Seeking a career in data analytics? You have made a superb choice. However, to build a successful data analytics career, you not only need to have an in-depth understanding but also learn the tools used in data analytics. Whether you are considering a career or curious about data analytics, we have curated a list of top tools used in data analytics that will help you grow and advance. Let’s get started!

Data analytics has increasingly become an integral part of every industry. 

From predicting future trends, understanding customer behaviour, to making accurate decisions, data analytics helps businesses thrive. This is the key reason why India’s data analytics market is projected to reach a value of $21.3 billion by 2030, at a CAGR (Compound Annual Growth Rate) of 35.8% from 2025 to 2030. 

As of August 2025, there were 7,115 job openings for data analysts in India, demonstrating a strong demand for the expertise. 

With the surge in demand for data professionals, many people are enrolling in data analytics courses to learn the skills needed to succeed in the field.

Whether you’re getting an advanced data analytics certification offline, planning to take an online course, or seeking to upskill to stay competitive, your first move should be to learn and master the right data analytics tools.

Which is why we are here with the curated list of the top 8 tools used in data analytics. Explore each tool, know its pros and cons, and its real-world use to make the right career decision.

Let’s dive in!

What are Data Analytics Tools?

Data analytics tools are software applications and platforms that help users and businesses in collecting, processing, analysing, and visualising a set of data to extract meaningful insights. Different data analytics tools allow users to solve problems, identify trends, make future predictions, and promote data-driven decisions. 

Data analytics tools are essential for the data analysis lifecycle, including

• Data Collection: Collecting data from various sources like datasets, websites, spreadsheets, etc.
• Data Cleaning and Preparation: Organising, rectifying errors, and formatting data to ensure accuracy and quality.
• Data Analysis: Applying statistical methods, models, and algorithms to identify patterns and relationships.
• Data Visualisation: Presenting patterns in an understandable and appealing format, like graphs, dashboards, and charts.

Who Uses Data Analysis Tools and Why?

Data analysis tools are not only used by specialists but also by data analysts, data scientists, and more. Here is the list.

Data Analysts and Data Scientists

Data analysts and data scientists are among the primary users of data analysis tools. They use data analytics tools to collect, clean, and interpret data to find patterns, trends, and solve complex problems. They use data analytics tools to build predictive models, perform statistical analysis, and create visualisations.

Business Analysts

Business analysts also leverage data analytics tools to make informed business decisions and improve overall processes. They use extracted insights to answer stakeholder questions, create dashboards and reports to identify growth opportunities, increase profitability, and optimise operations.

Marketing and Sales Professionals

Marketing and sales professionals also use data analysis tools to understand customer behaviour and optimise advertising campaigns. They analyse key metrics like website traffic, conversion rates, etc., to identify what is working, what’s not, and what needs to be improved.

Financial Analysts

Financial analysts leverage different data analysis tools to predict market trends, analyse risks, and manage budgets. They assess large datasets of financial statements, stock market data, etc., to forecast future performance. It also helps them in detecting fraud, building financial models, allocating resources, and making investment recommendations.

Project Managers

Project managers leverage data analysis tools to monitor progress, manage resources, and minimise risks. They use data analysis tools to track key performance indicators like project timelines, budget, and task completion rate. Leveraging data analysis tools, project managers identify potential risks, adjust schedules, and ensure the project stays on track for more successful outcomes. 

HR (Human Resources) Specialists

HR specialists use data analysis tools to streamline the recruitment process, improve employee retention, and boost productivity. By analysing data like employee turnover rates, performance metrics, and job satisfaction scores, HRs create better retention strategies, design training programs, and make more effective hiring decisions.

Top 8 Tools Used in Data Analytics

Here is the list of the top 8 tools used in data analytics, visualisation, and business intelligence.

1. Tableau

Tableau is one of the best tools used in data analytics and data visualisation. This business intelligence tool can transform complex data into easy-to-understand, interactive visualisations and dashboards.

Best For:

• Creating compelling and interactive charts, maps, graphs, etc.
• Transforming raw datasets into valuable insights for data-driven decision making.
• Data exploration and presenting findings.
• Consolidating and reporting on data from numerous sources.
• Creating a clear narrative with data.

Real-World Use:

• Retail: Sales performance analysis and inventory optimisation.
• Healthcare: Patient data tracking and operational efficiency.
• Finance: Visualising financial performance and market trends.
• Marketing: Tracking campaign performance and customer behaviour.

Pros	Cons
Easy to learn and use for beginners.	Limited data cleaning capabilities in the free version.
Highly interactive dashboards.	Not the primary tool for complex data engineering tasks.
Process and visualise large datasets efficiently.
Abundant free tutorials, forums, and shared projects
Highly sought-after skill for many in-demand data roles.

2. Power BI

Developed by Microsoft, Power BI is another powerful tool for data analytics, visualisation, and business intelligence. This tool helps users connect to numerous data sources and create appealing dashboards and reports. Power BI seamlessly integrates with popular Microsoft products like Excel and Office 365, which makes it a popular choice for several businesses. 

Best For:

• Turning raw data into valuable insights for strategic decision-making.
• Creating and sharing dynamic, compelling dashboards and reports.
• Managing simple spreadsheet data as well as massive enterprise data warehouses.
• Work seamlessly with tools like Excel, Azure, and SQL Server.

Real-World Use:

• Finance: Tracking expenses, visualising budgets and analysing performance trends.

• Sales: Analysing sales funnel, tracking KPIs, and monitoring sales team performance.

• HR: Reporting workforce demographics, turnover rates, and recruitment efficiency.

• Marketing: Monitoring website traffic, social media engagement, and campaign performance. 

Pros	Cons
An accessible tool for beginners to practice and build a portfolio.	Mastering DAX (Data Analysis Expressions) for advanced calculations can be challenging for beginners.
Smooth learning curves as the interface and functions are familiar to Excel.	Might slow down while working with large datasets.
Powerful capabilities for data modelling.

3. SQL

SQL (Structured Query Language) is another standard language for handling and manipulating relational databases. While SQL is not a general-purpose programming language, it is essential for data professionals to interact with, retrieve, clean, and aggregate databases.

Best For:

• Data cleaning and foundational transformation before its analysis. 
• Querying, retrieving, and updating data from relational databases.
• Handling and maintaining the structures of databases.
• Joining and aggregating data from several tables. 

Real-World Use: 

• Technology: Manage user data, track application performance, and retain customer information.
• E-commerce: Track sales, manage inventory, and analyse customer order history.
• Finance: Manage customer accounts, transaction records, and market data.
• Healthcare: Manage patient records and appointment scheduling systems.

Pros	Cons
Easy to learn the basics; beginners can start querying and manipulating data quickly.	Can be challenging to scale.
Highly valuable and marketable skill across a range of industries.	Not ideal for unstructured data.
Efficiently performs highly complex operations.	Complex queries can be difficult to optimise.
Can be easily integrated with a variety of programming languages and business intelligence tools.

4. Microsoft Excel

Microsoft Excel is a widely used and foundational spreadsheet tool for data analytics, management, and visualisation. Experience with this tool is a prerequisite for almost every professional role today. Excel is an excellent tool for learners to build fundamental data skills. Data analytics tools in Excel give a path to more advanced tools.

Best For:

• Handling and organising large tabular datasets with sorting and filtering.
• Performing complex calculations using a vast library of formulas and functions.
• Creating a wide variety of charts and graphs to visualise data and highlight trends.
• Financial modelling, budget planning, and forecasting.
• Automating various repetitive tasks with macros and VBA (Visual Basic for Applications).
• Connecting to external data sources and integrating with other tools.

Real-World Use:

• Finance: Tracking budgets, generating financial models, and creating profit and loss statements.
• Project Management: Managing project timelines, task lists, and resource allocation.
• Marketing: Tracking key campaign metrics, audience demographics, and social media analytics for small-scale projects.
• Human Resources: Tracking leaves, organising employee data, and managing performance reviews.

Pros	Cons
A universal skill required for almost every job role.	Manual data entry can lead to errors.
Easy to learn for beginners.	Lacks the advanced, interactive visualisation capabilities of modern tools like Power BI or Tableau.
It can be used for several tasks beyond data analysis.

5. Python

Python is a popular programming language, a widely used tool for data analytics, automation, and machine learning. Its simple, readable syntax makes Python an excellent choice for learners. Python is a powerful tool for complex tasks and a top IT skill in demand. Data analytics tools in Python which also provide a data science roadmap. 

Best For:

• Cleaning, transforming, and preparing messy datasets for analysis.
• Performing complex statistical analysis, machine learning, and data modelling.
• Creating highly customised and advanced charts and plots.
• Automating repetitive tasks, from web scraping to report generation.

Real-World Use:

• Finance: Developing financial models, analysing stock market trends, managing and predicting risks.
• Healthcare: Analysing massive patient data to identify genetic patterns, predicting disease outbreaks, rehospitalisation, and processing medical images.
• Marketing: Customer segmentation based on behaviour, preferences, and demographics, automating social media analytics, and personalised campaigns.
• Technology: Improving websites and apps, analysing user behaviour, developing machine learning algorithms, etc.
• Retail: Optimising logistics and inventory levels. Also, analysing historical sales data to improve forecasting future trends and demands.

Pros	Cons
Simple syntax and clear structure make it easier to learn for new programmers.	Slower than Java or C++ for specific high-performing tasks.
One of the most in-demand programming languages for data science and data analytics.	Mastering the language for complex projects can take time.
Massive library provides ready-to-use features for data analysis.
Versatile; can be used for data analytics, web development, scientific computing, and software engineering.

6. SAS

SAS (Statistical Analysis System) is another powerful tool for data analytics, business intelligence, and data management. This tool offers robust data security and statistical analysis capabilities, making it reliable for enterprise-level, complex tasks.

Best For:

• Handling and manipulating massive and complex data efficiently.
• Performing high-level statistical analysis, data modelling, and clinical trials.
• Creating detailed reports and dashboards.
• Ensuring data compliance and security.

Real-World Use:

• Finance: Fraud detection, credit risk modelling, and reporting. 

• Healthcare: Monitoring drug safety, data analysis of clinical trials, and submission to regulatory bodies.

• Government: Demographic analysis, public health research, and economic forecasting.

• Retail: Cross-sell analysis, customer loyalty programs, and optimising supply chain.

• Insurance: Claim processing, forecasting policy cancellations, and risk assessment. 

Pros	Cons
Clear, procedural syntax can be easier for beginners to grasp.	Steep learning curve for non-statisticians.
Highly secure and reliable for managing sensitive data.	Limited use; cannot be used for general software engineering, app creation, or web development.
Extensive and high-quality learning resources.
Provides a solid foundation in data management, statistical theory, and analytical workflow.

7. KNIME

KNIME (Konstanz Information Miner) is another popular tool for data analytics and reporting. This tool allows users to build complex data pipelines without writing code via a visual, node-based workflow.

Best For:

• Integrating a wide range of data sources and analytical tools.
• Visualising and creating end-to-end data science workflows.
• Automating and deploying analytical models.
• Performing tasks like data mining, blending, and machine learning.

Real-World Use:

• Life Sciences: Assessing drug discovery data and genetic research. 
• Marketing: Customer segmentation, analysing market trends, and lead scoring.
• Finance: Assessing credit risk, detecting fraud, and analysing portfolio.
• Retail: Forecasting sales, managing inventory and analysing customer behaviour.

Pros	Cons
The drag-and-drop interface makes it accessible for non-programmers to start working with data.	Steep learning curve for advanced use.
Highly versatile and offers seamless integration with any data source.	Limited customisation.
Offers a vast library of nodes for text processing and social network analysis.	Performance issues when handling very large datasets or complex operations.
The visual workflow makes it simple to share and explain a data project to anyone.

8. Qlik

Qlik is another leading business intelligence tool and data visualisation platform that allows users to create interactive reports and dashboards. Its drag-and-drop functionality makes Qlik accessible for a wide range of users.

Best For:

• Combining data from multiple sources. 
• Extracting hidden insights and relationships in data with AE (Associative Engine).
• Creating interactive dashboards.
• Exploring data through interactive visualisation.

Real-World Use:

• Marketing: Visualising campaign performance, analysing customer behaviour, and tracking website traffic.
• Sales: Monitoring sales pipelines, analysing regional performance, and forecasting sales trends.
• Finance: Creating dashboards to track budget vs. actuals, monitor profitability, and visualise financial forecasts.
• Retail: Analysing store performance, managing inventory levels, and understanding sales by product category.
• Healthcare: Visualising patient data to identify trends, track hospital performance, and manage resources.

Pros	Cons
Intuitive drag-and-drop features make it easy for learners to create compelling dashboards.	Steep learning curve for advanced use.
Qlik’s Associative Engine allows users to explore data freely.	Performance issues when dealing with extremely large or highly complex datasets.
Allows for seamless data connection, cleaning, and transformation.
Offers a wide array of learning resources.

To Conclude

By now, you must have understood what data analytics is, what it does, who it helps, and the top tools used in data analytics. 

Mastering these tools can help you tap into a booming tech career and command a high salary in top IT companies. 

To become successful in this path, you need reliable training that offers a comprehensive curriculum and hands-on experience. Our practical-focused data analytics course can help you master all the in-demand tools and build a fulfilling career in this booming field. 

Let’s discuss how we can help you kickstart a rewarding career in data analytics today.

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FAQs

What is the most used tool in data analytics?

The most used data analytics tool depends on its usage. For instance, SQL is considered fundamental for professionals to retrieve data. Whereas Python, Power BI, and Tableau are industry standards. 

Should I learn a coding language or a visualisation tool?

Consider learning both as they are complementary. Visualisation tools like Power BI and Tableau help in creating dashboards quickly. On the other hand, Python allows you to perform statistical analysis, advanced data cleaning, and build machine learning models. 

Which AI tool is best for data analysis?

The best AI tool depends on the task. For example, Power BI and Tableau have integrated artificial intelligence for natural language queries and quick insights. Python, with its extensive libraries like SciPy, Scikit-learn, and TensorFlow, is the industry standard for building custom, advanced machine learning models and automation.

What’s the main difference between Power BI and Tableau?

While both are powerful BI tools, Power BI offers strong integration with Microsoft, and Tableau handles large, complex datasets, which is more preferred by analysts for high-quality data visualisation. 

Which data analytical tool is the best for a beginner?

Microsoft Excel is the best data analytical tool for a beginner. It is widely used and has a low learning curve for basic data manipulation and analysis. Also, it offers a strong foundation for understanding data concepts.

What are the three main types of data analytics?

The three main types of data analytics are descriptive, predictive, and prescriptive. Descriptive analytics describe what happened, predictive analytics forecast what might happen, and prescriptive analytics recommend what measures should be taken.

Is Python a better choice than Excel for data analysis?

Yes, Python is considered a better choice than Excel, especially when it comes to analysing complex and large data sets. Python allows for automation, statistical modelling, and machine learning, which are not possible in Excel. 

Can I get a job by only knowing one of these data analytics tools?

Yes, you can get a job knowing any of the tools used in data analytics. However, most jobs today require a combination of skills. For instance, today, a data analyst might require proficiency in SQL for data extraction, Tableau/Power BI for data visualisation, and Python/R for data analysis.