organizing and formatting collected data to prepare it for analysis, a crucial stage preceding data analysis. The primary purpose of data processing is to extract meaningful information from large, unstructured, and often complex datasets.

3.4 Data Analysis

Data analysis involves using appropriate analytical methods and tools to analyze processed data, extracting valuable insights, and forming effective conclusions. During the stage of defining data analysis strategies, data analysts should select suitable analytical methods for the content to be analyzed. When conducting data analysis, they should be able to manage data smoothly and conduct analysis and research confidently.

To excel in data analysis, it’s essential to choose the right BI software. One recommended professional self-service analysis BI software is FineBI. It’s professional, concise, and user-friendly, with a clear interface and workflow. Through FineBI’s self-service data set function, ordinary business personnel can filter, slice, sort, and aggregate data flexibly to achieve desired data results. Additionally, they can select intelligent push charts and dashboards for data visualization.

The great data analysis tool for the data analyst-FineBi
Drag and drop to create charts (by FineBI)
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FineBI stands out for its user-friendly interface and ease of operation, which extends to its data processing capabilities. Equipped with a range of built-in calculation formulas and filtering components, FineBI eliminates the need for extensive SQL coding. Additionally, it facilitates the effortless construction of various classic data analysis models, including the pyramid model, KANO model analysis, RFM model, and market basket analysis model, empowering businesses with valuable insights.

AARRR Analysis for the data analyst
AARRR analysis model (by FineBI)
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FineBI offers tailored analysis scenarios for different industries such as manufacturing, healthcare, retail, and finance. Analyzing and presenting key business metrics empowers management teams to stay informed about business trends effortlessly. Moreover, FineBI frees data analysts from the complexities of data processing and visualization, enabling them to dedicate more energy to data analysis, management, algorithm research, and effective communication with stakeholders.

3.5 Data Visualization

Typically, data is represented using tables and graphs, as they offer a more intuitive way of conveying information. Popular visualization methods include pie charts, bar graphs, line graphs, scatter plots, and radar charts. These can be customized further to suit specific needs, such as pyramid charts, matrix charts, and Gantt charts.

In most cases, people prefer visual representations as they effectively communicate the analyst’s insights. Remember, whenever possible, opt for visuals over tables to explain concepts, and when appropriate, prefer tables over text. It’s worth noting that with the FineReport, users can easily create a comprehensive ‘command center,’ displaying crucial data management information on various screens like LED displays in areas such as order management, complaint resolution, production monitoring, and supply chain management, with a user-friendly drag-and-drop interface.

Bank Service Center for the data analyst
Supervision system created by FineReport
Production Dashboard for the data analyst
Production dashboard created by FineReport

So many Introductions have been made to the superior features of the BI system FineReport and FineBI. What are the specific differences between these two? Now let’s explore and find out.

FineBI serves as a self-service business intelligence (BI) tool primarily aimed at business professionals and data analysts, catering to their OLAP analysis needs within business operations. Its user-friendly interface prioritizes empowering business users to conduct their analyses. Both platforms generate reports and analytical outcomes intended for leadership and management, who rely on these insights for decision-making.

In contrast, FineReport functions as a reporting tool more suited for IT specialists or dedicated report developers within specific organizations, capable of handling intricate reporting requirements. Business units can access and update reports or data entries as needed. The speed of report generation varies based on the reporting tool utilized; for instance, FineReport’s Excel-like interface design significantly enhances production efficiency, saving IT personnel considerable time.

It’s crucial to highlight that both of these BI tools offer distinct advantages and cater to different scenarios, rather than being mutually exclusive options. They are expected to coexist in the long run, providing businesses with choices based on their specific needs until there’s a fundamental shift in the information infrastructure. Following Gartner’s bimodal IT model, it’s suggested that companies assess their data application maturity to determine which type of BI suits them best, or if a combination of both is necessary.

Many companies opt to procure both these products concurrently, leveraging the seamless integration of FineReport and FineBI to address the diverse needs of enterprise IT, from complex reporting to real-time analysis effortlessly!

3.6 Writing Reports

A data analysis report serves as a comprehensive summary of the entire data analysis process. It encapsulates the reasons behind the analysis, the steps taken, the findings, and suggestions for decision-makers. A well-crafted report must meet three key criteria:

(1) Structured Analysis Framework: Firstly, it needs a clear and organized framework with illustrative visuals to aid comprehension. A logically structured report with clear priorities helps readers grasp the content accurately. Visual aids make data more engaging, enhancing its impact and facilitating better understanding for the readers.

(2) Definitive Conclusions: Without clear conclusions, an analysis lacks substance and fails to fulfill the report’s purpose. Since the primary aim of analysis is to uncover or validate conclusions, it’s essential not to lose sight of this objective.

(3) Actionable Recommendations: Decision-makers require not just problem identification but also practical solutions. Therefore, data analysts must not only possess analytical skills but also understand the business context. This enables them to propose viable recommendations or solutions based on the identified business challenges.

4. How Data Analysts Conduct Data Analysis?

What are the typical techniques data analysts employ during their analysis? Let’s explore a handful of practical methods frequently utilized in data analysis:

4.1 Comparison Analysis Method


The comparison analysis method involves comparing two or more datasets to analyze their differences, thereby revealing the developmental changes and patterns represented by these data. This method is characterized by its ability to intuitively illustrate changes or discrepancies in certain aspects of phenomena. It also enables accurate and quantified representations of these changes or discrepancies. So, what does a data analyst do when employing the comparison analysis method, and what is a data analyst’s role in this context?


The comparison analysis method can be categorized into static comparison and dynamic comparison.

Static comparison entails comparing different aggregate indicators under the same time conditions, such as comparing different departments, regions, or countries, also known as horizontal comparison, or simply “horizontal comparison.”

Dynamic comparison involves comparing the values of different indicators over different periods under the same aggregate conditions, also known as vertical comparison, or simply “vertical comparison.”

Both methods can be used independently or in combination. When conducting comparison analysis, total indicators, relative indicators, or average indicators can be used alone or in combination. The results of the comparison can be expressed in relative numbers, such as percentages or multiples.

Practical Applications

a) Contrasting with objectives: Comparing actual completion values with objectives, falls under horizontal comparison.

b) Contrasting different periods: Selecting the values of indicators from different periods as comparison standards fall under vertical comparison.

c) Comparing with peer departments, units, or regions: Contrasting with peer departments, units, or regions falls under horizontal comparison.

d) Intra-industry comparison: Contrasting with benchmark enterprises in the industry, competitive comparisons, or industry averages fall under horizontal comparison.

e) Comparison of activity effects: Comparing before and after the launch of a marketing campaign falls under vertical comparison. Additionally, we can group and compare the progress of activities, which falls under horizontal comparison.


The scope, calculation method, and measurement units of indicators must be consistent, using the same units or standards for measurement. The objects of comparison must be comparable, and the types of indicators being compared must be consistent. Whether absolute indicators, relative indicators, average indicators, or other types of indicators, both sides must be unified when conducting comparisons.

In the illustration, to determine how to arrange sales plans based on data, sales in different regions were compared.

Sales Analysis Dashboard for the data analyst
Sales Analysis (by FineReport)
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4.2 Group Analysis Method


In data analysis, not only do we analyze the quantitative characteristics and relationships of the overall data, but we also explore internal group analysis of the entire dataset. This method involves dividing data analysis objects into different parts and types based on their characteristics and certain indicators to reveal their inherent connections and patterns.


The purpose of grouping is to facilitate comparison by distinguishing objects with different properties within the overall dataset. It involves merging objects with similar properties while maintaining consistency within each group and differences between groups’ attributes. This aids in further applying various data analysis methods to deconstruct the internal quantitative relationships. Therefore, the grouping method must be combined with the comparative method.


a) Determine the number of groups

This can be decided by the data analyst based on the characteristics of the data (e.g., data size). Since one of the purposes of grouping is to observe the characteristics of data distribution, the number of groups should be moderate. If there are too few groups, the data distribution will be too concentrated, and if there are too many, the distribution will be too scattered, both of which hinder observation of the characteristics and patterns of data distribution.

b) Determine the group interval for each group

The group interval is the difference between the maximum and minimum values of a group, determined based on the maximum and minimum values of all data and the number of groups, where the group interval = (maximum value – minimum value) / number of groups.

c) Organize the data into groups based on the group intervals, categorizing them accordingly.

In the illustration, women’s clothing is categorized into different market segments, categories, and regions for group comparison.

Sales Dashboard for the data analyst
Comparison analysis by FineReport

4.3 Structure Analysis Method

The structure analysis method refers to an analytical approach where the various parts within the analyzed dataset are contrasted with the entirety of the dataset. This involves examining the proportions of each part within the whole, which are relative indicators. Generally, the larger the proportion of a certain part, the higher its importance, and the greater its impact on the overall dataset.

Structural relative indicators (ratios) = (value of a certain part of the dataset / total quantity of the dataset) *100%

Market share = (sales volume of a specific product / total sales volume of that product) *100%

Market share is a crucial indicator for analyzing a company’s competitive position within an industry and serves as a comprehensive economic measure of its operational status. A high market share indicates favorable operational conditions, strong competitive capabilities, and advantageous market positioning for the company. Conversely, a low market share suggests poor operational status, weak competitive capabilities, and disadvantaged market positioning.

Pareto Analysis for the data analyst
Pareto Analysis by FineReport
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4.4 Average Analysis Method

The average analysis method involves using the calculation of averages to reflect the general level of a certain quantitative characteristic of the entirety within a specified time or location condition. This method serves two primary purposes:

(1) Utilizing average indicators to compare the degree of differences among similar phenomena across different regions, industries, or types of units, which is more persuasive than comparing using total indicators.

(2) Using average indicators to compare the changes of certain phenomena across different historical periods, which better illustrates their developmental trends and patterns.

Arithmetic mean = Sum of values of all units in the entirety / Total number of units in the entirety

The arithmetic mean is a fundamental indicator. It is a comprehensive measure abstracting the quantitative differences among units within the entirety. However, it only represents the general level of the entirety, obscuring the differences among individual units behind the average.

Sales Analysis for the data analyst
Sales Analysis by FineReport

4.5 Cross-Analysis Method

The cross-analysis method is typically used to analyze the relationship between two variables (fields). It involves arranging two variables with a certain correlation and their values simultaneously in a table, making each variable’s value a crossing point of different variables, thus forming a cross-table. This enables the analysis of the relationship between variables within the cross-table. Of course, cross-tables can have more than two dimensions, and the more dimensions there are, the more complex the cross-table becomes. Therefore, the choice of several dimensions should be determined based on the purpose of the analysis.

The cross-analysis method, commonly employed by a data analyst, facilitates the examination of the interrelation between two variables. This approach entails organizing related variables and their values into a table format, allowing for a comprehensive analysis of their correlations. Cross-tables can range from two dimensions to higher dimensions, with complexity increasing accordingly. Therefore, the selection of dimensions should align with the objectives of the analysis, ensuring relevance and clarity.

YoY, MoM financial report created by FineReport
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4.6 Comprehensive Evaluation Analysis Method

The comprehensive evaluation analysis method involves transforming multiple indicators into one indicator that reflects the overall situation for analysis and evaluation. It consists of five main steps:

(1) Determining the comprehensive evaluation indicator system, which includes the indicators serving as the basis and reference for the evaluation.

(2) Collecting data and standardizing the data of different measurement units.

(3) Determining the weights of each indicator in the indicator system to ensure the scientific validity of the evaluation.

(4) Aggregating the processed indicators to calculate the comprehensive evaluation index or score.

(5) Ranking the evaluated units based on evaluation indicators or scores and concluding accordingly.

4.7 DuPont Analysis Method

The DuPont analysis method, also known as the DuPont financial analysis system, is a comprehensive analytical approach created and initially adopted by the DuPont Company in the United States. It involves analyzing and evaluating a company’s financial condition and economic performance by utilizing the intrinsic connections among various key financial indicators.

The distinguishing feature of the DuPont analysis system is its integration of several ratios used to assess the operational efficiency and financial condition of a company, forming a comprehensive indicator system. Ultimately, it synthesizes these indicators through return on equity. The pyramid structure utilized in DuPont analysis enhances the clarity and coherence of financial ratio analysis, providing a concise and clear representation of the relationships between various financial indicators.

DuPont Analysis for the data analyst
DuPont financial analysis by FineBI
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4.8 Funnel Analysis Method

The funnel chart analysis method is a management analysis tool suitable for comparing and standardizing business processes with long cycles and involving complex business processes. For example, funnel charts are used for analyzing conversion rates of certain key pathways on websites. They not only display the final conversion analysis rate from entering the website to making a purchase but also show the conversion rate at each step of the entire key pathway.

When dealing with massive amounts of data, what tools can be used for funnel analysis? When faced with large amounts of data, tools like Excel may struggle to efficiently perform funnel analysis. In such cases, it is common practice to use specialized big data analysis tools such as FineReport to build a dashboard. This allows for easy funnel analysis:

Funnel charts are the most commonly used type of flow analysis chart. Through funnel charts, one can intuitively compare the conversion rates of each stage, making it easy to identify specific stages corresponding to problems. A typical application of funnel charts is in sales funnels.

Two forms (continuous, and discontinuous) of funnel charts:

The benefit of using professional data analysis tools lies in their efficiency. These tools come with built-in funnel models, allowing you to complete data funnel analysis through simple drag-and-drop operations. This functionality is difficult to achieve with software like Excel. Therefore, it is recommended to use professional software for such tasks.

From a technical perspective, FineReport can not only meet some business needs but also serve as an entire business system. Colleagues have used it to develop an attendance system, while there are also successful cases of using it to develop quality management systems, examination systems, and small-scale financial systems within the industry.

FineReport provides localized services in regions such as Taiwan, Hong Kong, Macau, Singapore, and Malaysia. Local teams from the Fanruan provide technical support and project implementation. Click the banner below to download FineReport reporting software for free and experience it. You can contact technical support engineers at any time for any technical issues.

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4.9 Matrix Correlation Analysis Method

The matrix analysis method refers to a type of analysis based on two important attributes (indicators) of things (products, services, etc.), conducting classification correlation analysis to identify a solution to problems. It is also known as the matrix correlation analysis method, abbreviated as the matrix analysis method.

The matrix correlation analysis method involves categorizing and analyzing items (such as products or services) based on two key attributes or indicators. This analytical approach aims to uncover relationships and associations between these attributes, thereby offering insights into problem-solving strategies. It is commonly referred to as the matrix analysis method.

Kano model for the data analyst
Kano model by FineReport

Matrix correlation analysis method provides decision-makers with an important reference basis for problem-solving and resource allocation. Addressing primary contradictions first, then secondary ones, enhances work efficiency, allowing resources to be allocated to departments or tasks where they can yield the most significant outcomes. This approach aids decision-makers in optimizing resource allocation.

5. How to be a Good Data Analyst

5.1 Essential Requirements for Embarking on a Career as a Data Analyst

Understanding of Business: A prerequisite for data analysis work is a thorough understanding of the business domain, including familiarity with industry knowledge, company operations, and processes. Having unique insights is beneficial, as analysis results without industry and company context lack practical value.

Management Skills: Proficiency in management is essential for establishing data analysis frameworks and offering insightful analysis recommendations. Without a grounding in management theory, ensuring the effectiveness of analysis recommendations becomes challenging.

Analytical Proficiency: Mastery of basic principles and effective methods of data analysis, along with the ability to apply them flexibly in practical work, constitutes analytical proficiency. Basic analysis methods include comparative analysis, grouping analysis method, structural analysis method, average analysis method, cross-analysis method, comprehensive evaluation analysis method, DuPont analysis method, funnel analysis method, matrix correlation analysis method, and advanced analysis methods.

Tool Proficiency: Mastery of commonly used data analysis tools is crucial. Data analysis tools translate theoretical data analysis methods into practice. Given the increasingly vast amount of data, relying on calculators for analysis is impractical; thus, leveraging powerful data analysis tools becomes imperative.

Design Skills: Effective expression of a data analyst’s analytical viewpoints through graphical representation ensures clarity of analysis results. Designing graphics requires mastery of principles such as selecting appropriate graphics, layout design, and color coordination.

5.2 Essential Qualities of Data Analysts

Rigorous and Responsible Attitude: Data analysts are akin to doctors for businesses, identifying critical issues and potential problems through the analysis of operational data. A qualified data analyst must maintain a rigorous and responsible attitude, maintain a neutral stance, and objectively evaluate problems in the business development process, providing decision-makers with an effective reference basis.

Strong Curiosity: Successful data analysts are driven by numerous “whys”: Why are the results as they are? Why aren’t they different? What are the reasons behind these results? Only by asking such questions during data analysis and finding satisfactory answers through analysis can data analysts continuously improve their skills.

Clear Logical Thinking: Analyzing complex business problems during data analysis requires a clear understanding of facts and the ability to logically structure the overall and local aspects of the problem. After deep thinking, clarifying the logical relationships within the structure is essential for objectively and scientifically finding answers to business problems.

Proficiency in Imitation Learning: While having original ideas is important in data analysis, learning from past experiences is also crucial for rapid growth. Successful imitation involves grasping the essence of others’ methods, understanding their analytical principles, and achieving substantial progress through practical application.

Courage for Innovation: Innovation is a quality that outstanding data analysts possess. Only through continuous innovation can analysts elevate their analytical skills, enabling them to analyze problems from a higher perspective and bring more value to the research field and society.

6. A Conclusion of Data Analysts

In summary, data analysis plays three primary roles in daily business operations: status analysis, root cause analysis, and predictive analysis. It involves using appropriate statistical analysis methods to analyze large amounts of collected data, summarize and understand them, and maximize the potential of data to serve business needs.

The data analysis process mainly includes six steps: defining analysis objectives and strategies, data collection, data processing, data analysis, data presentation, and report writing. Data analysis methods encompass various techniques, from basic methods like comparative analysis to advanced methods like factor analysis. To excel as a data analyst, one needs a combination of business understanding, management skills, analytical proficiency, tool proficiency, and design skills, along with qualities such as rigor, curiosity, clear thinking, and a balance between imitation learning and innovation.

Among these, finding the appropriate data analysis tools not only enhances the efficiency of data analysts but also aids in their rapid development with the assistance of professional tools. As the saying goes, ‘To do a good job, one must first sharpen one’s tools.’ Click on the banner below to try FineReport and FineBI tools for free, facilitating data analysts in conducting data analysis more professionally and efficiently. Click to see how FineReport and FineBI help your organization unleash the full potential of data!

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