What Does a Data Analyst Do? Job Description, Skills and Salary

The main responsibilities of a data analyst include the following.

Data Collection and Processing

Data analysts gather information from databases, spreadsheets, surveys and other sources. They organise and clean this information before analysing it.

• Collect data from relevant sources.
• Remove errors, duplicates and incomplete entries.
• Organise data into a format suitable for analysis.

Data Analysis and Interpretation

Data analysts examine datasets to identify patterns, trends and changes. They use statistical methods to understand what the data reveals and answer specific business questions.

• Identify trends and relationships in datasets.
• Use basic statistics to interpret results.
• Compare results across different periods or business activities.

Learn: What Is Data Interpretation?

Data Visualisation and Reporting

Data analysts present their findings in a format that is easy to understand. They use charts, graphs and dashboards to explain important results to managers and other teams.

• Create reports and dashboards using tools such as Excel and Power BI.
• Highlight important trends and performance changes.
• Present findings to technical and non-technical teams.

Data Quality Assurance

Data analysts check whether information is accurate, complete and consistent before using it for analysis.

• Identify missing, incorrect or duplicate records.
• Check data for inconsistencies.
• Validate results before sharing reports.

Business Intelligence and Decision-Making

Data analysts work with teams to understand business performance and identify areas for improvement. Their findings help managers assess results and plan their next steps.

• Track key performance indicators (KPIs).
• Compare actual results with business targets.
• Identify areas where processes or performance could improve.

Collaboration with Teams

Data analysts often work with departments such as marketing, finance, sales and operations. They discuss business requirements, understand what information each team needs and share relevant findings.

Making Data-Based Recommendations

After analysing the data, analysts explain what the results may mean for the business. They may suggest ways to improve customer experience, reduce costs or track performance more effectively.

Their recommendations help teams evaluate options before making decisions.

Continuous Learning and Development

Data analysts need to keep their technical knowledge up to date as tools and business requirements change. They can improve their skills by practising with new datasets, learning software and exploring relevant data science courses and certifications.

Earn certifications from PwC Academy, NSDC, and upGrad with Professional Certificate Program in Data Science and AI, designed by AI & ML leaders from Paytm, Gramener and Zalando and Build Real-World Projects on Snapdeal, Uber, Sportskeeda, and more

Data Analyst Tools: Excel, SQL, Python, Power BI

Data analysts use different tools to collect, organise, analyse and present data. The tools they need depend on their job responsibilities, the size of the datasets and the type of analysis they perform. Excel, SQL, Python and Power BI are useful tools for many data analyst roles.

Tool	Used For	Level Needed
Excel	Organising data, performing calculations, using formulas and creating charts and reports.	Beginner to intermediate
SQL	Retrieving, filtering and managing data stored in databases.	Beginner to intermediate
Python	Cleaning datasets, automating tasks, performing statistical analysis and building models.	Beginner to intermediate, with advanced skills for specialised roles
Power BI	Creating interactive dashboards, visualising data and sharing business reports.	Beginner to intermediate
Tableau	Creating interactive charts, dashboards and visual reports.	Beginner to intermediate
R	Performing statistical analysis, building statistical models and creating visualisations.	Beginner to intermediate, depending on the role
Google Analytics	Tracking website traffic, user behaviour and digital marketing performance.	Beginner to intermediate for analytics roles
Git	Tracking code changes and collaborating on data analysis projects.	Basic knowledge is useful
SAS	Performing statistical analysis, managing data and generating reports, particularly in specialised industries.	Depends on the role

Also Learn: Why Should You Learn R for Data Science?

Data Analyst vs Business Analyst vs Data Scientist

Data analysts, business analysts and data scientists help organisations make better decisions, but they focus on different problems. Data analysts study data to identify trends, business analysts look for ways to improve business processes, and data scientists use statistical methods and machine learning to build predictive models.

Basis of Comparison	Data Analyst	Business Analyst	Data Scientist
Focus	Analyses data to identify trends, measure performance and support decisions.	Studies business needs, identifies problems and recommends process improvements.	Uses data, statistics and machine learning to build predictive models and solve complex problems.
Tools	Excel, SQL, Power BI, Tableau and Python.	Excel, Jira, business intelligence tools and process-mapping software.	Python, SQL, machine learning libraries and statistical tools.
Typical Background	Statistics, mathematics, economics, computer science or data analytics.	Business administration, management, information technology or a related field.	Data science, computer science, statistics, mathematics or a related field.

If you want to explore the data science career path further, read our guide on what a data scientist does. You can also explore data science courses and certifications to learn about the skills used in this field.

Types of Data Analysts

Data analysts can work in different fields based on their interests and skills. Some focus on business performance, finance, marketing, or cybersecurity. Others use data to understand past results and predict future trends.

1. Business Analyst

Business analysts study a company's operations and performance to find ways to improve them. They use data to help teams make better decisions and connect business needs with technical solutions.

• What Business Analysts Do: Study business operations, strategies, and key performance indicators (KPIs) to support decision-making.
• Industries Hiring Business Analysts: E-commerce, consulting, and technology.
• Common Tools Used by Business Analysts: Excel, Tableau, and SQL.

2. Risk Analyst

Risk analysts study data to identify possible risks that could affect a business. They help companies understand financial, operational, and security risks and find ways to reduce potential losses.

• What Risk Analysts Do: Identify risks, review data, and help businesses follow relevant rules and regulations.
• Industries Hiring Risk Analysts: Finance, healthcare, and information technology.
• Common Tools Used by Risk Analysts: SQL, audit tools, and Power BI.

You can learn more about SQL using this SQL tutorial.

3. Financial Analyst

Financial analysts study financial records, budgets, and market trends. They help businesses make decisions about spending, investments, and future financial plans.

• What Financial Analysts Do: Analyse budgets, financial statements, and market trends.
• Industries Hiring Financial Analysts: Banking, fintech, and insurance.
• Common Tools Used by Financial Analysts: Excel, R, and SAS.

4. Data Scientist

Data scientists work with large and complex datasets to find patterns and solve problems. They also use advanced methods, such as machine learning, to build models and predict possible outcomes.

• What Data Scientists Do: Collect and analyse data, build models, and use their findings to solve business problems.
• Industries Hiring Data Scientists: Technology, fintech, healthcare, and telecommunications.
• Common Tools Used by Data Scientists: Python, R, SQL, and Power BI.

You can get started with this Python tutorial.

5. Marketing Analyst

Marketing analysts study customer behaviour, campaign performance, and market trends. They use this information to improve marketing campaigns, reach the right audience, and make better use of marketing budgets.

• What Marketing Analysts Do: Track campaign results, study customer behaviour, and measure marketing performance.
• Industries Hiring Marketing Analysts: Retail, media, and advertising agencies.
• Common Tools Used by Marketing Analysts: Google Analytics, HubSpot, and Tableau.

6. Cybersecurity Analyst

Cybersecurity analysts study network activity, system logs, and security weaknesses to identify possible threats. They help protect a company's systems and digital information from cyberattacks.

• What Cybersecurity Analysts Do: Monitor security systems, investigate suspicious activity, and help prevent cyber threats.
• Industries Hiring Cybersecurity Analysts: E-commerce, education, healthcare, and banking.
• Common Tools Used by Cybersecurity Analysts: Burp Suite, Metasploit, and Splunk.

7. Operations Analyst

Operations analysts study data related to a company's daily activities, workflows, and resources. They identify problems and suggest ways to improve efficiency, reduce delays, and manage resources better.

• What Operations Analysts Do: Analyse workflows, improve processes, and help businesses use their resources effectively.
• Industries Hiring Operations Analysts: Logistics, manufacturing, and supply chain management.
• Common Tools Used by Operations Analysts: Python, Power BI, and enterprise resource planning (ERP) tools.

8. Product Analyst

Product analysts study how customers use a product, such as a website, mobile app, or software platform. They use this information to identify problems, understand user needs, and suggest product improvements.

• What Product Analysts Do: Track user activity, study product performance, and help teams improve product features.
• Industries Hiring Product Analysts: Technology, gaming, mobile apps, and e-commerce.
• Common Tools Used by Product Analysts: Mixpanel, Amplitude, and SQL.

9. Prescriptive Analytics

Prescriptive analytics helps businesses decide what action to take in a particular situation. It uses data, predictions, and simulations to compare possible actions and suggest a suitable option.

• Use of Prescriptive Analytics: Recommends actions based on data and possible outcomes.
• Example: A marketing team can use it to choose a campaign strategy based on customer behaviour and expected results.

10. Diagnostic Analytics

Diagnostic analytics helps data analysts understand why something happened. They examine data from different sources to find patterns, connections, and possible causes behind a result.

• Use of Diagnostic Analytics: Investigates the reasons behind changes or problems in business performance.
• Example: A company can use it to understand why sales dropped in a particular region by studying customer demand, pricing, and past sales.

11. Descriptive Analytics

Descriptive analytics summarises past data to show what has happened over a period. Businesses use it to track performance and understand trends.

• Use of Descriptive Analytics: Summarises historical data using reports, charts, and dashboards.
• Example: A business can use monthly sales reports to compare revenue across different months and identify changes in performance.

Read More: Predictive Analytics vs Descriptive Analytics

12. Predictive Analytics

Predictive analytics uses historical data, patterns, and statistical models to estimate what might happen in the future. Some predictive methods also use machine learning to improve forecasts.

• Use of Predictive Analytics: Helps businesses estimate future outcomes and prepare for possible changes.
• Example: An online store can analyse past purchases and customer behaviour to estimate future product demand.

Also read: 50 Data Analyst Interview Questions and Answers

Data Analyst Salary in India

Data analysts work across industries such as technology, banking, consulting, retail, and e-commerce. Their salaries vary based on experience, technical skills, job responsibilities, and the company they work for.

The table below shows the average annual salaries listed for data analysts at some of the top companies in India.

Company	Average Salary per Year
Apple	₹11.4 Lakhs
Microsoft	₹19.1 Lakhs
Meta (Facebook)	₹16.2 Lakhs
Google	₹15 Lakhs
Accenture	₹7.7 Lakhs
TCS	₹5.7 Lakhs
Cognizant	₹6.7 Lakhs
Wipro	₹5.2 Lakhs
Capgemini	₹5.5 Lakhs
S&P Global	₹6.1 Lakhs
Infosys	₹4.9 Lakhs
Tech Mahindra	₹5.4 Lakhs
Deloitte	₹8.1 Lakhs
HCLTech	₹4.9 Lakhs
Amazon	₹7.5 Lakhs
Flipkart	₹6.5 Lakhs
Genpact	₹6.1 Lakhs
Ernst & Young	₹7.7 Lakhs
IBM	₹8.1 Lakhs
LTIMindtree	₹6 Lakhs

Source: AmbitionBox. 

Also read: Data Analyst Salary in India: For Freshers and Experienced Professionals

How to Get Your First Data Analyst Job

You can start a career in data analytics by learning essential tools, building practical skills, and applying for entry-level roles. Follow these steps to prepare for your first data analyst job.

1. Learn Essential Skills

Start with Excel, SQL, and basic statistics. Then learn a data visualisation tool such as Power BI or Tableau. You can learn Python later to analyse larger datasets.

2. Practise SQL

Practise writing queries to filter, sort, group, and combine data. Solve practical problems and prepare using these common SQL interview questions.

3. Build a Portfolio

Create two or three projects using public datasets. For example, analyse sales trends, study customer behaviour, or build a Power BI dashboard. Explain your findings and share your projects on GitHub.

4. Consider a Certificate

A data analytics certificate can help you learn key concepts and tools. Choose a course with practical assignments and projects. You can explore relevant data science courses and certifications.

5. Apply for Entry-Level Roles

Look for positions such as Junior Data Analyst, Data Analyst Intern, Reporting Analyst, and MIS Executive. Highlight your technical skills, projects, and certifications in your resume.

6. Prepare for Interviews

Practise SQL, Excel, and basic statistics questions. Be ready to explain your projects, the insights you found, and how businesses can use those insights.

The Future of Data Analysts

The demand for data analysts is growing as businesses use data to make better decisions. AI and machine learning are also changing how analysts collect, study, and interpret data.

Here are some key trends shaping the future of data analytics:

• Growing demand: Industries such as healthcare, retail, finance, and real estate need data analysts to understand trends and improve business performance.
• AI and automation: AI tools can automate repetitive tasks, allowing analysts to focus more on interpreting results and solving business problems. Learn more with this artificial intelligence tutorial.
• Career growth: With experience, data analysts can move into roles such as senior data analyst, analytics manager, or chief data officer (CDO).
• New skills: Learning SQL, Python, data visualisation, and AI-based analytics tools can help professionals stay relevant as technology changes.

To grow in this field, keep practising with real datasets, learn new tools, and improve your ability to explain data insights clearly.

You can also book your free personalized career counseling session today and take the first step toward transforming your future.  

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