written by
5000fish Team

What Does a Data Analyst Do? (Roles, Skills, and Responsibility Explained)

BI Problems and Solutions 8 min read
Yurbi - Self-Service Business Intelligence

One of the people you need in your company is a responsible data analyst.

Have you ever wondered how companies target your online habits or how streaming services recommend your next binge-watch? The answer lies in data analysis.

Data analysts transform raw numbers into compelling narratives that drive informed decisions. This article will explore the fascinating world of a data analyst and their role in uncovering trends, solving problems, and shaping the future through the power of data.

What Is Data Analysis?

Data analysis involves inspecting, cleaning, transforming, and modeling data to find useful information, draw informed conclusions, and aid in decision-making. Analysts use various techniques and methods to uncover patterns, trends, correlations, and insights within datasets.

What Industries Use Data Analysis?

A wide range of industries utilize data analysis techniques and processes, but the following are some of the fields that rely on it the most:

  • Finance and Banking: Financial institutions use data analysis for risk assessment, fraud detection, investment analysis, customer segmentation, and personalized financial services.
  • Healthcare: In healthcare, data analysis is used for clinical research, patient diagnosis and treatment planning, drug discovery, disease surveillance, and healthcare management to improve patient outcomes and operational efficiency.
  • Retail and E-commerce: Retailers analyze customer data to understand buying behavior, optimize inventory management, personalize marketing campaigns, and enhance customer experience through recommendation systems.
  • Marketing and Advertising: Marketers leverage data analysis for market segmentation, customer profiling, campaign optimization, sentiment analysis, and measuring the effectiveness of advertising efforts.
  • Manufacturing and Supply Chain: Manufacturers analyze production data to improve efficiency, quality control, predictive maintenance, and supply chain management to reduce costs and enhance productivity.

What Does a Data Analyst Do?

Data analysts are responsible for uncovering the insights hidden within mountains of data. Here's a breakdown of the most common tasks they must perform:

Data Acquisition

Data analysts gather information from various sources, like surveys, website traffic, or company databases. They may even scrape data from public sources or purchase datasets from specialists.

Data Wrangling

Raw data is rarely perfect. Data analysts spend a significant amount of time cleaning and organizing the data (also known as wrangling). This might involve removing errors, formatting inconsistencies, and ensuring everything is structured for analysis.

Data Analysis

Once the data is clean, the analyst puts on their detective hat! They use statistical tools and programming languages to identify patterns, trends, and relationships within the data. During this process, they also calculate averages, create charts, or build models to predict future outcomes.

Communication as a Data Analyst

Data analysis is all about uncovering stories within the data. Analysts don't just crunch numbers; they must also clearly communicate their findings. They communicate by creating reports, dashboards, or presentations for various stakeholders, from company executives to marketing teams.


Businesses use data to answer questions and solve problems. Data analysts play a crucial role in this process. They translate business questions into analytical problems and use their findings to recommend solutions or identify areas for improvement.

What Skills Does a Data Analyst Need?

Data analysts need a diverse combination of both technical (or hard) and soft skills to succeed. Some of the most important technical and soft skills are described below.

Technical Skills for a Data Analyst

People learn hard skills through education, hands-on experience, or -- in most cases -- a combination of the two. The following are examples of hard skills a data analyst should have:

  • Programming Languages: SQL is essential for querying databases and retrieving data. R and Python are popular choices for data manipulation, analysis, and visualization.
  • Data Wrangling: Cleaning and organizing messy data is a core skill. Analysts need to be comfortable identifying and fixing errors, formatting inconsistencies, and wrangling data into a usable format.
  • Statistical Analysis: Understanding statistical concepts like hypothesis testing, averages, and correlations is crucial for drawing meaningful insights from data.

Data Visualization: Data storytelling is vital. Analysts should be proficient in various data visualization tools, which they can use to create clear and impactful charts, graphs, and dashboards.

Soft Skills A Data Analyst Should Have

In addition to hard skills, data analysts also need soft skills -- personal attributes that help individuals communicate and work well alongside others. These soft skills are fundamental:

  • Communication: Analysts must translate complex data findings into clear and concise reports and presentations for technical and non-technical audiences.
  • Problem-Solving: Business problems often translate into analytical challenges. Analysts need strong problem-solving skills to identify the right questions to ask of the data and use their findings to propose solutions.
  • Critical Thinking: Data can be messy and misleading. Analysts need to evaluate information critically, identify potential biases, and ensure their conclusions are sound.
  • Curiosity: A genuine interest in uncovering hidden insights and a thirst for knowledge are essential for success in this ever-evolving field.

What Are a Data Analyst’s Roles and Responsibilities?

A data analyst's responsibilities encompass the entire data lifecycle, from its raw state to providing actionable insights for businesses. Here's a closer look at their key roles, whether they work in healthcare, manufacturing, or any field in between:

Data Acquisition and Management

Data analysts must gather information from various sources. Information gathering involves a range of tasks, including the following:

  • Designing and conducting surveys
  • Extracting data from website traffic logs
  • Downloading data from company databases
  • Scraping publicly available data
  • Purchasing data sets from external vendors

Data Cleaning and Preparation for a Data Analyst

Data analysts also spend significant time ensuring the data is ready for analysis with the following practices:

  • Identifying and removing errors or inconsistencies
  • Formatting data for consistency
  • Filling in missing values
  • Transforming data into a usable format

Data Analysis and Exploration

Once the data is clean, the fun begins!

Analysts use their technical skills to perform statistical analysis using tools and programming languages, identify patterns, trends, and relationships within the data sets, and build models to predict future outcomes or test hypotheses.

Data Visualization and Communication

Data analysts are also storytellers who must translate complex findings into easily digestible formats. They accomplish this by doing the following:

  • Create clear and concise reports and presentations
  • Develop interactive dashboards for data exploration
  • Use data visualization tools to create charts, graphs, and other visuals to communicate insights effectively to both technical and non-technical audiences.

Problem-Solving and Recommendation

Businesses leverage data to answer questions and solve problems. Data analysts play a crucial role by translating business questions into analytical issues. They use their findings to recommend solutions or identify areas for improvement and collaborate with various teams within the organization to ensure data-driven decision-making.

What’s the Difference Between a Data Analyst and a Data Scientist?

Data analysts and data scientists are both crucial players in the world of data, but their roles have some key distinctions. Here's a breakdown of the key differences:


Data Analysts primarily focus on understanding historical data to answer specific business questions and provide insights for immediate decision-making.

Data scientists, on the other hand, are more concerned with the future and developing new methods to extract knowledge from data. They delve into complex problems, often using advanced techniques like machine learning and artificial intelligence to build predictive models.


Data Analysts rely on a solid foundation in statistics, data wrangling, data visualization, and proficiency in tools like SQL, Excel, and data visualization software.

Data scientists possess similar skills but with a deeper understanding of computer science, machine learning algorithms, and programming languages like Python and R.

Data Type

Data Analysts typically work with structured data that are already organized and stored in databases.

Data Scientists often deal with unstructured data like text, images, or social media feeds, requiring additional skills to clean and format the data for analysis.

How to Become a Data Analyst

There are multiple paths to becoming a data analyst, but they all involve developing a specific skill set. Here's a simple roadmap to get you started:


While not always mandatory, a bachelor's degree in a relevant field like statistics, mathematics, computer science, economics, or business can provide a strong foundation. Coursework in statistics, data analysis, and programming languages like SQL and Python will be beneficial.

If you don't have a relevant degree, you can also consider boot camps or online courses specializing in data analysis. These programs offer intensive training in essential skills and can be a faster alternative to a traditional degree.

Develop Core Skills

Focus on developing technical skills like SQL and other programming languages, data wrangling, statistical analysis, and using different data visualization tools. Don’t forget about soft skills, though, including communication, critical thinking, and problem-solving skills.

Build Your Portfolio

One of the best ways to develop your skills is to work on personal data analysis projects. You can find datasets online or use information from your hobbies or interests.

You can also look for freelance gigs or volunteer opportunities to gain practical experience working with real-world data.

Network and Apply

Attend industry events, online forums, or meetups to network with data analysts and learn from their experiences.

As you work on your resume, be sure to highlight your data analysis skills and relevant coursework on your resume. Focus on achievements that demonstrate your ability to solve problems with data.

When you’re ready to apply, search for entry-level data analyst positions. Keep in mind that many companies value transferable skills like problem-solving and communication alongside technical expertise.

Continuous Learning

The data analysis field is constantly evolving. Stay updated with the latest trends and technologies by taking online courses, attending workshops, and participating in online communities.

How Yurbi Elevates the Work of A Data Analyst

Data analysts are the backbone of informed decision-making. Yet, time-consuming tasks like report generation and patiently explaining data can hinder your ability to tackle the true complexities of your role. Yurbi aims to change that.

With Yurbi, you step into a more collaborative and proactive environment. The platform's intuitive tools make complex datasets accessible to everyone. This means you'll spend less time translating information and more time unearthing the actionable insights that drive strategic business growth.

  • Freeing valuable time: Yurbi's intuitive platform empowers business users to address basic data queries and visualization needs themselves. This lets you focus on more strategic modeling and analysis.
  • Data democratization: Yurbi fosters a company-wide culture of data utilization, meaning stakeholders come to you with better-informed questions and a greater understanding of how to use insights.
  • Actionable insights: Yurbi's customizable dashboards allow you to visually present complex data in ways everyone can understand and act upon.

Want to see how Yurbi can streamline your data analysis process and amplify your impact? Book a free live demo session or schedule a personalized meeting with our team!

Yurbi: Analyze more, administrate less.
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